problem solving research adalah

Riset Pemasaran: Pengertian, Tujuan, Jenis dan Proses

Pengertian riset pemasaran – Rahasia kesuksesan menjalankan sebuah bisnis usaha adalah menghasilkan keuntungan. Sementara untuk memperoleh keuntungan, butuh cara dan proses. Salah satunya dengan melakukan riset pemasaran . Mengapa pemasaran menjadi hal penting?

Yaps, hal ini disebabkan terkait dengan bisnis harus memiliki pemasukan selain pengeluaran. Pengeluaran produksi dan marketing apalagi gaji sangatlah banyak, maka pemasaran menjadi penting untuk bisa menjalankan sebuah usaha. Namun, banyak usahawan gagal karena tidak pernah melakukan yang namanya riset terhadap pemasaran.

Dalam artikel dan pada kesempatan kali ini kita akan mengulas tuntas tentang apa itu riset pemasaran, tujuan dan jenis dari riset pasar.

Apa Itu Riset Pemasaran?

Apa itu riset pemasaran? Barangkali masih ada yang merasa asing dengan istilah riset pemasaran ? Pengertian riset pemasaran dapat dimaknai sebagai pengumpulan data, observasi dan pengolahan data terhadap objek penelitian di dunia pemasaran. 

Tentu saja riset pemasaran ini memiliki tujuan, yang akan kita ulas di sub bab di bawah. Riset pemasaran sebagai bentuk dari penelitian atau tinjauan dalam kurun waktu tertentu. Dengan kata lain, butuh proses demi memperoleh data yang objektif dan akurat. 

Setidaknya dari perolehan data yang objektif dan akurat akan membantu dalam pembuatan keputusan. Keputusan inilah yang mempengaruhi strategi, trik dan perolehan omset yang diinginkan. Agar lebih jelas lagi, kita intip pengertian riset pemasaran menurut para ahli berikut ini.

Pengertian Riset Pemasaran Menurut Ahli

Setiap orang maupun lembaga dalam mengartikan riset pemasaran memiliki pandangannya sendiri-sendiri. Seperti apa sih pendapat mereka? Berikut ulasannya. 

1. Maholtra 

Pengertian riset pasar menurut Malhotra adalah analisis, pembagian informasi dan upaya mengidentifikasi tentang informasi yang dilakukan secara objektif dan sistematis. Dimana riset pasar dilakukan untuk pengambilan keputusan yang ada kaitannya dengan permasalahan dan kesempatan di dunia bisnis. 

2. Robby Susatyo 

Sementara Robby Susatyo mengartikan bahwa pengertian riset pemasaran upaya mengidentifikasi secara sistematis dan objektif. Dua inilah yang dijadikan sebagai bentuk pengumpulan, analisis dan perangkaian informasi yang bertujuan untuk membantu mengambil keputusan atas masalah yang yang dihadapi. 

3. American Marketing Association (AMA) 

Sementara AMA memiliki pengertian riset pemasaran yang bertujuan untuk menghubungkan masyarakat umum, konsumen dan pelanggan lewat sarana informasi. Dari data-data tersebut yang dapat digunakan untuk melihat peluang, melihat permasalahan dalam pemasaran dan membantu untuk mengevaluasi tindakan pemasaran.

Itulah beberapa riset pemasaran menurut para ahli dibidangnya. Sepertinya mengetahui pendapat mereka belum cukup. Kamu juga wajib tahu tentang tujuan, jenis dan prosesnya. Yuks, langsung simak ulasannya di bawah.

Tujuan Riset Pasar

Riset pemasaran memang lebih sering digunakan oleh para pebisnis menengah ataupun bisnis besar. Tidak lain bertujuan untuk mencapai beberapa hal berikut ini. 

1. Membuat keputusan yang tepat

Salah satu alasan kenapa riset pasar dilakukan, tidak lain bertujuan untuk membantu dalam membuat keputusan. Sudah rahasia umum jika menjalankan sebuah usaha bisnis berhadapan dengan permasalahan di lapangan. 

Banyaknya kendala inilah yang menuntut pelaku usaha untuk terus update, membuat strategi dan membuat inovasi agar tidak lekang oleh waktu. Tentu saja agar konsumen tetap setia terhadap usaha bisnis yang kamu tawarkan. 

2. Mendapatkan peluang bisnis baru

Tujuan melakukan riset pasar adalah membukakan peluang bisnis baru. Buat pelaku usaha yang memperoleh omset besar, dan ingin mengembangkan usaha lain, butuh kemampuan untuk membaca peluang. Salah satunya peluang untuk memulai usaha bisnis yang dicari oleh konsumen. 

Atau mungkin kamu punya modal, dan ingin menekuni dunia bisnis untuk pertama kalinya? Nah, kamu cukup melakukan riset pasar. Kamu cukup mencari apa yang paling konsumen minati, maka itulah usaha yang bisa kamu tekuni. Peluang tidak akan kita temukan, jika kita tidak jeli membaca situasi. 

3. Menghindari kegagalan usaha

Setiap pelaku usaha bisnis pastinya menginginkan usaha mereka berjalan lancar tanpa terkendala. Maka sudah tidak heran jika tujuan riset pemasaran tidak lain agar usaha bisnis yang dijalankan berjalan lancar tanpa ada kegagalan yang begitu berarti. Ketika usaha gagal, maka gagal pula secara finansial. 

4. Memanfaatkan peluang investor

Kita tahu bahwasanya dalam menjalankan sebuah bisnis perlu kerjasama untuk menggalang kekuatan. Salah satunya memperoleh investor yang mau menyuntikan dana untuk modal dan mengembangkan usaha.

Tentu saja agar para investor tertarik dengan usaha bisnis yang kamu jalankan harus memiliki prospek yang jelas, menguntungkan dan meyakinkan. Seorang investor tidak akan bersedia mengorbankan dana mereka untuk usaha yang tidak memiliki harapan dan peluang bagus.  

5. Evaluasi

Rata-rata pelaku usaha melakukan riset pasar berperan untuk mengetahui apa yang perlu dievaluasi. Dimana evaluasi ini memudahkan perusahaan melakukan r eview terhadap brand positioning demi mengetahui posisi dan selera produk dari pesaing. 

6. Understanding

Tujuan penting melakukan riset pasar adalah understanding yang berperan untuk memberikan pemahaman bahwa konsumen sebagai insight masukan yang paling penting. Tanpa mengenali konsumen, sebuah perusahaan tidak bisa mengetahui apa yang menjadi kebutuhan yang mereka inginkan.

Dengan kata lain, Understanding sebagai upaya perusahaan atau pelaku usaha untuk memahami perilaku konsumen agar keinginan mereka terpenuhi. 

7. Predicting

Sebuah perusahaan atau pelaku usaha bisnis tidak dapat menjalankan bisnis usaha mereka tanpa peta bisnis. Salah satunya adalah kemudahan dalam membuat prediksi. Jadi riset pasar berperan sebagai basis data untuk melakukan prediksi agar usaha yang dijalankan tidak gagal dan riset pasar masih dapat digunakan untuk sumber penilaian.

Jenis Riset Pemasaran

Buat kamu yang ingin menekuni dunia usaha bisnis, penting juga mengetahui jenis-jenis riset pasar. Secara umum, dibagi menjadi dua jenis yang akan kita ulas sebagai berikut. 

1. Problem Solving

Problem solving research adalah riset pemasaran yang dimanfaatkan untuk mengetahui solusi dari atas permasalahan yang terjadi di dunia pemasaran. Umumnya riset jenis problem solving research mencoba untuk melakukan riset kejadian atau kasus yang terjadi pada masa lalu.

Tujuan dari jenis riset pemasaran satu ini untuk meminimalisir terjadinya pengulangan kesalahan yang sama di masa yang akan datang. 

2. Problem Identification Research

Sementara yang dimaksud dengan controlling research adalah riset pemasaran yang digunakan untuk mengawasi proses bisnis ataupun pemasaran. Selain digunakan untuk mengisi, dapat digunakan sebagai pengendalian usaha yang kamu jalankan.

Sesuai dengan namanya, controlling research lebih sering digunakan untuk menjaga proses dan kinerja bisnis yang dilakukan secara reguler. Adapun tujuan dari riset pemasaran kontrol ini, yaitu membantu dalam mengatasi zero defec t yang dilakukan secara berkala. 

3. Planning Research

Planning research merupakan riset pemasaran yang dilakukan seseorang untuk mendapatkan informasi untuk merencanakan kegiatan pemasaran.

Kita tahu bahwasanya segala hal yang berkaitan dengan menjalankan sebuah usaha bisnis membutuhkan informasi dan data untuk membantu kita membuat perencanaan dan strategi pemasaran. Tentunya pengumpulan informasi ini dilakukan dengan cara riset. .

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Proses Riset Pasar

Ingin melakukan riset pemasaran untuk usaha kecil-kecilan kamu? Namun bingung proses riset pemasarannya gimana? Yuks, simak ulasannya berikut ini. 

1. Definisi Masalah

Buat kamu yang penasaran gimana sih caranya mengawali riset pasar? langkah pertama adalah membuat definisi masalah. Nah di tahap ini kamu harus tahu apa tujuan dilakukan riset.

Penting juga mengetahui latar belakang melakukan riset. Di tahap ini kamu bisa membuat keputusan, wawancara, dan membuat analisis data sekunder. 

Hal yang penting dalam definisi masalah, wajib tahu dan bisa mendefinisikan masalah. karena mendefinisikan masalah adalah kunci untuk menemukan problem solving yang tepat. 

2. Melakukan Pendekatan Masalah

Di proses riset pemasaran dalam pengembangan dan pendekatan masalah dapat dilakukan dengan beberapa cara. Ada yang dilakukan dengan melakukan pendekatan teoritis, dapat pula dengan diskusi dengan pakar. Dapat pula dilakukan dengan cara riset kualitatif. 

3. Merancang Riset

Bentuk rancangan riset adalah menguji hasil penelitian riset yang sudah dilakukan. Rancangan proses riset pemasaran dapat dilakukan dengan cara melakukan analisis data sekunder, rencana analisis data, rancangan kuesioner dan masih banyak lagi. 

4. Pengumpulan data 

Proses riset pemasaran bagi perusahaan atau pelaku bisnis dapat dilakukan beberapa cara. Diantaranya, dapat dilakukan oleh tim yang dibuat perusahaan, atau bisa juga menggunakan orang ketiga yang khusus menyewa jasa riset. Tentu saja dari masing-masing keputusan memiliki kelebihan dan kekurangannya sendiri-sendiri. 

5. Menganalisis data

Jika riset pasar dijalankan secara mandiri, maka langkah selanjutnya data yang sudah diperoleh perlu dilakukan analisis data. Tentu dibutuhkan konsentrasi agar bisa menghasilkan hasil yang maksimal. 

6. Membuat presentasi laporan

Terakhir, Proses riset pemasaran adalah membuat laporan akhir. Dimana hasil dari riset pasar dapat dibuat laporan dan dilaporkan apa saja penemuan yang terjadi di lapangan. Tentu saja laporan dibuat berdasarkan pada data dan fakta yang ditemukan dilapangan. Karena itulah yang nantinya akan mempengaruhi keputusan yang akan diambil.

Itulah beberapa proses riset pemasaran. Sekilas memang terkesan format seperti halnya melakukan penelitian. Memang seperti itulah proses melakukan riset, dan semoga dari ulasan tersebut kamu terbantu.

Fungsi Riset Pemasaran

Setelah membahas tentang proses riset pasar, kali ini kita akan membahas tentang fungsi riset pemasaran, diantaranya sebagai berikut:

1. Fungsi Understanding

Fungsi riset pemasaran understanding adalah memahami produk atau jasa yang dibutuhkan konsumen. Tujuannya untuk membantu perusahaan membuat produk yang sedang dibutuhkan pasar. Sehingga potensi penjualan akan semakin tinggi.

2. Fungsi Evaluating

Evaluating berfungsi untuk melakukan evaluasi terhadap strategi pemasaran yang telah diterapkan. Selain itu, riset ini juga berfungsi untuk evaluasi produk. Dengan begitu, perusahaan dapat melakukan perbaikan atau penambahan fitur yang dibutuhkan.

3. Fungsi Predicting

Selanjutnya terdapat fungsi predicting, yaitu memprediksi apa yang akan terjadi di masa depan. Riset ini harus dilakukan, terutama bagi perusahaan yang bergerak di bidang teknologi agar bisa menyesuaikan kebutuhan konsumen.

Peran Riset Pemasaran Dalam Rencana dan Strategi Pemasaran 

Tidak banyak orang yang menyadari pentingnya peran riset pemasaran. Saat melakukan riset, ada beberapa peran penting yang akan kamu dapatkan. Diantaranya sebagai berikut. 

1. Menciptakan Ide 

Tahukah kamu jika riset pemasaran sangat membantu kamu untuk menciptakan ide. Cocok banget buat kamu yang mengalami kesulitan menemukan ide usaha bisnis . Jadi yang masih ragu ingin ingin menjalankan usaha apa, tidak ada salahnya melakukan riset pemasaran kecil-kecilan untuk memantik ide-ide. 

2. Variasi Pilihan Ide 

Barangkali masih meragukan apakah benar melakukan riset pemasaran akan menciptakan ide? Tentu saja, tidak hanya satu ide saja. Tetapi beberapa ide bisa dapat kita temukan. Jadi selama melakukan riset pemasaran ada banyak ide, hal yang perlu dilakukan hanya mencatat ide-ide tersebut. 

Dari hasil cek list variasi ide itulah yang bisa kamu jadikan alternatif pilihan. Kamu bisa pilih salah satu atau salah dua ide yang paling kamu sukai, yang sesuai karakter kamu, dan pilih ide yang memang kamu kenal dan menyukai ide tersebut. Jangan pilih ide yang kamu tidak ada daya tarik. 

3. Memudahkan Mengembangkan Konsep 

Kendala yang sering dirasakan saat hendak mengembangkan usaha bisnis, kesulitan dalam mengembangkan konsep. Pada dasarnya kesulitan dalam mengembangkan konsep diakibatkan kurangnya data dan informasi yang ada di luar sana. Setidaknya setelah dilakukan riset pemasaran, akan banyak data masuk yang memudahkan kamu membuat konsep bisnis plan.

4. Pengembangan dan Strategi Pemasaran 

Seperti yang sudah disinggung sebelumnya, riset pemasaran berperan untuk membantu kamu mengembangkan sebuah usaha. Kita bisa melihat pelaku usaha kecil-kecilan yang ada di sekeliling mereka. Saya yakin ada banyak kasus yang bisa kita jadikan pelajaran. 

Misal ada pelaku usaha kecil yang menjual barang/jasa. Dia hanya menjalankan usaha ala kadarnya. Tidak ada inovasi ataupun terobosan baru dan berbeda.

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5. Analisis Bisnis

Menjalankan sebuah bisnis tidak seperti kita bekerja di perusahaan orang lain. Dimana kita hanya cukup bekerja menjalankan system, mentaati aturan dan menuruti keinginan ownernya. Saat Anda menjadi seorang owner, memiliki peran dan tuntutan lebih tinggi.  

Maka dari itu, penting sekali seorang pelaku usaha perlu melakukan analisis bisnis. Sebuah bisnis, apalagi jika bisnis tersebut skala menengah ke atas, maka analisis bisnis perlu diperhatikan. Karena dari hasil analisis bisnis inilah yang akan mempengaruhi dan menentukan hasil akhir perusahaan.  

6. Mengembangkan Produk 

Peran riset pemasaran membantu dalam pengembangan produk usaha. Contoh kasus, sebut saja Ibu A. Dia menjual bunga hidup di dalam pot.

Karena stok bunga terlalu banyak, dan banyak pelanggan yang menanyakan tentang buket bunga, maka Ibu A akhirnya mengembangkan produk membuat buket bunga, dengan system pelanggan bisa memilih bunga yang ada di kebun. 

Dari contoh di atas menunjukan bahwasanya mengembangkan produk seringkali bukan karena keinginan ownernya. Melainkan karena permintaan konsumen. Jadi buat kamu yang merasa usahanya begitu-begitu saja dan tidak ada yang berkunjung, bisa jadi usaha perlu dikembangkan.

7. Tes Pasar (menghasilkan komersialisasi)

Disebut-sebut bahwa riset pemasaran juga dapat digunakan untuk mengetes pasar. Pelaku usaha wajib tahu selera, keinginan dan kebutuhan pasar itu bagaimana dan seperti apa. Tujuannya agar produk yang dikeluarkan menghasilkan komersialisasi secara maksimal. 

Hampir semua pelaku usaha menginginkan keuntungan. Sementara untuk menghasilkan sebuah keuntungan tidaklah. Salah satu caranya adalah melakukan tes pasar. Minimal apa yang dijual terserap oleh pelanggan. Jadi buat kamu yang ingin menekuni dunia bisnis.

Apapun itu bentuknya, penting sekali menguasai ilmu riset pemasaran. Setidaknya dengan melakukan riset pemasaran, kamu akan terbantu banyak hal, yang sudah disebutkan di atas. Semoga ulasan di atas bermanfaat tentang pengertian riset pemasaran sampai proses riset pemasaran itu sendiri.

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Problem Solving: Pengertian, Proses, dan Metodenya

Problem solving adalah proses penyelesaian suatu masalah.

Tiffany Revita - 24 February 2023

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Problem solving merupakan salah satu skill penting yang diperlukan dalam dunia kerja. Pasalnya, problem solving berkaitan erat dengan kemampuan seseorang untuk memecahkan masalah dan menemukan solusi terbaik sebagai bentuk penyelesaiannya.

Namun, problem solving tidak hanya berguna untuk diterapkan dalam hal pekerjaan saja, tetapi juga dapat digunakan untuk memecahkan suatu masalah dalam kehidupan sehari-hari. Lantas, bagaimana prosesnya dan seperti apa metode yang digunakannya?

Simak penjelasan selengkapnya dalam artikel ini!

Apa Itu Problem Solving ?

Pada dasarnya, problem solving adalah sebuah cara untuk menemukan solusi dari sebuah masalah. Menurut Oemar Hamalik, problem solving merupakan suatu proses mental dan intelektual dalam menemukan masalah.

Kemampuan ini berkaitan dengan berbagai hal, seperti kemampuan mendengar, menganalisa, meneliti, kreativitas, komunikasi, kerja tim, hingga pengambilan keputusan. Tujuannya, agar sebuah masalah dapat dipecahkan secara efektif berdasarkan data serta informasi yang akurat.

Proses Problem Solving

Dalam prosesnya, ada empat tahapan dasar problem solving , yakni:

1. Mengidentifikasi Masalah

Langkah pertama dalam proses problem solving adalah mendefinisikan sebuah masalah berdasarkan gejala yang ada. Pasalnya, sebuah masalah biasanya dipengaruhi oleh berbagai faktor.

Faktor-faktor tersebut harus diuraikan terlebih dahulu dengan cara identifikasi agar penyelesainnya dapat dilakukan dengan baik.

2. Menemukan Solusi Terbaik

Problem solving bertujuan untuk menemukan solusi terbaik atas sebuah masalah. Untuk mendapatkan hal tersebut, diperlukan pemahaman yang mendalam mengenai masalah tersebut agar dapat terselesaikan secara efektif.

3. Melakukan Evaluasi

Evaluasi merupakan tahap paling akhir dalam proses problem solving . Dalam tahap ini, solusi yang sudah diputuskan sebelumnya dapat diterapkan. Namun, hal tersebut tidak hanya sampai di situ saja, karena solusi tersebut juga harus ditindaklanjuti agar dapat menyelesaikan masalah secara menyeluruh.

Metode Problem Solving

1. brainstorming.

Brainstorming merupakan metode problem solving yang paling banyak digunakan oleh orang-orang. Pasalnya, metode ini efektif untuk digunakan sebagai pemecahan masalah melalui solusi kreatif.

Prosesnya adalah setiap orang harus menyampaikan ide-ide maupun pendapat yang kemudian dapat diolah menjadi satu solusi utama.

2. 6 Thinking Hats

Dalam metode ini, setiap orang akan mencoba memberikan penyelesaian terhadap suatu masalah dari beragam perspektif. Caranya adalah dengan mengelompokkan ide-ide yang ada ke dalam daftar pro-cons. Dengan begitu, kamu bisa melihat ide mana yang memiliki kelebihan yang paling banyak.

3. The 5 Whys

Metode ini dilakukan dengan cara meng-highlight masalah yang ingin dipecahkan. Kemudian, cari tahu jawaban mengenai “mengapa” masalah tersebut bisa terjadi sebanyak lima kali hingga kamu mendapatkan jawaban yang objektif tentang pertanyaanmu.

4. Lightning Decision Jam

Metode ini memungkinkanmu untuk menulis berbagai hal, mulai dari tantangan, kekhawatiran, hingga kesalahan dalam sebuah catatan kecil. Dengan hal tersebut, kamu bisa memilih masalah mana yang ingin diselesaikan terlebih dahulu dengan melihatnya dari sudut pandang baru. Dengan begitu, penyelesaian masalah dapat dilakukan secara tertatur.

5. Failure Mode and Effect Analysis

Terakhir, metode ini digunakan untuk menganalisis setiap elemen dari strategi bisnis serta kemungkinan-kemungkinan buruk yang akan terjadi. Dengan begitu, kamu bisa menemukan solusi dari masalahmu serta langkah preventif untuk mencegahnya secara lebih mudah.

Nah, itulah penjelasan mengenai problem solving . Dari penjelasan di atas, dapat diketahui bahwa problem solving merupakan kemampuan pemecahan masalah yang dilakukan dengan proses yang cukup panjang.

Tags: Problem Solving proses problem solving metode problem solving

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• Seputar Kerja

Apa Itu Problem Solving? Ini Pengertian, Tujuan, & 5 Metodenya

Maret 20, 2024

Di masa ini, problem solving adalah salah satu skill yang wajib dimiliki karyawan, terutama pemimpin dan manajer. Ada banyak manfaat problem solving , mulai dari mempermudah pengambilan keputusan hingga meningkatkan efisiensi. Tapi apa itu problem solving sebenarnya? Apa saja skill problem solving yang perlu Anda kuasai?

Dalam bahasan kali ini, kita akan membahas dengan lengkap tentang problem solving , tujuan, manfaat, dan berbagai metodenya. Yuk, scroll ke bawah untuk tahu kelanjutannya!

Apa itu Problem Solving ?

Memahami apa itu problem solving adalah hal fundamental yang harus dipahami siapapun, terutama yang baru masuk ke dunia kerja atau ingin naik jenjang karir. Tanpa pemahaman dan skill problem solving yang mumpuni, seseorang akan mengalami kesulitan saat bekerja, apalagi jika lingkungan kerjanya penuh tekanan.

Menurut buku The Executive Guide to Improvement and Change , pengertian problem solving adalah kemampuan mendefinisikan masalah, menentukan sumbernya, membuat skala prioritas, menyusun alternatif-alternatif solusi, dan mengimplementasikannya sesuai kebutuhan. Singkatnya, problem solving adalah kemampuan menemukan masalah dan memecahkannya dengan baik.

Agar proses pemecahan masalah terlaksana, ada beberapa karakteristik problem solving yang wajib dipenuhi, yaitu:

• Interaksi antara pihak-pihak terlibat, misalnya antar karyawan dalam satu divisi, lintas jabatan, atau antara atasan dan bawahan.
• Terdapat diskusi yang diselenggarakan dengan efektif, sistematis, dan menghasilkan progres, baik secara formal, semiformal, atau informal.
• Informasi lengkap dan valid, penyampai dapat mempertanggungjawabkan kebenarannya.
• Saling membimbing dan melatih dari pihak berpengalaman ke yang kurang berpengalaman.

Berdasarkan karakteristik di atas, kita dapat menemukan bahwa peran pemimpin sangat vital dalam proses pengambilan keputusan. Agar proses problem solving terselesaikan, pemimpin tidak boleh egois atau terlalu longgar pada rekan-rekan yang membantunya mengambil keputusan.

Tujuan Problem Solving

Setelah mengetahui apa itu problem solving , kali ini kita akan membahas beberapa tujuan problem solving dalam perusahaan, di antaranya adalah:

• Melatih kemampuan karyawan untuk menghadapi masalah
• Melatih karyawan dalam menemukan langkah-langkah terbaik untuk mencari solusi dari masalah yang ada
• Melatih karyawan bagaimana cara bertindak dan apa yang harus dilakukan dalam situasi baru
• Melatih karyawan untuk lebih berani dalam mengambil keputusan terbaik
• Melatih karyawan untuk meneliti suatu masalah dari berbagai sudut pandang dan kemungkinan yang ada

Sementara itu, melatih skill problem solving bagi diri sendiri juga sangat penting. Sebab pada faktanya, keahlian ini tidak hanya berguna di dunia kerja, tapi juga dalam aspek-aspek lain kehidupan.

Sebagai contoh, Anda adalah seorang karyawan berusia 24 tahun dengan tanggungan orang tua dan 3 adik. Selain itu, Anda juga punya keinginan punya rumah dan kendaraan di usia 30 tahun. Supaya tanggung jawab dan impian tercapai, Anda melakukan proses problem solving dan menemukan solusi bahwa Anda harus punya side hustle supaya bisa menabung sekaligus tetap membantu ekonomi keluarga.

BACA JUGA: Manfaat Menerapkan Teamwork Karyawan di Perusahaan Anda

  Tahapan Problem Solving

Setelah memahami apa itu problem solving dan tujuannya, di bawah ini terdapat beberapa tahapan untuk menerapkan metode problem solving . Jika Anda merasa belum punya skill problem solving mumpuni, cara-cara di bawah ini dapat membantu Anda berlatih.

1. Mendefinisikan Masalah

Tahapan pertama problem solving adalah dengan mendefinisikan, mengurai, dan menyusun kembali satu per satu masalah pokok yang sedang terjadi. Meskipun masalah-masalah tersebut tampak banyak, usahakan untuk menemukan inti dari semua masalah tersebut.

Jika Anda sedang bekerja di perusahaan, pastikan untuk mengajak rekan kerja dan orang lain yang berhubungan dengan masalah tersebut. Dengan demikian, Anda dapat mendengar masalah dari berbagai perspektif dan menemukan titik masalah.

2. Menentukan Sumber/Dalang Penyebab Masalah

Setelah masalah utama ditemukan, tahapan selanjutnya problem solving adalah menyelidiki sumber masalah tersebut. Apakah masalah timbul karena sistem? Orang-orang terlibat? Atau komunikasi yang kurang efektif? Dengan menemukan jawaban dari pertanyaan semacam itu, Anda dan tim dapat melakukan brainstorming sumber masalah, sebelum mencari solusinya.

3. Menentukan Prioritas Masalah

Dalam satu kali brainstorming , Anda dan rekan-rekan barangkali akan menemukan lebih dari satu masalah untuk dipecahkan. Namun demikian, memaksakan diri menyelesaikan semua masalah dalam satu waktu sangat tidak efisien. Bukannya tuntas, bisa-bisa Anda dan tim justru tidak akan memecahkan satu pun masalah.

4. Mengembangkan Solusi Alternatif

Claire Cook – penulis terkenal asal Amerika Serikat – pernah berkata, “Jika plan A tidak berhasil, ingatlah masih ada 25 huruf untuk dijadikan rencana ( plan B, C, D, dan seterusnya”. Alternatif-alternatif rencana seperti ini juga perlu Anda siapkan jika sewaktu-waktu solusi utama tidak bekerja.

5. Mengimplementasikan Solusi dan Mengevaluasinya

Tahapan terakhir pada proses problem solving adalah mengimplementasikan solusi sesuai kesepakatan bersama. Setelah sudah menemukan solusi terbaik, maka Anda tinggal menyusun strategi penerapan, membagikannya kepada tim anggota, dan menindaklanjuti solusi yang sudah diputuskan.

Tidak berhenti sampai disitu, ada baiknya jika Anda bisa mengumpulkan masukan dari anggota tim atau pihak-pihak yang terlibat dan melakukan evaluasi dari penerapan solusi tersebut.

Pada setiap tahapan untuk menyelesaikan masalah, dibutuhkan beberapa skill problem solving yang mumpuni. Seperti kemampuan menganalisis, kemampuan berdiskusi, hingga penentuan prioritas.

BACA JUGA: Jenis Kepemimpinan Dalam Perusahaan. Anda Termasuk yang Mana?

Metode Problem Solving

Dalam proses problem solving , ada beberapa metode yang dapat Anda gunakan, di antaranya adalah:

1. Linear Thinking

Metode problem solving pertama yang dapat Anda terapkan adalah linear thinking . Penggunaan metode ini sangat sederhana, yaitu dengan menekankan pada pertanyaan “mengapa” agar bisa menemukan akar permasalahan. Setelah akarnya ditemukan, Anda bisa menggunakan data-data lama dan solusi yang ada untuk diterapkan.

Linear thinking adalah salah satu metode problem solving paling tradisional dan mudah dilaksanakan. Kelemahannya, linear thinking hanya cocok untuk menghadapi masalah yang pernah dihadapi sebelumnya, tapi tidak sesuai jika masalahnya sama sekali baru.

2. Design Thinking

Berbeda dengan linear thinking , dalam apa itu problem solving penggunaan design thinking lebih menekankan pendekatan dari sisi user . Untuk memulainya Anda bisa mencoba untuk berempati kepada user yang sedang menghadapi masalah.

Kemudian setelah Anda mengetahui apa masalah yang dihadapinya, Anda bisa menggunakan skill problem solving yang dimiliki untuk membuat beberapa gambaran atau prototype yang dapat diuji untuk menemukan solusi dari masalah tersebut.

3. Creative Problem Solving

Ketika kita membahas apa itu problem solving , maka Anda perlu menciptakan keseimbangan antara logika dan kreativitas. Anda bisa menggunakan kreativitas untuk mencari tahu apa penyebab masalah yang terjadi dan kemudian mengembangkan solusi yang inovatif.

Metode creative problem solving tidak hanya seputar brainstorming atau ide-ide gila yang out of the box . Tetapi Anda juga perlu fokus untuk mendapatkan ide sebanyak-banyaknya dari proses tersebut.

4. Solution-based Thinking

Metode problem solving keempat yang dapat Anda terapkan adalah solution-based thinking , yaitu metode pemecahan masalah dengan berfokus pada solusi-solusi yang dapat dipastikan keberhasilannya.

Jika dibandingkan, solution-based thinking tampak seperti pertengahan antara linear thinking dan creative problem solving . Dari segi kecepatan, metode solution-based sama terfokusnya seperti linear thinking . Akan tetapi, dari segi fleksibilitas ide, solution-based thinking menggunakan pendekatan brainstorming seperti creative problem solving .

Demikianlah penjelasan mengenai apa itu problem solving , tujuan, dan metode-metodenya. Skill problem solving adalah salah satu keahlian paling dicari di dunia kerja. Bagi perusahaan, karyawan dengan kemampuan memecahkan masalah adalah aset berharga, baik untuk masa sekarang atau masa depan.

Apakah perusahaan Anda sedang mencari karyawan berkualitas tersebut? Kesulitan menemukan platform penyedia SDM dengan skill problem solving tingkat tinggi? Pasang iklan lowongan kerja Anda di KitaLulus dan jemput anggota tim impian Anda sekarang juga!

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Problem Solving

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Cognition ; Problem typology ; Problem-based learning ; Problems ; Reasoning

Problem solving is the process of constructing and applying mental representations of problems to finding solutions to those problems that are encountered in nearly every context.

Theoretical Background

Problem solving is the process of articulating solutions to problems. Problems have two critical attributes. First, a problem is an unknown in some context. That is, there is a situation in which there is something that is unknown (the difference between a goal state and a current state). Those situations vary from algorithmic math problems to vexing and complex social problems, such as violence in society (see Problem Typology ). Second, finding or solving for the unknown must have some social, cultural, or intellectual value. That is, someone believes that it is worth finding the unknown. If no one perceives an unknown or a need to determine an unknown, there is no perceived problem. Finding...

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Jonassen, D.H., Hung, W. (2012). Problem Solving. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_208

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Complex Problem Solving: What It Is and What It Is Not

Dietrich dörner.

1 Department of Psychology, University of Bamberg, Bamberg, Germany

Joachim Funke

2 Department of Psychology, Heidelberg University, Heidelberg, Germany

Computer-simulated scenarios have been part of psychological research on problem solving for more than 40 years. The shift in emphasis from simple toy problems to complex, more real-life oriented problems has been accompanied by discussions about the best ways to assess the process of solving complex problems. Psychometric issues such as reliable assessments and addressing correlations with other instruments have been in the foreground of these discussions and have left the content validity of complex problem solving in the background. In this paper, we return the focus to content issues and address the important features that define complex problems.

Succeeding in the 21st century requires many competencies, including creativity, life-long learning, and collaboration skills (e.g., National Research Council, 2011 ; Griffin and Care, 2015 ), to name only a few. One competence that seems to be of central importance is the ability to solve complex problems ( Mainzer, 2009 ). Mainzer quotes the Nobel prize winner Simon (1957) who wrote as early as 1957:

The capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problem whose solution is required for objectively rational behavior in the real world or even for a reasonable approximation to such objective rationality. (p. 198)

The shift from well-defined to ill-defined problems came about as a result of a disillusion with the “general problem solver” ( Newell et al., 1959 ): The general problem solver was a computer software intended to solve all kind of problems that can be expressed through well-formed formulas. However, it soon became clear that this procedure was in fact a “special problem solver” that could only solve well-defined problems in a closed space. But real-world problems feature open boundaries and have no well-determined solution. In fact, the world is full of wicked problems and clumsy solutions ( Verweij and Thompson, 2006 ). As a result, solving well-defined problems and solving ill-defined problems requires different cognitive processes ( Schraw et al., 1995 ; but see Funke, 2010 ).

Well-defined problems have a clear set of means for reaching a precisely described goal state. For example: in a match-stick arithmetic problem, a person receives a false arithmetic expression constructed out of matchsticks (e.g., IV = III + III). According to the instructions, moving one of the matchsticks will make the equations true. Here, both the problem (find the appropriate stick to move) and the goal state (true arithmetic expression; solution is: VI = III + III) are defined clearly.

Ill-defined problems have no clear problem definition, their goal state is not defined clearly, and the means of moving towards the (diffusely described) goal state are not clear. For example: The goal state for solving the political conflict in the near-east conflict between Israel and Palestine is not clearly defined (living in peaceful harmony with each other?) and even if the conflict parties would agree on a two-state solution, this goal again leaves many issues unresolved. This type of problem is called a “complex problem” and is of central importance to this paper. All psychological processes that occur within individual persons and deal with the handling of such ill-defined complex problems will be subsumed under the umbrella term “complex problem solving” (CPS).

Systematic research on CPS started in the 1970s with observations of the behavior of participants who were confronted with computer simulated microworlds. For example, in one of those microworlds participants assumed the role of executives who were tasked to manage a company over a certain period of time (see Brehmer and Dörner, 1993 , for a discussion of this methodology). Today, CPS is an established concept and has even influenced large-scale assessments such as PISA (“Programme for International Student Assessment”), organized by the Organization for Economic Cooperation and Development ( OECD, 2014 ). According to the World Economic Forum, CPS is one of the most important competencies required in the future ( World Economic Forum, 2015 ). Numerous articles on the subject have been published in recent years, documenting the increasing research activity relating to this field. In the following collection of papers we list only those published in 2010 and later: theoretical papers ( Blech and Funke, 2010 ; Funke, 2010 ; Knauff and Wolf, 2010 ; Leutner et al., 2012 ; Selten et al., 2012 ; Wüstenberg et al., 2012 ; Greiff et al., 2013b ; Fischer and Neubert, 2015 ; Schoppek and Fischer, 2015 ), papers about measurement issues ( Danner et al., 2011a ; Greiff et al., 2012 , 2015a ; Alison et al., 2013 ; Gobert et al., 2015 ; Greiff and Fischer, 2013 ; Herde et al., 2016 ; Stadler et al., 2016 ), papers about applications ( Fischer and Neubert, 2015 ; Ederer et al., 2016 ; Tremblay et al., 2017 ), papers about differential effects ( Barth and Funke, 2010 ; Danner et al., 2011b ; Beckmann and Goode, 2014 ; Greiff and Neubert, 2014 ; Scherer et al., 2015 ; Meißner et al., 2016 ; Wüstenberg et al., 2016 ), one paper about developmental effects ( Frischkorn et al., 2014 ), one paper with a neuroscience background ( Osman, 2012 ) 1 , papers about cultural differences ( Güss and Dörner, 2011 ; Sonnleitner et al., 2014 ; Güss et al., 2015 ), papers about validity issues ( Goode and Beckmann, 2010 ; Greiff et al., 2013c ; Schweizer et al., 2013 ; Mainert et al., 2015 ; Funke et al., 2017 ; Greiff et al., 2017 , 2015b ; Kretzschmar et al., 2016 ; Kretzschmar, 2017 ), review papers and meta-analyses ( Osman, 2010 ; Stadler et al., 2015 ), and finally books ( Qudrat-Ullah, 2015 ; Csapó and Funke, 2017b ) and book chapters ( Funke, 2012 ; Hotaling et al., 2015 ; Funke and Greiff, 2017 ; Greiff and Funke, 2017 ; Csapó and Funke, 2017a ; Fischer et al., 2017 ; Molnàr et al., 2017 ; Tobinski and Fritz, 2017 ; Viehrig et al., 2017 ). In addition, a new “Journal of Dynamic Decision Making” (JDDM) has been launched ( Fischer et al., 2015 , 2016 ) to give the field an open-access outlet for research and discussion.

This paper aims to clarify aspects of validity: what should be meant by the term CPS and what not? This clarification seems necessary because misunderstandings in recent publications provide – from our point of view – a potentially misleading picture of the construct. We start this article with a historical review before attempting to systematize different positions. We conclude with a working definition.

Historical Review

The concept behind CPS goes back to the German phrase “komplexes Problemlösen” (CPS; the term “komplexes Problemlösen” was used as a book title by Funke, 1986 ). The concept was introduced in Germany by Dörner and colleagues in the mid-1970s (see Dörner et al., 1975 ; Dörner, 1975 ) for the first time. The German phrase was later translated to CPS in the titles of two edited volumes by Sternberg and Frensch (1991) and Frensch and Funke (1995a) that collected papers from different research traditions. Even though it looks as though the term was coined in the 1970s, Edwards (1962) used the term “dynamic decision making” to describe decisions that come in a sequence. He compared static with dynamic decision making, writing:

• simple  In dynamic situations, a new complication not found in the static situations arises. The environment in which the decision is set may be changing, either as a function of the sequence of decisions, or independently of them, or both. It is this possibility of an environment which changes while you collect information about it which makes the task of dynamic decision theory so difficult and so much fun. (p. 60)

The ability to solve complex problems is typically measured via dynamic systems that contain several interrelated variables that participants need to alter. Early work (see, e.g., Dörner, 1980 ) used a simulation scenario called “Lohhausen” that contained more than 2000 variables that represented the activities of a small town: Participants had to take over the role of a mayor for a simulated period of 10 years. The simulation condensed these ten years to ten hours in real time. Later, researchers used smaller dynamic systems as scenarios either based on linear equations (see, e.g., Funke, 1993 ) or on finite state automata (see, e.g., Buchner and Funke, 1993 ). In these contexts, CPS consisted of the identification and control of dynamic task environments that were previously unknown to the participants. Different task environments came along with different degrees of fidelity ( Gray, 2002 ).

According to Funke (2012) , the typical attributes of complex systems are (a) complexity of the problem situation which is usually represented by the sheer number of involved variables; (b) connectivity and mutual dependencies between involved variables; (c) dynamics of the situation, which reflects the role of time and developments within a system; (d) intransparency (in part or full) about the involved variables and their current values; and (e) polytely (greek term for “many goals”), representing goal conflicts on different levels of analysis. This mixture of features is similar to what is called VUCA (volatility, uncertainty, complexity, ambiguity) in modern approaches to management (e.g., Mack et al., 2016 ).

In his evaluation of the CPS movement, Sternberg (1995) compared (young) European approaches to CPS with (older) American research on expertise. His analysis of the differences between the European and American traditions shows advantages but also potential drawbacks for each side. He states (p. 301): “I believe that although there are problems with the European approach, it deals with some fundamental questions that American research scarcely addresses.” So, even though the echo of the European approach did not enjoy strong resonance in the US at that time, it was valued by scholars like Sternberg and others. Before attending to validity issues, we will first present a short review of different streams.

Different Approaches to CPS

In the short history of CPS research, different approaches can be identified ( Buchner, 1995 ; Fischer et al., 2017 ). To systematize, we differentiate between the following five lines of research:

• simple (a) The search for individual differences comprises studies identifying interindividual differences that affect the ability to solve complex problems. This line of research is reflected, for example, in the early work by Dörner et al. (1983) and their “Lohhausen” study. Here, naïve student participants took over the role of the mayor of a small simulated town named Lohhausen for a simulation period of ten years. According to the results of the authors, it is not intelligence (as measured by conventional IQ tests) that predicts performance, but it is the ability to stay calm in the face of a challenging situation and the ability to switch easily between an analytic mode of processing and a more holistic one.
• simple (b) The search for cognitive processes deals with the processes behind understanding complex dynamic systems. Representative of this line of research is, for example, Berry and Broadbent’s (1984) work on implicit and explicit learning processes when people interact with a dynamic system called “Sugar Production”. They found that those who perform best in controlling a dynamic system can do so implicitly, without explicit knowledge of details regarding the systems’ relations.
• simple (c) The search for system factors seeks to identify the aspects of dynamic systems that determine the difficulty of complex problems and make some problems harder than others. Representative of this line of research is, for example, work by Funke (1985) , who systematically varied the number of causal effects within a dynamic system or the presence/absence of eigendynamics. He found, for example, that solution quality decreases as the number of systems relations increases.
• simple (d) The psychometric approach develops measurement instruments that can be used as an alternative to classical IQ tests, as something that goes “beyond IQ”. The MicroDYN approach ( Wüstenberg et al., 2012 ) is representative for this line of research that presents an alternative to reasoning tests (like Raven matrices). These authors demonstrated that a small improvement in predicting school grade point average beyond reasoning is possible with MicroDYN tests.
• simple (e) The experimental approach explores CPS under different experimental conditions. This approach uses CPS assessment instruments to test hypotheses derived from psychological theories and is sometimes used in research about cognitive processes (see above). Exemplary for this line of research is the work by Rohe et al. (2016) , who test the usefulness of “motto goals” in the context of complex problems compared to more traditional learning and performance goals. Motto goals differ from pure performance goals by activating positive affect and should lead to better goal attainment especially in complex situations (the mentioned study found no effect).

To be clear: these five approaches are not mutually exclusive and do overlap. But the differentiation helps to identify different research communities and different traditions. These communities had different opinions about scaling complexity.

The Race for Complexity: Use of More and More Complex Systems

In the early years of CPS research, microworlds started with systems containing about 20 variables (“Tailorshop”), soon reached 60 variables (“Moro”), and culminated in systems with about 2000 variables (“Lohhausen”). This race for complexity ended with the introduction of the concept of “minimal complex systems” (MCS; Greiff and Funke, 2009 ; Funke and Greiff, 2017 ), which ushered in a search for the lower bound of complexity instead of the higher bound, which could not be defined as easily. The idea behind this concept was that whereas the upper limits of complexity are unbound, the lower limits might be identifiable. Imagine starting with a simple system containing two variables with a simple linear connection between them; then, step by step, increase the number of variables and/or the type of connections. One soon reaches a point where the system can no longer be considered simple and has become a “complex system”. This point represents a minimal complex system. Despite some research having been conducted in this direction, the point of transition from simple to complex has not been identified clearly as of yet.

Some years later, the original “minimal complex systems” approach ( Greiff and Funke, 2009 ) shifted to the “multiple complex systems” approach ( Greiff et al., 2013a ). This shift is more than a slight change in wording: it is important because it taps into the issue of validity directly. Minimal complex systems have been introduced in the context of challenges from large-scale assessments like PISA 2012 that measure new aspects of problem solving, namely interactive problems besides static problem solving ( Greiff and Funke, 2017 ). PISA 2012 required test developers to remain within testing time constraints (given by the school class schedule). Also, test developers needed a large item pool for the construction of a broad class of problem solving items. It was clear from the beginning that MCS deal with simple dynamic situations that require controlled interaction: the exploration and control of simple ticket machines, simple mobile phones, or simple MP3 players (all of these example domains were developed within PISA 2012) – rather than really complex situations like managerial or political decision making.

As a consequence of this subtle but important shift in interpreting the letters MCS, the definition of CPS became a subject of debate recently ( Funke, 2014a ; Greiff and Martin, 2014 ; Funke et al., 2017 ). In the words of Funke (2014b , p. 495):

• simple  It is funny that problems that nowadays come under the term ‘CPS’, are less complex (in terms of the previously described attributes of complex situations) than at the beginning of this new research tradition. The emphasis on psychometric qualities has led to a loss of variety. Systems thinking requires more than analyzing models with two or three linear equations – nonlinearity, cyclicity, rebound effects, etc. are inherent features of complex problems and should show up at least in some of the problems used for research and assessment purposes. Minimal complex systems run the danger of becoming minimal valid systems.

Searching for minimal complex systems is not the same as gaining insight into the way how humans deal with complexity and uncertainty. For psychometric purposes, it is appropriate to reduce complexity to a minimum; for understanding problem solving under conditions of overload, intransparency, and dynamics, it is necessary to realize those attributes with reasonable strength. This aspect is illustrated in the next section.

Importance of the Validity Issue

The most important reason for discussing the question of what complex problem solving is and what it is not stems from its phenomenology: if we lose sight of our phenomena, we are no longer doing good psychology. The relevant phenomena in the context of complex problems encompass many important aspects. In this section, we discuss four phenomena that are specific to complex problems. We consider these phenomena as critical for theory development and for the construction of assessment instruments (i.e., microworlds). These phenomena require theories for explaining them and they require assessment instruments eliciting them in a reliable way.

The first phenomenon is the emergency reaction of the intellectual system ( Dörner, 1980 ): When dealing with complex systems, actors tend to (a) reduce their intellectual level by decreasing self-reflections, by decreasing their intentions, by stereotyping, and by reducing their realization of intentions, (b) they show a tendency for fast action with increased readiness for risk, with increased violations of rules, and with increased tendency to escape the situation, and (c) they degenerate their hypotheses formation by construction of more global hypotheses and reduced tests of hypotheses, by increasing entrenchment, and by decontextualizing their goals. This phenomenon illustrates the strong connection between cognition, emotion, and motivation that has been emphasized by Dörner (see, e.g., Dörner and Güss, 2013 ) from the beginning of his research tradition; the emergency reaction reveals a shift in the mode of information processing under the pressure of complexity.

The second phenomenon comprises cross-cultural differences with respect to strategy use ( Strohschneider and Güss, 1999 ; Güss and Wiley, 2007 ; Güss et al., 2015 ). Results from complex task environments illustrate the strong influence of context and background knowledge to an extent that cannot be found for knowledge-poor problems. For example, in a comparison between Brazilian and German participants, it turned out that Brazilians accept the given problem descriptions and are more optimistic about the results of their efforts, whereas Germans tend to inquire more about the background of the problems and take a more active approach but are less optimistic (according to Strohschneider and Güss, 1998 , p. 695).

The third phenomenon relates to failures that occur during the planning and acting stages ( Jansson, 1994 ; Ramnarayan et al., 1997 ), illustrating that rational procedures seem to be unlikely to be used in complex situations. The potential for failures ( Dörner, 1996 ) rises with the complexity of the problem. Jansson (1994) presents seven major areas for failures with complex situations: acting directly on current feedback; insufficient systematization; insufficient control of hypotheses and strategies; lack of self-reflection; selective information gathering; selective decision making; and thematic vagabonding.

The fourth phenomenon describes (a lack of) training and transfer effects ( Kretzschmar and Süß, 2015 ), which again illustrates the context dependency of strategies and knowledge (i.e., there is no strategy that is so universal that it can be used in many different problem situations). In their own experiment, the authors could show training effects only for knowledge acquisition, not for knowledge application. Only with specific feedback, performance in complex environments can be increased ( Engelhart et al., 2017 ).

These four phenomena illustrate why the type of complexity (or degree of simplicity) used in research really matters. Furthermore, they demonstrate effects that are specific for complex problems, but not for toy problems. These phenomena direct the attention to the important question: does the stimulus material used (i.e., the computer-simulated microworld) tap and elicit the manifold of phenomena described above?

Dealing with partly unknown complex systems requires courage, wisdom, knowledge, grit, and creativity. In creativity research, “little c” and “BIG C” are used to differentiate between everyday creativity and eminent creativity ( Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ). Everyday creativity is important for solving everyday problems (e.g., finding a clever fix for a broken spoke on my bicycle), eminent creativity changes the world (e.g., inventing solar cells for energy production). Maybe problem solving research should use a similar differentiation between “little p” and “BIG P” to mark toy problems on the one side and big societal challenges on the other. The question then remains: what can we learn about BIG P by studying little p? What phenomena are present in both types, and what phenomena are unique to each of the two extremes?

Discussing research on CPS requires reflecting on the field’s research methods. Even if the experimental approach has been successful for testing hypotheses (for an overview of older work, see Funke, 1995 ), other methods might provide additional and novel insights. Complex phenomena require complex approaches to understand them. The complex nature of complex systems imposes limitations on psychological experiments: The more complex the environments, the more difficult is it to keep conditions under experimental control. And if experiments have to be run in labs one should bring enough complexity into the lab to establish the phenomena mentioned, at least in part.

There are interesting options to be explored (again): think-aloud protocols , which have been discredited for many years ( Nisbett and Wilson, 1977 ) and yet are a valuable source for theory testing ( Ericsson and Simon, 1983 ); introspection ( Jäkel and Schreiber, 2013 ), which seems to be banned from psychological methods but nevertheless offers insights into thought processes; the use of life-streaming ( Wendt, 2017 ), a medium in which streamers generate a video stream of think-aloud data in computer-gaming; political decision-making ( Dhami et al., 2015 ) that demonstrates error-proneness in groups; historical case studies ( Dörner and Güss, 2011 ) that give insights into the thinking styles of political leaders; the use of the critical incident technique ( Reuschenbach, 2008 ) to construct complex scenarios; and simulations with different degrees of fidelity ( Gray, 2002 ).

The methods tool box is full of instruments that have to be explored more carefully before any individual instrument receives a ban or research narrows its focus to only one paradigm for data collection. Brehmer and Dörner (1993) discussed the tensions between “research in the laboratory and research in the field”, optimistically concluding “that the new methodology of computer-simulated microworlds will provide us with the means to bridge the gap between the laboratory and the field” (p. 183). The idea behind this optimism was that computer-simulated scenarios would bring more complexity from the outside world into the controlled lab environment. But this is not true for all simulated scenarios. In his paper on simulated environments, Gray (2002) differentiated computer-simulated environments with respect to three dimensions: (1) tractability (“the more training subjects require before they can use a simulated task environment, the less tractable it is”, p. 211), correspondence (“High correspondence simulated task environments simulate many aspects of one task environment. Low correspondence simulated task environments simulate one aspect of many task environments”, p. 214), and engagement (“A simulated task environment is engaging to the degree to which it involves and occupies the participants; that is, the degree to which they agree to take it seriously”, p. 217). But the mere fact that a task is called a “computer-simulated task environment” does not mean anything specific in terms of these three dimensions. This is one of several reasons why we should differentiate between those studies that do not address the core features of CPS and those that do.

What is not CPS?

Even though a growing number of references claiming to deal with complex problems exist (e.g., Greiff and Wüstenberg, 2015 ; Greiff et al., 2016 ), it would be better to label the requirements within these tasks “dynamic problem solving,” as it has been done adequately in earlier work ( Greiff et al., 2012 ). The dynamics behind on-off-switches ( Thimbleby, 2007 ) are remarkable but not really complex. Small nonlinear systems that exhibit stunningly complex and unstable behavior do exist – but they are not used in psychometric assessments of so-called CPS. There are other small systems (like MicroDYN scenarios: Greiff and Wüstenberg, 2014 ) that exhibit simple forms of system behavior that are completely predictable and stable. This type of simple systems is used frequently. It is even offered commercially as a complex problem-solving test called COMPRO ( Greiff and Wüstenberg, 2015 ) for business applications. But a closer look reveals that the label is not used correctly; within COMPRO, the used linear equations are far from being complex and the system can be handled properly by using only one strategy (see for more details Funke et al., 2017 ).

Why do simple linear systems not fall within CPS? At the surface, nonlinear and linear systems might appear similar because both only include 3–5 variables. But the difference is in terms of systems behavior as well as strategies and learning. If the behavior is simple (as in linear systems where more input is related to more output and vice versa), the system can be easily understood (participants in the MicroDYN world have 3 minutes to explore a complex system). If the behavior is complex (as in systems that contain strange attractors or negative feedback loops), things become more complicated and much more observation is needed to identify the hidden structure of the unknown system ( Berry and Broadbent, 1984 ; Hundertmark et al., 2015 ).

Another issue is learning. If tasks can be solved using a single (and not so complicated) strategy, steep learning curves are to be expected. The shift from problem solving to learned routine behavior occurs rapidly, as was demonstrated by Luchins (1942) . In his water jar experiments, participants quickly acquired a specific strategy (a mental set) for solving certain measurement problems that they later continued applying to problems that would have allowed for easier approaches. In the case of complex systems, learning can occur only on very general, abstract levels because it is difficult for human observers to make specific predictions. Routines dealing with complex systems are quite different from routines relating to linear systems.

What should not be studied under the label of CPS are pure learning effects, multiple-cue probability learning, or tasks that can be solved using a single strategy. This last issue is a problem for MicroDYN tasks that rely strongly on the VOTAT strategy (“vary one thing at a time”; see Tschirgi, 1980 ). In real-life, it is hard to imagine a business manager trying to solve her or his problems by means of VOTAT.

What is CPS?

In the early days of CPS research, planet Earth’s dynamics and complexities gained attention through such books as “The limits to growth” ( Meadows et al., 1972 ) and “Beyond the limits” ( Meadows et al., 1992 ). In the current decade, for example, the World Economic Forum (2016) attempts to identify the complexities and risks of our modern world. In order to understand the meaning of complexity and uncertainty, taking a look at the worlds’ most pressing issues is helpful. Searching for strategies to cope with these problems is a difficult task: surely there is no place for the simple principle of “vary-one-thing-at-a-time” (VOTAT) when it comes to global problems. The VOTAT strategy is helpful in the context of simple problems ( Wüstenberg et al., 2014 ); therefore, whether or not VOTAT is helpful in a given problem situation helps us distinguish simple from complex problems.

Because there exist no clear-cut strategies for complex problems, typical failures occur when dealing with uncertainty ( Dörner, 1996 ; Güss et al., 2015 ). Ramnarayan et al. (1997) put together a list of generic errors (e.g., not developing adequate action plans; lack of background control; learning from experience blocked by stereotype knowledge; reactive instead of proactive action) that are typical of knowledge-rich complex systems but cannot be found in simple problems.

Complex problem solving is not a one-dimensional, low-level construct. On the contrary, CPS is a multi-dimensional bundle of competencies existing at a high level of abstraction, similar to intelligence (but going beyond IQ). As Funke et al. (2018) state: “Assessment of transversal (in educational contexts: cross-curricular) competencies cannot be done with one or two types of assessment. The plurality of skills and competencies requires a plurality of assessment instruments.”

There are at least three different aspects of complex systems that are part of our understanding of a complex system: (1) a complex system can be described at different levels of abstraction; (2) a complex system develops over time, has a history, a current state, and a (potentially unpredictable) future; (3) a complex system is knowledge-rich and activates a large semantic network, together with a broad list of potential strategies (domain-specific as well as domain-general).

Complex problem solving is not only a cognitive process but is also an emotional one ( Spering et al., 2005 ; Barth and Funke, 2010 ) and strongly dependent on motivation (low-stakes versus high-stakes testing; see Hermes and Stelling, 2016 ).

Furthermore, CPS is a dynamic process unfolding over time, with different phases and with more differentiation than simply knowledge acquisition and knowledge application. Ideally, the process should entail identifying problems (see Dillon, 1982 ; Lee and Cho, 2007 ), even if in experimental settings, problems are provided to participants a priori . The more complex and open a given situation, the more options can be generated (T. S. Schweizer et al., 2016 ). In closed problems, these processes do not occur in the same way.

In analogy to the difference between formative (process-oriented) and summative (result-oriented) assessment ( Wiliam and Black, 1996 ; Bennett, 2011 ), CPS should not be reduced to the mere outcome of a solution process. The process leading up to the solution, including detours and errors made along the way, might provide a more differentiated impression of a person’s problem-solving abilities and competencies than the final result of such a process. This is one of the reasons why CPS environments are not, in fact, complex intelligence tests: research on CPS is not only about the outcome of the decision process, but it is also about the problem-solving process itself.

Complex problem solving is part of our daily life: finding the right person to share one’s life with, choosing a career that not only makes money, but that also makes us happy. Of course, CPS is not restricted to personal problems – life on Earth gives us many hard nuts to crack: climate change, population growth, the threat of war, the use and distribution of natural resources. In sum, many societal challenges can be seen as complex problems. To reduce that complexity to a one-hour lab activity on a random Friday afternoon puts it out of context and does not address CPS issues.

Theories about CPS should specify which populations they apply to. Across populations, one thing to consider is prior knowledge. CPS research with experts (e.g., Dew et al., 2009 ) is quite different from problem solving research using tasks that intentionally do not require any specific prior knowledge (see, e.g., Beckmann and Goode, 2014 ).

More than 20 years ago, Frensch and Funke (1995b) defined CPS as follows:

• simple  CPS occurs to overcome barriers between a given state and a desired goal state by means of behavioral and/or cognitive, multi-step activities. The given state, goal state, and barriers between given state and goal state are complex, change dynamically during problem solving, and are intransparent. The exact properties of the given state, goal state, and barriers are unknown to the solver at the outset. CPS implies the efficient interaction between a solver and the situational requirements of the task, and involves a solver’s cognitive, emotional, personal, and social abilities and knowledge. (p. 18)

The above definition is rather formal and does not account for content or relations between the simulation and the real world. In a sense, we need a new definition of CPS that addresses these issues. Based on our previous arguments, we propose the following working definition:

• simple  Complex problem solving is a collection of self-regulated psychological processes and activities necessary in dynamic environments to achieve ill-defined goals that cannot be reached by routine actions. Creative combinations of knowledge and a broad set of strategies are needed. Solutions are often more bricolage than perfect or optimal. The problem-solving process combines cognitive, emotional, and motivational aspects, particularly in high-stakes situations. Complex problems usually involve knowledge-rich requirements and collaboration among different persons.

The main differences to the older definition lie in the emphasis on (a) the self-regulation of processes, (b) creativity (as opposed to routine behavior), (c) the bricolage type of solution, and (d) the role of high-stakes challenges. Our new definition incorporates some aspects that have been discussed in this review but were not reflected in the 1995 definition, which focused on attributes of complex problems like dynamics or intransparency.

This leads us to the final reflection about the role of CPS for dealing with uncertainty and complexity in real life. We will distinguish thinking from reasoning and introduce the sense of possibility as an important aspect of validity.

CPS as Combining Reasoning and Thinking in an Uncertain Reality

Leading up to the Battle of Borodino in Leo Tolstoy’s novel “War and Peace”, Prince Andrei Bolkonsky explains the concept of war to his friend Pierre. Pierre expects war to resemble a game of chess: You position the troops and attempt to defeat your opponent by moving them in different directions.

“Far from it!”, Andrei responds. “In chess, you know the knight and his moves, you know the pawn and his combat strength. While in war, a battalion is sometimes stronger than a division and sometimes weaker than a company; it all depends on circumstances that can never be known. In war, you do not know the position of your enemy; some things you might be able to observe, some things you have to divine (but that depends on your ability to do so!) and many things cannot even be guessed at. In chess, you can see all of your opponent’s possible moves. In war, that is impossible. If you decide to attack, you cannot know whether the necessary conditions are met for you to succeed. Many a time, you cannot even know whether your troops will follow your orders…”

In essence, war is characterized by a high degree of uncertainty. A good commander (or politician) can add to that what he or she sees, tentatively fill in the blanks – and not just by means of logical deduction but also by intelligently bridging missing links. A bad commander extrapolates from what he sees and thus arrives at improper conclusions.

Many languages differentiate between two modes of mentalizing; for instance, the English language distinguishes between ‘thinking’ and ‘reasoning’. Reasoning denotes acute and exact mentalizing involving logical deductions. Such deductions are usually based on evidence and counterevidence. Thinking, however, is what is required to write novels. It is the construction of an initially unknown reality. But it is not a pipe dream, an unfounded process of fabrication. Rather, thinking asks us to imagine reality (“Wirklichkeitsfantasie”). In other words, a novelist has to possess a “sense of possibility” (“Möglichkeitssinn”, Robert Musil; in German, sense of possibility is often used synonymously with imagination even though imagination is not the same as sense of possibility, for imagination also encapsulates the impossible). This sense of possibility entails knowing the whole (or several wholes) or being able to construe an unknown whole that could accommodate a known part. The whole has to align with sociological and geographical givens, with the mentality of certain peoples or groups, and with the laws of physics and chemistry. Otherwise, the entire venture is ill-founded. A sense of possibility does not aim for the moon but imagines something that might be possible but has not been considered possible or even potentially possible so far.

Thinking is a means to eliminate uncertainty. This process requires both of the modes of thinking we have discussed thus far. Economic, political, or ecological decisions require us to first consider the situation at hand. Though certain situational aspects can be known, but many cannot. In fact, von Clausewitz (1832) posits that only about 25% of the necessary information is available when a military decision needs to be made. Even then, there is no way to guarantee that whatever information is available is also correct: Even if a piece of information was completely accurate yesterday, it might no longer apply today.

Once our sense of possibility has helped grasping a situation, problem solvers need to call on their reasoning skills. Not every situation requires the same action, and we may want to act this way or another to reach this or that goal. This appears logical, but it is a logic based on constantly shifting grounds: We cannot know whether necessary conditions are met, sometimes the assumptions we have made later turn out to be incorrect, and sometimes we have to revise our assumptions or make completely new ones. It is necessary to constantly switch between our sense of possibility and our sense of reality, that is, to switch between thinking and reasoning. It is an arduous process, and some people handle it well, while others do not.

If we are to believe Tuchman’s (1984) book, “The March of Folly”, most politicians and commanders are fools. According to Tuchman, not much has changed in the 3300 years that have elapsed since the misguided Trojans decided to welcome the left-behind wooden horse into their city that would end up dismantling Troy’s defensive walls. The Trojans, too, had been warned, but decided not to heed the warning. Although Laocoön had revealed the horse’s true nature to them by attacking it with a spear, making the weapons inside the horse ring, the Trojans refused to see the forest for the trees. They did not want to listen, they wanted the war to be over, and this desire ended up shaping their perception.

The objective of psychology is to predict and explain human actions and behavior as accurately as possible. However, thinking cannot be investigated by limiting its study to neatly confined fractions of reality such as the realms of propositional logic, chess, Go tasks, the Tower of Hanoi, and so forth. Within these systems, there is little need for a sense of possibility. But a sense of possibility – the ability to divine and construe an unknown reality – is at least as important as logical reasoning skills. Not researching the sense of possibility limits the validity of psychological research. All economic and political decision making draws upon this sense of possibility. By not exploring it, psychological research dedicated to the study of thinking cannot further the understanding of politicians’ competence and the reasons that underlie political mistakes. Christopher Clark identifies European diplomats’, politicians’, and commanders’ inability to form an accurate representation of reality as a reason for the outbreak of World War I. According to Clark’s (2012) book, “The Sleepwalkers”, the politicians of the time lived in their own make-believe world, wrongfully assuming that it was the same world everyone else inhabited. If CPS research wants to make significant contributions to the world, it has to acknowledge complexity and uncertainty as important aspects of it.

For more than 40 years, CPS has been a new subject of psychological research. During this time period, the initial emphasis on analyzing how humans deal with complex, dynamic, and uncertain situations has been lost. What is subsumed under the heading of CPS in modern research has lost the original complexities of real-life problems. From our point of view, the challenges of the 21st century require a return to the origins of this research tradition. We would encourage researchers in the field of problem solving to come back to the original ideas. There is enough complexity and uncertainty in the world to be studied. Improving our understanding of how humans deal with these global and pressing problems would be a worthwhile enterprise.

Author Contributions

JF drafted a first version of the manuscript, DD added further text and commented on the draft. JF finalized the manuscript.

Authors Note

After more than 40 years of controversial discussions between both authors, this is the first joint paper. We are happy to have done this now! We have found common ground!

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank the Deutsche Forschungsgemeinschaft (DFG) for the continuous support of their research over many years. Thanks to Daniel Holt for his comments on validity issues, thanks to Julia Nolte who helped us by translating German text excerpts into readable English and helped us, together with Keri Hartman, to improve our style and grammar – thanks for that! We also thank the two reviewers for their helpful critical comments on earlier versions of this manuscript. Finally, we acknowledge financial support by Deutsche Forschungsgemeinschaft and Ruprecht-Karls-Universität Heidelberg within their funding programme Open Access Publishing .

1 The fMRI-paper from Anderson (2012) uses the term “complex problem solving” for tasks that do not fall in our understanding of CPS and is therefore excluded from this list.

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Zenius Fellow

• UTBK-SBMPTN

Pengertian Problem Solving Beserta Teori dan Contoh Soalnya

• Posted by by Maulia Indriana Ghani
• Mei 10, 2022

Elo pernah main game tebak-tebakan, nggak? Misalnya, ada tiga orang, manakah yang termasuk pencuri? Nah, itu termasuk contoh problem solving. Apa pengertian problem solving? Gimana strategi penyelesaiannya? Yuk, kepoin!

Elo termasuk pencinta kopi, bukan? Biasanya, pencinta kopi itu kalau pagi-pagi sebelum beraktivitas, ya ngopi dulu. Kalau nggak ngopi, rasanya bakal lemas sepanjang hari, nggak bergairah.

Alhasil, kegiatan membuat kopi itu menjadi sesuatu yang elo lakukan secara otomatis tanpa proses berpikir panjang. Pokoknya langsung satsetsatset . Mulai dari menyiapkan cangkir, menuang kopi ke dalam cangkir, menambahkan gula, menuang air panas, mengaduk-aduk, dan yang terakhir, seruput, deh!

Lain halnya ketika elo mau membuat kopi ala coffee shop , misalnya latte art . Buat elo yang nggak biasa bikin latte art , kegiatan tersebut tentu membutuhkan proses berpikir, yang mencakup strategi dan perencanaan.

Misalnya, apa aja sih, yang gue butuhkan untuk membuat latte art ? Oh, gue butuh alatnya, bahan-bahan harus yang terbaik, lama proses pembuatannya juga perlu gue perhatikan supaya nggak telat berangkat sekolah, terakhir bentuk art -nya.

Kurang lebih, elo akan berpikir seperti itu, kan? Jadi, dalam menyelesaikan masalah atau problem solving itu elo akan menggunakan metode yang berbeda-beda. Misalnya pada contoh kasus kopi di atas, elo menggunakan metode planning perincian detail.

Kedua, ada metode perhitungan matematis. Jadi, elo menggunakan perhitungan dalam menyelesaikan suatu masalah. Selanjutnya, ada metode trial-error , elo coba, gagal, elo ulang lagi sampai berhasil.

Nah, cara terbaik untuk solve problem adalah elo harus tahu konteks masalah dan informasi yang elo punya terlebih dahulu untuk mendapatkan metode yang paling cocok digunakan. Namun, elo nggak harus memilih salah satu dari ketiga cara tersebut, kok. Elo bisa mengombinasikan ketiga cara tersebut untuk mendapatkan solusi yang terbaik.

Oke, contohnya bakal gue bahas setelah elo memahami pengertian problem solving di bawah ini, ya.

Apa Itu Problem Solving?

Elo pasti sering mendengar istilah problem solving , kan? Di sekolah pun kita dididik untuk memiliki skill yang satu ini. Nggak cuma di sekolah, kok. Dunia kerja pun membutuhkan orang-orang dengan skill tersebut.

Pasalnya, problem solving adalah bagian dari keterampilan atau kecakapan intelektual seseorang. Tanpa memahami dan memiliki skill tersebut, akan sulit rasanya saat elo menghadapi berbagai masalah atau hambatan dalam hidup.

Kita bisa mendefinisikan pengertian problem solving sebagai proses identifikasi masalah, mengembangkan solusi yang mungkin bisa digunakan, dan mengambil tindakan yang tepat dari pilihan solusi tersebut.

Oke, sekarang kita tahu nih, kalau problem solving itu secara istilah use logic atau menggunakan logika berpikir dan prosedur efektif untuk menyelesaikan suatu masalah setepat dan sesimpel mungkin.

Baca Juga : 5 Cara Melatih Logika Berpikir Supaya Lolos Tes Logika Penalaran

Jadi, jelas ya, bahwa tujuan problem solving itu untuk memecahkan suatu masalah. Selain itu, untuk melatih orang-orang dalam menghadapi permasalahan dan hambatan, mendapatkan langkah terbaik untuk menyelesaikan permasalahan, dan melatih orang untuk bertindak di situasi baru.

Ada nggak sih, pengertian problem solving secara teoritis? Ada. Teori problem solving yang akan gue angkat kali ini berdasarkan pendapat Marzano dkk (1988), bahwa problem solving adalah salah satu bagian dari proses berpikir yang berupa kemampuan untuk memecahkan permasalahan.

Nah, kalau di sekolah, tujuan problem solving ini untuk memecahkan masalah dalam pelajaran matematika, sains, dan ilmu sosial. Contohnya gimana, sih? Penasaran? Oke, lanjut ke poin berikutnya, ya.

Strategi Problem Solving

Coba deh, elo perhatikan soal dan penyelesaiannya di bawah ini!

Gimana, kebayang nggak sama cara di atas? Gue rincikan penyelesaiannya supaya elo bisa lebih mudah dalam memahaminya, ya.

Pertama, elo perhatikan dulu data yang disajikan. Dari data tersebut, elo bisa memperoleh informasi penting atau aturan-aturan suatu masalah. Ingat, bahwa aturan itu untuk elo perhatikan dan ikuti, bukan kontradiksi atau kebalikan dari aturan itu, ya!

Baca Juga : Mengenal Kesalahan Logika Beban Pembuktian

Selanjutnya, elo proses dan analisis datanya hingga menghasilkan solusi.

Dari contoh kasus tersebut, kita memperoleh satu hal penting. Hal penting apa, sih? Dari situ kita belajar, bahwa untuk memecahkan masalah secara tepat, kita perlu mengikuti serangkaian tahapan.

Kita bisa menyebut rangkaian tahapan tersebut sebagai strategi problem solving . Ada yang gue suka, nih. Bransford dan Stein (1993), memperkenalkan strategi problem solving dengan akronim IDEAL.

IDEAL = Identify, Define, Explore, Act dan Look

Gue uraikan satu per satu, ya.

I → Identify Problem

Pada tahap ini, elo perlu mengidentifikasi masalahnya terlebih dahulu. Karena, masalah itu kadang nggak sesederhana itu, guys.

Dalam beberapa kasus, orang-orang mungkin saja salah menafsirkan atau mengidentifikasikan masalah. Alhasil, upaya problem solving yang dilakukan nggak seefektif dan seefisien yang diharapkan, iya nggak?

Strategi yang bisa elo gunakan, misalnya dengan mengajukan pertanyaan mengenai masalah tersebut, cari tahu seluk-beluk permasalahan itu—bisa menjawab apa, siapa, mengapa, kapan, di mana, dan bagaimana.

Elo juga bisa memecah atau mengklasifikasikan permasalahan menjadi bagian yang lebih kecil. Lihat juga masalah itu dari berbagai sudut pandang. Kalau udah, elo bisa lanjut ke tahap selanjutnya.

D → Define Goal

Setelah identifikasi masalah, elo juga perlu mendefinisikan suatu masalah secara detail. Untuk apa? Tentu saja untuk dapat solve problem tersebut.

Cari tahu aspek mana sih, yang termasuk fakta, dan mana yang termasuk opini. Bedakan hal itu. Kemudian, definisikan masalah secara jelas dan identifikasi solusinya.

E → Explore Possible Strategies

Selanjutnya, gali solusinya. Manakah solusi yang paling potensial untuk memecahkan masalah tersebut?

Di tahap ini, elo perlu mengumpulkan banyak ide, sebanyak-banyaknya, ya.

Kalau udah ada banyak ide, langkah selanjutnya adalah mengembangkan strategi. Elo bisa menggunakan strategi heuristik, yaitu menemukan solusi berdasarkan pengalaman masa lalu yang mirip dengan masalah sekarang.

Atau menggunakan strategi algoritma, yaitu menemukan solusi dengan cara bertahap untuk mendapatkan solusi yang lebih akurat. Namun, tentu saja strategi algoritma lebih lama, karena elo harus merinci lebih detail dalam menyelesaikan masalahnya.

A → Anticipate Outcomes and Act

Setelah strategi tertentu dipilih, elo mulai melaksanakan strategi tersebut di tahap ini. Kira-kira, strategi yang udah gue pilih ini akan berhasil atau nggak, ya? Langkah ini sudah betul atau belum, ya? Efektif atau nggak, ya?

Selain menggunakan strategi, elo juga masih perlu memantau situasi. Pastikan bahwa masalah yang sedang diselesaikan sekarang itu nggak menimbulkan masalah baru.

L → Look back and Learn

Setelah solusi tercapai, bukan berarti elo bisa melenggang pergi gitu aja, ya. Kaji kembali solusi yang sudah dilaksanakan dan evaluasi dampaknya.

Kalau di sekolah, setelah elo menyelesaikan suatu soal, misalnya matematika, elo cek lagi hasilnya. Perhitungan elo udah benar atau ada yang keliru? Elo udah menggunakan cara yang tepat atau belum? Elo tadi baca soalnya teliti atau nggak? Begitu, kan?

Kalau semuanya sudah oke, artinya elo berhasil menyelesaikan suatu masalah. Kalau masih belum berhasil, elo coba lagi, ulang dari awal. Artinya, elo sedang menggunakan metode trial-error .

Gimana, paham sampai sini? Kalau elo masih kurang greget sama uraian di atas, jangan khawatir. Karena, elo bisa pelajari materi problem solving pakai animasi di video belajar Zenius dengan klik banner di bawah ini.

Contoh Soal Problem Solving dan Pembahasan

Setelah memahami uraian mengenai pengertian problem solving di atas, artinya elo udah siap menyelesaikan berbagai permasalahan dari soal-soal di bawah ini. Cekidot !

Contoh Soal 1

Zahra mengikuti acara amal dan ia kebagian mengumpulkan amplop-amplop yang berisi uang dari penyumbang. Amplop-amplop tersebut berisi uang kertas. Semua amplopnya berisi tiga uang kertas, namun ada juga beberapa amplop yang berisi satu, dua atau tiga nota (bukan uang). Semua uang kertas bisa bernilai Rp1.000, Rp5.000, Rp10.000, atau Rp20.000. Berapa jumlah uang terkecil yang nggak mungkin ada di dalam sebuah amplop?

A. Rp2.000.

B. Rp3.000.

C. Rp4.000.

D. Rp6.000.

E. Rp7.000.

Jawab: C. Rp4.000 .

Pembahasan:

Dari bacaan, kita peroleh kemungkinan-kemungkinan munculnya jumlah uang.

• Tiga uang = 3U.
• Satu nota bukan uang (artinya ada dua uang) = 2U + 1N.
• Dua nota bukan uang (artinya ada satu uang) = 1U + 2N.
• Tiga nota = 3N.

Uang yang ada di dalam amplop senilai Rp1.000, Rp5.000, Rp10.000, atau Rp20.000.

Nah, ditanyakan jumlah uang terkecil yang nggak mungkin ada dalam amplop. Kita coba satu per satu pilihan ganda di atas, berdasarkan aturan dari poin-poin yang udah dibuat ya.

Opsi A → Rp2.000.

Kita bisa peroleh dari 2U + 1N = Rp1.000 + Rp1.000 + nota = Rp2.000. Jadi, bukan opsi A jawabannya, ya.

Opsi B → Rp3.000.

Kita bisa memperolehnya dari 3U = Rp1.000 + Rp1.000 + Rp1.000 = Rp3.000. Jadi, bukan opsi B jawabannya, ya.

Opsi C → Rp4.000.

Kita coba satu per satu. Dimulai dari 3U dulu, ya. 3U akan menghasilkan Rp3.000, Rp7.000, dan seterusnya yang jumlahnya akan semakin besar. Nggak mungkin.

2U + 1N akan menghasilkan Rp2.000, Rp6.000, dan seterusnya.

1U + 2N akan menghasilkan Rp1.000, Rp5.000, dan seterusnya.

Artinya, kita nggak bisa memperoleh uang total Rp4.000 di dalam amplop. Jawabannya C, ya.

Penasaran sama opsi lainnya? Udah ketemu jawabannya, opsi D menghasilkan Rp6.000, ada ya dari 2U + 1N. Kemudian, opso E yaitu Rp7.000 diperoleh dari 3U. Kemungkinan, ada amplop yang totalnya Rp6.000 dan Rp7.000.

Jadi, jumlah uang terkecil yang nggak mungkin ada di dalam sebuah amplop adalah Rp4.000.

Contoh Soal 2

Perhatikan gambar di bawah ini!

Kalau kita lihat dari gambar bus di Indonesia yang sedang melaju di jalanan, kira-kira bus tersebut melaju ke arah kanan atau kiri?

Gue tantang elo untuk menjawab pertanyaan di atas. Ada yang bisa jawab, nggak?

Ayo, belajar jadi detektif! Elo identifikasi kasus di atas, kemudian cari strategi dan solusi yang paling tepat untuk menyelesaikan permasalahannya. Kalau udah, cantumkan jawaban elo di kolom komentar, ya!

Kalau bingung atau mau intip pembahasannya, elo bisa meluncur ke video contoh soal dan pembahasan problem solving teka-teki di sini .

Wah, nggak kerasa bahasan kita udah di ujung, nih. Sampai sini udah paham tentang pengertian problem solving, teori, tujuan, strategi, dan contoh soalnya? Kalau elo lebih suka belajar dengan nonton video, elo bisa mengakses materi UTBK lainnya di video Zenius. Elo juga bisa mencoba melatih kemampuan dengan level soal yang mirip UTBK beneran di Try Out bareng Zenius .

Kalau elo mau berlatih mengerjakan berbagai soal menarik, gampang banget! Elo bisa segera langganan paket Zenius dengan klik gambar di bawah ini!

Baca Juga : Panduan Belajar dan Soal Pola Gambar UTBK TPS/TPA

Overview of the Problem-Solving Mental Process — Verywell Mind (2022).

Problem Solving : Signifikansi, Pengertian, dan Ragamnya — Satya Widya, Vol 28, No. 2 (2012).

Pembelajaran Matematika Model Ideal Problem Solving dengan Teori Pemrosesan Informasi Untuk Pembentukan Pendidikan Karakter dan Pemecahan Masalah Materi Dimensi Tiga Kelas X SMA — Pythagoras, Vol. 7, No. 2 (2012).

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Home » Research Problem – Examples, Types and Guide

Research Problem – Examples, Types and Guide

Table of Contents

Research Problem

Definition:

Research problem is a specific and well-defined issue or question that a researcher seeks to investigate through research. It is the starting point of any research project, as it sets the direction, scope, and purpose of the study.

Types of Research Problems

Types of Research Problems are as follows:

Descriptive problems

These problems involve describing or documenting a particular phenomenon, event, or situation. For example, a researcher might investigate the demographics of a particular population, such as their age, gender, income, and education.

Exploratory problems

These problems are designed to explore a particular topic or issue in depth, often with the goal of generating new ideas or hypotheses. For example, a researcher might explore the factors that contribute to job satisfaction among employees in a particular industry.

Explanatory Problems

These problems seek to explain why a particular phenomenon or event occurs, and they typically involve testing hypotheses or theories. For example, a researcher might investigate the relationship between exercise and mental health, with the goal of determining whether exercise has a causal effect on mental health.

Predictive Problems

These problems involve making predictions or forecasts about future events or trends. For example, a researcher might investigate the factors that predict future success in a particular field or industry.

Evaluative Problems

These problems involve assessing the effectiveness of a particular intervention, program, or policy. For example, a researcher might evaluate the impact of a new teaching method on student learning outcomes.

How to Define a Research Problem

Defining a research problem involves identifying a specific question or issue that a researcher seeks to address through a research study. Here are the steps to follow when defining a research problem:

• Identify a broad research topic : Start by identifying a broad topic that you are interested in researching. This could be based on your personal interests, observations, or gaps in the existing literature.
• Conduct a literature review : Once you have identified a broad topic, conduct a thorough literature review to identify the current state of knowledge in the field. This will help you identify gaps or inconsistencies in the existing research that can be addressed through your study.
• Refine the research question: Based on the gaps or inconsistencies identified in the literature review, refine your research question to a specific, clear, and well-defined problem statement. Your research question should be feasible, relevant, and important to the field of study.
• Develop a hypothesis: Based on the research question, develop a hypothesis that states the expected relationship between variables.
• Define the scope and limitations: Clearly define the scope and limitations of your research problem. This will help you focus your study and ensure that your research objectives are achievable.
• Get feedback: Get feedback from your advisor or colleagues to ensure that your research problem is clear, feasible, and relevant to the field of study.

Components of a Research Problem

The components of a research problem typically include the following:

• Topic : The general subject or area of interest that the research will explore.
• Research Question : A clear and specific question that the research seeks to answer or investigate.
• Objective : A statement that describes the purpose of the research, what it aims to achieve, and the expected outcomes.
• Hypothesis : An educated guess or prediction about the relationship between variables, which is tested during the research.
• Variables : The factors or elements that are being studied, measured, or manipulated in the research.
• Methodology : The overall approach and methods that will be used to conduct the research.
• Scope and Limitations : A description of the boundaries and parameters of the research, including what will be included and excluded, and any potential constraints or limitations.
• Significance: A statement that explains the potential value or impact of the research, its contribution to the field of study, and how it will add to the existing knowledge.

Research Problem Examples

Following are some Research Problem Examples:

Research Problem Examples in Psychology are as follows:

• Exploring the impact of social media on adolescent mental health.
• Investigating the effectiveness of cognitive-behavioral therapy for treating anxiety disorders.
• Studying the impact of prenatal stress on child development outcomes.
• Analyzing the factors that contribute to addiction and relapse in substance abuse treatment.
• Examining the impact of personality traits on romantic relationships.

Research Problem Examples in Sociology are as follows:

• Investigating the relationship between social support and mental health outcomes in marginalized communities.
• Studying the impact of globalization on labor markets and employment opportunities.
• Analyzing the causes and consequences of gentrification in urban neighborhoods.
• Investigating the impact of family structure on social mobility and economic outcomes.
• Examining the effects of social capital on community development and resilience.

Research Problem Examples in Economics are as follows:

• Studying the effects of trade policies on economic growth and development.
• Analyzing the impact of automation and artificial intelligence on labor markets and employment opportunities.
• Investigating the factors that contribute to economic inequality and poverty.
• Examining the impact of fiscal and monetary policies on inflation and economic stability.
• Studying the relationship between education and economic outcomes, such as income and employment.

Political Science

Research Problem Examples in Political Science are as follows:

• Analyzing the causes and consequences of political polarization and partisan behavior.
• Investigating the impact of social movements on political change and policymaking.
• Studying the role of media and communication in shaping public opinion and political discourse.
• Examining the effectiveness of electoral systems in promoting democratic governance and representation.
• Investigating the impact of international organizations and agreements on global governance and security.

Environmental Science

Research Problem Examples in Environmental Science are as follows:

• Studying the impact of air pollution on human health and well-being.
• Investigating the effects of deforestation on climate change and biodiversity loss.
• Analyzing the impact of ocean acidification on marine ecosystems and food webs.
• Studying the relationship between urban development and ecological resilience.
• Examining the effectiveness of environmental policies and regulations in promoting sustainability and conservation.

Research Problem Examples in Education are as follows:

• Investigating the impact of teacher training and professional development on student learning outcomes.
• Studying the effectiveness of technology-enhanced learning in promoting student engagement and achievement.
• Analyzing the factors that contribute to achievement gaps and educational inequality.
• Examining the impact of parental involvement on student motivation and achievement.
• Studying the effectiveness of alternative educational models, such as homeschooling and online learning.

Research Problem Examples in History are as follows:

• Analyzing the social and economic factors that contributed to the rise and fall of ancient civilizations.
• Investigating the impact of colonialism on indigenous societies and cultures.
• Studying the role of religion in shaping political and social movements throughout history.
• Analyzing the impact of the Industrial Revolution on economic and social structures.
• Examining the causes and consequences of global conflicts, such as World War I and II.

Research Problem Examples in Business are as follows:

• Studying the impact of corporate social responsibility on brand reputation and consumer behavior.
• Investigating the effectiveness of leadership development programs in improving organizational performance and employee satisfaction.
• Analyzing the factors that contribute to successful entrepreneurship and small business development.
• Examining the impact of mergers and acquisitions on market competition and consumer welfare.
• Studying the effectiveness of marketing strategies and advertising campaigns in promoting brand awareness and sales.

Research Problem Example for Students

An Example of a Research Problem for Students could be:

“How does social media usage affect the academic performance of high school students?”

This research problem is specific, measurable, and relevant. It is specific because it focuses on a particular area of interest, which is the impact of social media on academic performance. It is measurable because the researcher can collect data on social media usage and academic performance to evaluate the relationship between the two variables. It is relevant because it addresses a current and important issue that affects high school students.

To conduct research on this problem, the researcher could use various methods, such as surveys, interviews, and statistical analysis of academic records. The results of the study could provide insights into the relationship between social media usage and academic performance, which could help educators and parents develop effective strategies for managing social media use among students.

Another example of a research problem for students:

“Does participation in extracurricular activities impact the academic performance of middle school students?”

This research problem is also specific, measurable, and relevant. It is specific because it focuses on a particular type of activity, extracurricular activities, and its impact on academic performance. It is measurable because the researcher can collect data on students’ participation in extracurricular activities and their academic performance to evaluate the relationship between the two variables. It is relevant because extracurricular activities are an essential part of the middle school experience, and their impact on academic performance is a topic of interest to educators and parents.

To conduct research on this problem, the researcher could use surveys, interviews, and academic records analysis. The results of the study could provide insights into the relationship between extracurricular activities and academic performance, which could help educators and parents make informed decisions about the types of activities that are most beneficial for middle school students.

Applications of Research Problem

Applications of Research Problem are as follows:

• Academic research: Research problems are used to guide academic research in various fields, including social sciences, natural sciences, humanities, and engineering. Researchers use research problems to identify gaps in knowledge, address theoretical or practical problems, and explore new areas of study.
• Business research : Research problems are used to guide business research, including market research, consumer behavior research, and organizational research. Researchers use research problems to identify business challenges, explore opportunities, and develop strategies for business growth and success.
• Healthcare research : Research problems are used to guide healthcare research, including medical research, clinical research, and health services research. Researchers use research problems to identify healthcare challenges, develop new treatments and interventions, and improve healthcare delivery and outcomes.
• Public policy research : Research problems are used to guide public policy research, including policy analysis, program evaluation, and policy development. Researchers use research problems to identify social issues, assess the effectiveness of existing policies and programs, and develop new policies and programs to address societal challenges.
• Environmental research : Research problems are used to guide environmental research, including environmental science, ecology, and environmental management. Researchers use research problems to identify environmental challenges, assess the impact of human activities on the environment, and develop sustainable solutions to protect the environment.

Purpose of Research Problems

The purpose of research problems is to identify an area of study that requires further investigation and to formulate a clear, concise and specific research question. A research problem defines the specific issue or problem that needs to be addressed and serves as the foundation for the research project.

Identifying a research problem is important because it helps to establish the direction of the research and sets the stage for the research design, methods, and analysis. It also ensures that the research is relevant and contributes to the existing body of knowledge in the field.

A well-formulated research problem should:

• Clearly define the specific issue or problem that needs to be investigated
• Be specific and narrow enough to be manageable in terms of time, resources, and scope
• Be relevant to the field of study and contribute to the existing body of knowledge
• Be feasible and realistic in terms of available data, resources, and research methods
• Be interesting and intellectually stimulating for the researcher and potential readers or audiences.

Characteristics of Research Problem

The characteristics of a research problem refer to the specific features that a problem must possess to qualify as a suitable research topic. Some of the key characteristics of a research problem are:

• Clarity : A research problem should be clearly defined and stated in a way that it is easily understood by the researcher and other readers. The problem should be specific, unambiguous, and easy to comprehend.
• Relevance : A research problem should be relevant to the field of study, and it should contribute to the existing body of knowledge. The problem should address a gap in knowledge, a theoretical or practical problem, or a real-world issue that requires further investigation.
• Feasibility : A research problem should be feasible in terms of the availability of data, resources, and research methods. It should be realistic and practical to conduct the study within the available time, budget, and resources.
• Novelty : A research problem should be novel or original in some way. It should represent a new or innovative perspective on an existing problem, or it should explore a new area of study or apply an existing theory to a new context.
• Importance : A research problem should be important or significant in terms of its potential impact on the field or society. It should have the potential to produce new knowledge, advance existing theories, or address a pressing societal issue.
• Manageability : A research problem should be manageable in terms of its scope and complexity. It should be specific enough to be investigated within the available time and resources, and it should be broad enough to provide meaningful results.

Advantages of Research Problem

The advantages of a well-defined research problem are as follows:

• Focus : A research problem provides a clear and focused direction for the research study. It ensures that the study stays on track and does not deviate from the research question.
• Clarity : A research problem provides clarity and specificity to the research question. It ensures that the research is not too broad or too narrow and that the research objectives are clearly defined.
• Relevance : A research problem ensures that the research study is relevant to the field of study and contributes to the existing body of knowledge. It addresses gaps in knowledge, theoretical or practical problems, or real-world issues that require further investigation.
• Feasibility : A research problem ensures that the research study is feasible in terms of the availability of data, resources, and research methods. It ensures that the research is realistic and practical to conduct within the available time, budget, and resources.
• Novelty : A research problem ensures that the research study is original and innovative. It represents a new or unique perspective on an existing problem, explores a new area of study, or applies an existing theory to a new context.
• Importance : A research problem ensures that the research study is important and significant in terms of its potential impact on the field or society. It has the potential to produce new knowledge, advance existing theories, or address a pressing societal issue.
• Rigor : A research problem ensures that the research study is rigorous and follows established research methods and practices. It ensures that the research is conducted in a systematic, objective, and unbiased manner.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Mengenal Apa itu Problem Solving, Manfaat dan Contohnya

Dalam kehidupan sehari-hari, kita sering dihadapkan pada berbagai masalah yang perlu diselesaikan. Dalam hal ini, kemampuan untuk memecahkan masalah atau problem solving adalah suatu keterampilan yang sangat penting. 

Saat ini masih banyak orang yang meremehkan tentang bagaimana cara problem solving yang baik dan benar. Padahal faktanya, problem solving yang buruk bisa berdampak buruk pula. Seperti salah dalam mengambil keputusan besar, hingga perkelahian karena perbedaan pendapat.

Nah, karenanya penting untuk memahami tentang apa itu problem solving, manfaat, hingga cara menerapkannya. 

• 1 Apa Itu Problem Solving?
• 2.1 1. Identifikasi Masalah
• 2.2 2. Pengumpulan Informasi
• 2.3 3. Analisis
• 2.4 4. Pengembangan Solusi
• 2.5 5. Pemilihan Solusi
• 2.6 6. Implementasi
• 2.7 7. Evaluasi
• 3.1 1. Pengembangan Kemampuan Berpikir Kritis
• 3.2 2. Peningkatan Kreativitas
• 3.3 3. Meningkatkan Efisiensi
• 3.4 4. Peningkatan Keterampilan Komunikasi
• 3.5 5. Kepercayaan Diri
• 3.6 6. Pengambilan Keputusan yang Lebih Baik
• 4.1 1. Saat Menumpahkan Air
• 4.2 2. Perencanaan Perjalanan
• 4.3 3. Konflik dengan Rekan Kerja
• 4.4 4. Memecahkan Masalah Matematika
• 5 Mau Mengasah Kemampuan Problem Solving?

Apa Itu Problem Solving?

Problem solving adalah proses kognitif yang melibatkan pemecahan masalah atau menemukan solusi untuk situasi atau permasalahan tertentu. Dalam bahasa Indonesia, kita dapat menyebutnya sebagai “pemecahan masalah.” 

Ini melibatkan pemikiran kreatif, analitis, dan kemampuan untuk mengatasi hambatan. Proses ini umumnya dilakukan untuk mengatasi situasi yang memerlukan solusi, baik dalam kehidupan sehari-hari maupun dalam berbagai konteks, seperti pekerjaan atau pendidikan.

Tahapan Problem Solving

Proses problem solving terdiri dari beberapa tahapan, yaitu:

1. Identifikasi Masalah

Tahap pertama adalah mengidentifikasi masalah dengan jelas. Ini melibatkan pemahaman yang mendalam tentang sifat masalah, penyebabnya, dan dampaknya. Identifikasi masalah yang tepat adalah kunci untuk memulai proses pemecahan masalah.

2. Pengumpulan Informasi

Setelah masalah diidentifikasi, langkah selanjutnya adalah mengumpulkan informasi yang relevan. Informasi ini bisa berasal dari berbagai sumber, termasuk observasi, penelitian, atau wawancara. Pengumpulan informasi membantu dalam memahami akar masalah dan faktor-faktor yang berkontribusi.

3. Analisis

Tahap analisis melibatkan pemikiran kritis dan kemampuan untuk menghubungkan fakta-fakta yang ada. Pada tahap ini, informasi yang telah dikumpulkan dievaluasi dengan cermat untuk memahami sifat masalah secara lebih mendalam.

4. Pengembangan Solusi

Setelah analisis, langkah berikutnya adalah mengembangkan berbagai solusi yang mungkin. Pada tahap ini, kreativitas sangat diperlukan. Solusi yang dihasilkan mungkin bersifat konvensional atau inovatif.

5. Pemilihan Solusi

Dari berbagai solusi yang ada, tahap ini melibatkan pemilihan solusi terbaik yang paling memungkinkan untuk menyelesaikan masalah. Keputusan ini harus didasarkan pada analisis yang baik.

6. Implementasi

Solusi yang dipilih kemudian diimplementasikan. Ini melibatkan tindakan nyata untuk memecahkan masalah. Pada tahap ini, perencanaan yang matang dan pelaksanaan yang efektif penting.

7. Evaluasi

Setelah implementasi, hasilnya dievaluasi. Dalam tahap ini, perlu diperiksa apakah masalah telah terselesaikan atau perlu perubahan lebih lanjut. Evaluasi juga membantu dalam menilai keberhasilan proses pemecahan masalah.

Baca Juga: 5 Metode Problem Solving dan Tips Menghadapi Tantangannya!

Manfaat Problem Solving

Pemecahan masalah memiliki banyak manfaat, terutama dalam konteks kehidupan sehari-hari dan berbagai bidang lainnya. Berikut adalah beberapa manfaat utama dari kemampuan pemecahan masalah:

1. Pengembangan Kemampuan Berpikir Kritis

Pemecahan masalah melibatkan analisis mendalam, evaluasi, dan pemikiran kritis. Ini membantu seseorang untuk menjadi pemikir yang lebih baik dan mampu mengambil keputusan yang lebih baik.

2. Peningkatan Kreativitas

Dalam upaya mencari solusi, pemecahan masalah mendorong seseorang untuk berpikir secara kreatif. Ini dapat menghasilkan solusi yang inovatif dan tidak konvensional.

3. Meningkatkan Efisiensi

Dengan kemampuan pemecahan masalah yang baik, tugas-tugas sehari-hari dapat diselesaikan dengan lebih efisien. Ini menghemat waktu dan sumber daya.

4. Peningkatan Keterampilan Komunikasi

Proses problem solving sering melibatkan berdiskusi dan kolaborasi dengan orang lain, yang dapat meningkatkan keterampilan komunikasi. Ini dapat meningkatkan keterampilan komunikasi interpersonal.

5. Kepercayaan Diri

Menyelesaikan masalah dengan sukses dapat meningkatkan kepercayaan diri seseorang. Mampu mengatasi masalah memberikan rasa pencapaian dan kepuasan pribadi.

6. Pengambilan Keputusan yang Lebih Baik

Kemampuan pemecahan masalah membantu seseorang dalam membuat keputusan yang lebih baik. Dengan analisis yang baik, keputusan yang diambil lebih mungkin membuahkan hasil yang positif.

Dalam rangkaian kehidupan sehari-hari, manfaat pemecahan masalah ini menjadikan keterampilan ini sangat penting. Mulai dari mengatasi masalah sederhana seperti memperbaiki keran yang bocor, hingga menyelesaikan masalah kompleks dalam dunia bisnis, problem solving adalah keterampilan yang bermanfaat.

Baca Juga: Analytical Thinking: Skill yang Paling Dibutuhkan di Dunia Kerja!

Contoh Problem Solving di Kehidupan Sehari-hari

Untuk memberikan pemahaman yang lebih baik, berikut beberapa contoh problem solving dalam kehidupan sehari-hari:

1. Saat Menumpahkan Air

Saat kamu menghadapi tumpahan air di lantai dapur. Kamu akan mengidentifikasi masalahnya, mengambil kain untuk membersihkannya (solusi), dan masalah terselesaikan.

2. Perencanaan Perjalanan

Saat kamu ingin merencanakan liburan keluarga. Dengan mengumpulkan informasi tentang destinasi, transportasi, dan akomodasi, kamu dapat mengembangkan rencana perjalanan yang optimal.

3. Konflik dengan Rekan Kerja

Saat kamu memiliki konflik dengan rekan kerja. Dengan berbicara dengannya dan mencari solusi bersama, kamu dapat mengatasi konflik tersebut.

4. Memecahkan Masalah Matematika

Seorang siswa dihadapkan pada soal matematika yang sulit. Dengan menganalisis soal dan mencari rumus yang sesuai, siswa dapat menyelesaikan soal tersebut.

Baca Juga: Mengenal Apa itu Leadership dan Sikap yang Harus Dimilikinya

Mau Mengasah Kemampuan Problem Solving?

Nah, sekarang Arkawan sudah pahan kan, tentang apa itu problem solving? Jika Arkawan masih bingung atau bahkan ingin mendalami keterampilan tentang problem solving ini, mungkin pelatihan problem solving dari Arkademi ini bisa membantumu!

Pada dasarnya, dalam dunia kerja kita tidak hanya perlu mengasah skill teknikal saja. Softskill seperti problem solving yang satu ini juga sangat dibutuhkan dalam pekerjaan bidang apapun.

Dengan mengidentifikasi masalah, mengumpulkan informasi, menganalisis, mengembangkan solusi, dan mengimplementasikannya, kita dapat mengatasi berbagai permasalahan dengan efektif di dunia kerja.

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Teknik analisis data: pengertian, jenis, dan tahapannya, ketahui berapa gaji data analyst dan jenjang kariernya.

PROBLEM SOLVING: SIGNIFIKANSI, PENGERTIAN, DAN RAGAMNYA

• Bambang Suteng Sulasmono Program Studi S1 PPKn - FKIP Universitas Kristen Satya Wacana

Pemecahan masalah (problem solving) merupakan bagian dari ketrampilan atau kecakapan intelektual yang dinilai sebagai hasil belajar yang penting dan signifikan dalam proses pendidikan. Signifikansi kecakapan pemecahan masalah itu dapat dilihat baik dari banyaknya perhatian berbagai aliran psikologi terhadap kecakapan intelektual ini, tingginya peringkat kecakapan itu dalam berbagai taksonomi hasil belajar, maupun dari posisi kecakapan ini dalam taksonomi disain pembelajaran. Terdapat banyak ragam pengertian maupun klasifikasi masalah. Dari segi cara pernyataannya masalah ada yang bersifat kebahasaan (lingustic), dan masalah yang bersifat bukan-kebahasaan (non-linguistic). Dari segi perumusan, cara menjawab dan kemungkinan jawabannya, masalah dapat dibedakan menjadi masalah yang dibatasi dengan baik (well-defined), dan masalah yang dibatasi tidak dengan baik (ill-defined). Ada juga yang membedakan menjadi masalah yang well-structured (distrukturkan dengan baik) dan masalah yang ill-structured (tidak distrukturkan dengan baik). Demikian juga terdapat banyak pendapat tentang proses pemecahan atas berbagai macam masalah yang ada tersebut. Ada yang berpendapat bahwa proses pemecahan atas masalah yang well defined maupun yang ill defined sama, namun ada juga yang berpendapat bahwa proses pemecahan kedua jenis masalah di atas berbeda.

Borich, G.D. 1996. Effective Teaching Methods. Third Edition, NJ: Prentice Hall

Frederiksen, N. 1984. Implications of Cognitive Theory for Instruction in Problem Solving; Review of Educational Research;Vol. 54 (3): 363-407.

Fuchs, L.S. et all. 2003. Explicitly Teaching for Transfer: Effects on Third-Grade Students’ Mathematical Problem Solving; Journal of Educational Psychology; Vol. 95 (2): 293 – 305.

Gagne, R.M. & Briggs, L.J. 1979. Principles of Instructinal Design. Second Edition; New York: Holt, Rinehart and Winston.

Ge, Xun & Land. S.M., 2004. A Conceptual Framework for Scaffolding Ill-Structured Problem solving Processess Using Question Prompts and Peer Interactions; ETR&D : Vol. 52 (2) pp 5-22.

Greeno, J.G. 1978. Natures of Problem Solving Abilities. Dalam W.K. Estes (ed) Handbook of Learning and Cognitive Processes. Volume 5. Human Information Processing; New Jersey: Lawrence Erlbaum Associates, Publisher.

Girl, T.A., Wah, L.K.M., Kang, G.Ng., & Sai, C.L. 2002. New Paradigm for Science Education. A Perspective of Teaching Problem-Solving, Creative Teaching and Primary Science Education; Singapore: Prentice Hall.

Hokanson, B. & Hooper, S. 2004. Level of Teaching: A Taxonomy for Instructional Design. Educational Technology; November-December.

Jonnasen, D.H. & Serrano, J.H. 2002. Case-Based Reasoning and Instructional Design: Using Stories to Support Problem Solving; ETR&D: Vol. 50 (2) pp 65 – 77.

Kemp. J.E., Morrison, G.R. & Ross, S.M. 1994. Designing Effective Instruction; New York: Maxwell Macmillan International.

Lampert. M, 1990. When the Problem Is Not the Question and the Solution Is Not Answer: Mathematical Knowing and Teaching. American Educational Research Journal; Spring. Vol. 27 (1), pp 29 –63.

Marzano, R.J. et all, 1988. Dimension of Thinking: A Framework for Curriculum and Instruction. Viginia: Association for Supervision and Curriculum Development.

McLellan, H. 2004. The Case for Case-Based Teaching in Online Classes; Educational Technology: July - August.

Nastasi, B.K., Clements, D.H. & battista, M.T. 1990. Social-Cognitive Interactions, Motivation, and Cognitive Growth in Logo Programming and CAI Problem-Solving Environments. Journal of Educational Psychology; Vol. 82 (1): 150-158.

Palumbo.D.B. 1990. Programming Language/Problem-Solving Research: A Review of Relevant Issue. Review of Educational Research; Spring. Vol. 60 (1), pp 65 –89.

Qin, Z., Johnson, D.W. & Johnson R.T. 1995. Cooperative Versus Competitive Effort and Problem Solving; Review of Educational Research, Vol. 60 (2): 129 –143.

Steinberg, R.J. 1999. Cognitive Psychology. Second Edition. Philadephia: Harcout Brace College Publishers.

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Pengertian Problem Solving: Aspek, Ciri, dan Langkah-langkahnya 

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Program pintar, pengertian problem solving: aspek, ciri, dan langkah-langkahnya , serafica gischa.

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KOMPAS.com - Problem solving termasuk soft skill yang harus dimiliki setiap individu, karena memiliki manfaat ketika sudah bekerja di perusahaan. 

Dilansir dari buku Handbook of Cognitive-Behavioral Therapies (3rd Edition) (2010) oleh D'Zurilla dan Nezu, social problem solving adalah suatu proses di mana individu berusaha menangani stres dalam diri, yang juga dapat berfungsi sebagai mediator dalam menangani stres dan tekanan emosional. 

Adapun jenis permasalahan yang digunakan dalam social problem solving , seperti depresi, kecemasan, perilaku bunuh diri, penyakit mental yang berat, putus asa, pesimis, rawan kemarahan, penyalahgunaan zat, kriminal, harga diri yang rendah, stres kerja, dan pelecehan seksual.

Baca juga: Pengertian Problem Solving Menurut Ahli

Aspek kemampuan problem solving  

Menurut Polya dalam bukunya How to Solve It: A New Aspect of Mathematical Method (Second ed) (1973), terdapat empat aspek  kemampuan problem solving , sebagai berikut:

• Memahami masalah

Pemahaman masalah sangat menentukan kesuksesan dalam menemukan solusi masalah. Pada aspek ini melibatkan pendalaman situasi masalah, melakukan pemilahan fakta-fakta, menentukan hubungan di antara fakta-fakta dan membuat formulasi pertanyaan masalah. 

Setiap permasalahan harus dipahami berulang kali dan dipelajari dengan saksama.

• Membuat rencana pemecahan masalah

Rencana solusi masalah dibangun dengan mempertimbangkan struktur masalah dan pertanyaan yang harus dijawab. Pada proses pemecahan masalah siswa dikondisikan memiliki pengalaman dalam menentukan strategi pemecahan masalah.

• Melaksanakan rencana pemecahan masalah

Pada saat mencari solusi yang tepat, rencana yang sudah dibuat harus dilaksanakan dengan hati-hati. Diagram, tabel atau urutan dibangun secara saksama sehingga si pemecah masalah tidak akan bingung. 

Jika muncul ketidak konsistenan ketika melaksanakan rencana, proses harus ditelaah ulang untuk mencari sumber kesulitan masalah.

• Melihat (mengecek) kembali

Selama melakukan pengecekan, solusi masalah tetap di pertimbangkan. Harus tetap cocok terhadap akar masalah meskipun kelihatan tidak beralasan.

Baca juga: Mengenal Individu dengan Karakteristik Self Control

Ciri-ciri problem solving  

Metode problem solving memiliki ciri-ciri, sebagai berikut: 

• Menyiapkan masalah yang jelas untuk diselesaikan

Masalah ini harus tumbuh dari peserta didik sesuai dengan taraf kemampuannya, juga sesuai dengan materi yang disampaikannya. Serta ada dalam kehidupan nyata peserta didik.

• Merumuskan penyelesaian masalah dengan berbagai pendekatan

Mencari data atau keterangan yang dapat memecahkan masalah tersebut. Misalnya dengan membaca buku, meneliti, bertanya, atau pengalaman peserta didik sendiri.

• Menyelesaikan masalah sesuai rencana

Melakukan pembuktian atau pengecekan dari tiap tahap rencana penyelesaian masalah yang telah dirumuskan. Kemudian menjelaskan tahap-tahap penyelesaian dengan benar.

• Memeriksa jawaban yang telah dilakukan dalam penyelesaian masalah

Setelah memeriksa jawaban yang dilakukan dalam penyelesaian masalah, kemudian memberikan penekanan dan menarik kesimpulan atas penyelesaian masalah.

Baca juga: Kegunaan dan Manfaat Self Control dalam kehidupan Sehari-hari

Langkah-langkah kemampuan problem solving  

Disadur dari buku Kurikulum dan Pembelajaran (2013) oleh Oemar Hamalik, ada tujuh langkah kemampuan problem solving secara umum , yaitu: 

• Menghadapi masalah, artinya individu menyadari ada suatu masalah yang dihadapi
• Merumuskan masalah, menjabarkan masalah dengan jelas dan spesifik atau rinci
• Merumuskan hipotesis, merumuskan kemungkinan-kemungkinan jawaban atas masalah tersebut yang masih perlu diuji kebenarannya
• Mengumpulkan dan mengolah data/informasi dengan teknik dan prosedur tertentu
• Menguji hipotesis berdasarkan data/informasi yang telah dikumpulkan dan diolah
• Menarik kesimpulan berdasarkan pengujian hipotesis
• Menerapkan hasil pemecahan masalah situasi baru.

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Tag materi IPS kelas 9 pengertian problem solving adalah social problem solving adalah aspek-aspek problem solving ciri-ciri problem solving langkah kemampuan problem solving secara umum

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Organizing Your Social Sciences Research Paper

• The Research Problem/Question
• Purpose of Guide
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A research problem is a definite or clear expression [statement] about an area of concern, a condition to be improved upon, a difficulty to be eliminated, or a troubling question that exists in scholarly literature, in theory, or within existing practice that points to a need for meaningful understanding and deliberate investigation. A research problem does not state how to do something, offer a vague or broad proposition, or present a value question. In the social and behavioral sciences, studies are most often framed around examining a problem that needs to be understood and resolved in order to improve society and the human condition.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Guba, Egon G., and Yvonna S. Lincoln. “Competing Paradigms in Qualitative Research.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, editors. (Thousand Oaks, CA: Sage, 1994), pp. 105-117; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

Importance of...

The purpose of a problem statement is to:

• Introduce the reader to the importance of the topic being studied . The reader is oriented to the significance of the study.
• Anchors the research questions, hypotheses, or assumptions to follow . It offers a concise statement about the purpose of your paper.
• Place the topic into a particular context that defines the parameters of what is to be investigated.
• Provide the framework for reporting the results and indicates what is probably necessary to conduct the study and explain how the findings will present this information.

In the social sciences, the research problem establishes the means by which you must answer the "So What?" question. This declarative question refers to a research problem surviving the relevancy test [the quality of a measurement procedure that provides repeatability and accuracy]. Note that answering the "So What?" question requires a commitment on your part to not only show that you have reviewed the literature, but that you have thoroughly considered the significance of the research problem and its implications applied to creating new knowledge and understanding or informing practice.

To survive the "So What" question, problem statements should possess the following attributes:

• Clarity and precision [a well-written statement does not make sweeping generalizations and irresponsible pronouncements; it also does include unspecific determinates like "very" or "giant"],
• Demonstrate a researchable topic or issue [i.e., feasibility of conducting the study is based upon access to information that can be effectively acquired, gathered, interpreted, synthesized, and understood],
• Identification of what would be studied, while avoiding the use of value-laden words and terms,
• Identification of an overarching question or small set of questions accompanied by key factors or variables,
• Identification of key concepts and terms,
• Articulation of the study's conceptual boundaries or parameters or limitations,
• Some generalizability in regards to applicability and bringing results into general use,
• Conveyance of the study's importance, benefits, and justification [i.e., regardless of the type of research, it is important to demonstrate that the research is not trivial],
• Does not have unnecessary jargon or overly complex sentence constructions; and,
• Conveyance of more than the mere gathering of descriptive data providing only a snapshot of the issue or phenomenon under investigation.

Bryman, Alan. “The Research Question in Social Research: What is its Role?” International Journal of Social Research Methodology 10 (2007): 5-20; Brown, Perry J., Allen Dyer, and Ross S. Whaley. "Recreation Research—So What?" Journal of Leisure Research 5 (1973): 16-24; Castellanos, Susie. Critical Writing and Thinking. The Writing Center. Dean of the College. Brown University; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Selwyn, Neil. "‘So What?’…A Question that Every Journal Article Needs to Answer." Learning, Media, and Technology 39 (2014): 1-5; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518.

Structure and Writing Style

I.  Types and Content

There are four general conceptualizations of a research problem in the social sciences:

• Casuist Research Problem -- this type of problem relates to the determination of right and wrong in questions of conduct or conscience by analyzing moral dilemmas through the application of general rules and the careful distinction of special cases.
• Difference Research Problem -- typically asks the question, “Is there a difference between two or more groups or treatments?” This type of problem statement is used when the researcher compares or contrasts two or more phenomena. This a common approach to defining a problem in the clinical social sciences or behavioral sciences.
• Descriptive Research Problem -- typically asks the question, "what is...?" with the underlying purpose to describe the significance of a situation, state, or existence of a specific phenomenon. This problem is often associated with revealing hidden or understudied issues.
• Relational Research Problem -- suggests a relationship of some sort between two or more variables to be investigated. The underlying purpose is to investigate specific qualities or characteristics that may be connected in some way.

A problem statement in the social sciences should contain :

• A lead-in that helps ensure the reader will maintain interest over the study,
• A declaration of originality [e.g., mentioning a knowledge void or a lack of clarity about a topic that will be revealed in the literature review of prior research],
• An indication of the central focus of the study [establishing the boundaries of analysis], and
• An explanation of the study's significance or the benefits to be derived from investigating the research problem.

NOTE :   A statement describing the research problem of your paper should not be viewed as a thesis statement that you may be familiar with from high school. Given the content listed above, a description of the research problem is usually a short paragraph in length.

II.  Sources of Problems for Investigation

The identification of a problem to study can be challenging, not because there's a lack of issues that could be investigated, but due to the challenge of formulating an academically relevant and researchable problem which is unique and does not simply duplicate the work of others. To facilitate how you might select a problem from which to build a research study, consider these sources of inspiration:

Deductions from Theory This relates to deductions made from social philosophy or generalizations embodied in life and in society that the researcher is familiar with. These deductions from human behavior are then placed within an empirical frame of reference through research. From a theory, the researcher can formulate a research problem or hypothesis stating the expected findings in certain empirical situations. The research asks the question: “What relationship between variables will be observed if theory aptly summarizes the state of affairs?” One can then design and carry out a systematic investigation to assess whether empirical data confirm or reject the hypothesis, and hence, the theory.

Interdisciplinary Perspectives Identifying a problem that forms the basis for a research study can come from academic movements and scholarship originating in disciplines outside of your primary area of study. This can be an intellectually stimulating exercise. A review of pertinent literature should include examining research from related disciplines that can reveal new avenues of exploration and analysis. An interdisciplinary approach to selecting a research problem offers an opportunity to construct a more comprehensive understanding of a very complex issue that any single discipline may be able to provide.

Interviewing Practitioners The identification of research problems about particular topics can arise from formal interviews or informal discussions with practitioners who provide insight into new directions for future research and how to make research findings more relevant to practice. Discussions with experts in the field, such as, teachers, social workers, health care providers, lawyers, business leaders, etc., offers the chance to identify practical, “real world” problems that may be understudied or ignored within academic circles. This approach also provides some practical knowledge which may help in the process of designing and conducting your study.

Personal Experience Don't undervalue your everyday experiences or encounters as worthwhile problems for investigation. Think critically about your own experiences and/or frustrations with an issue facing society or related to your community, your neighborhood, your family, or your personal life. This can be derived, for example, from deliberate observations of certain relationships for which there is no clear explanation or witnessing an event that appears harmful to a person or group or that is out of the ordinary.

Relevant Literature The selection of a research problem can be derived from a thorough review of pertinent research associated with your overall area of interest. This may reveal where gaps exist in understanding a topic or where an issue has been understudied. Research may be conducted to: 1) fill such gaps in knowledge; 2) evaluate if the methodologies employed in prior studies can be adapted to solve other problems; or, 3) determine if a similar study could be conducted in a different subject area or applied in a different context or to different study sample [i.e., different setting or different group of people]. Also, authors frequently conclude their studies by noting implications for further research; read the conclusion of pertinent studies because statements about further research can be a valuable source for identifying new problems to investigate. The fact that a researcher has identified a topic worthy of further exploration validates the fact it is worth pursuing.

III.  What Makes a Good Research Statement?

A good problem statement begins by introducing the broad area in which your research is centered, gradually leading the reader to the more specific issues you are investigating. The statement need not be lengthy, but a good research problem should incorporate the following features:

1.  Compelling Topic The problem chosen should be one that motivates you to address it but simple curiosity is not a good enough reason to pursue a research study because this does not indicate significance. The problem that you choose to explore must be important to you, but it must also be viewed as important by your readers and to a the larger academic and/or social community that could be impacted by the results of your study. 2.  Supports Multiple Perspectives The problem must be phrased in a way that avoids dichotomies and instead supports the generation and exploration of multiple perspectives. A general rule of thumb in the social sciences is that a good research problem is one that would generate a variety of viewpoints from a composite audience made up of reasonable people. 3.  Researchability This isn't a real word but it represents an important aspect of creating a good research statement. It seems a bit obvious, but you don't want to find yourself in the midst of investigating a complex research project and realize that you don't have enough prior research to draw from for your analysis. There's nothing inherently wrong with original research, but you must choose research problems that can be supported, in some way, by the resources available to you. If you are not sure if something is researchable, don't assume that it isn't if you don't find information right away--seek help from a librarian !

NOTE:   Do not confuse a research problem with a research topic. A topic is something to read and obtain information about, whereas a problem is something to be solved or framed as a question raised for inquiry, consideration, or solution, or explained as a source of perplexity, distress, or vexation. In short, a research topic is something to be understood; a research problem is something that needs to be investigated.

IV.  Asking Analytical Questions about the Research Problem

Research problems in the social and behavioral sciences are often analyzed around critical questions that must be investigated. These questions can be explicitly listed in the introduction [i.e., "This study addresses three research questions about women's psychological recovery from domestic abuse in multi-generational home settings..."], or, the questions are implied in the text as specific areas of study related to the research problem. Explicitly listing your research questions at the end of your introduction can help in designing a clear roadmap of what you plan to address in your study, whereas, implicitly integrating them into the text of the introduction allows you to create a more compelling narrative around the key issues under investigation. Either approach is appropriate.

The number of questions you attempt to address should be based on the complexity of the problem you are investigating and what areas of inquiry you find most critical to study. Practical considerations, such as, the length of the paper you are writing or the availability of resources to analyze the issue can also factor in how many questions to ask. In general, however, there should be no more than four research questions underpinning a single research problem.

Given this, well-developed analytical questions can focus on any of the following:

• Highlights a genuine dilemma, area of ambiguity, or point of confusion about a topic open to interpretation by your readers;
• Yields an answer that is unexpected and not obvious rather than inevitable and self-evident;
• Provokes meaningful thought or discussion;
• Raises the visibility of the key ideas or concepts that may be understudied or hidden;
• Suggests the need for complex analysis or argument rather than a basic description or summary; and,
• Offers a specific path of inquiry that avoids eliciting generalizations about the problem.

NOTE:   Questions of how and why concerning a research problem often require more analysis than questions about who, what, where, and when. You should still ask yourself these latter questions, however. Thinking introspectively about the who, what, where, and when of a research problem can help ensure that you have thoroughly considered all aspects of the problem under investigation and helps define the scope of the study in relation to the problem.

V.  Mistakes to Avoid

Beware of circular reasoning! Do not state the research problem as simply the absence of the thing you are suggesting. For example, if you propose the following, "The problem in this community is that there is no hospital," this only leads to a research problem where:

• The need is for a hospital
• The objective is to create a hospital
• The method is to plan for building a hospital, and
• The evaluation is to measure if there is a hospital or not.

This is an example of a research problem that fails the "So What?" test . In this example, the problem does not reveal the relevance of why you are investigating the fact there is no hospital in the community [e.g., perhaps there's a hospital in the community ten miles away]; it does not elucidate the significance of why one should study the fact there is no hospital in the community [e.g., that hospital in the community ten miles away has no emergency room]; the research problem does not offer an intellectual pathway towards adding new knowledge or clarifying prior knowledge [e.g., the county in which there is no hospital already conducted a study about the need for a hospital, but it was conducted ten years ago]; and, the problem does not offer meaningful outcomes that lead to recommendations that can be generalized for other situations or that could suggest areas for further research [e.g., the challenges of building a new hospital serves as a case study for other communities].

Alvesson, Mats and Jörgen Sandberg. “Generating Research Questions Through Problematization.” Academy of Management Review 36 (April 2011): 247-271 ; Choosing and Refining Topics. Writing@CSU. Colorado State University; D'Souza, Victor S. "Use of Induction and Deduction in Research in Social Sciences: An Illustration." Journal of the Indian Law Institute 24 (1982): 655-661; Ellis, Timothy J. and Yair Levy Nova. "Framework of Problem-Based Research: A Guide for Novice Researchers on the Development of a Research-Worthy Problem." Informing Science: the International Journal of an Emerging Transdiscipline 11 (2008); How to Write a Research Question. The Writing Center. George Mason University; Invention: Developing a Thesis Statement. The Reading/Writing Center. Hunter College; Problem Statements PowerPoint Presentation. The Writing Lab and The OWL. Purdue University; Procter, Margaret. Using Thesis Statements. University College Writing Centre. University of Toronto; Shoket, Mohd. "Research Problem: Identification and Formulation." International Journal of Research 1 (May 2014): 512-518; Trochim, William M.K. Problem Formulation. Research Methods Knowledge Base. 2006; Thesis and Purpose Statements. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Thesis Statements. The Writing Center. University of North Carolina; Tips and Examples for Writing Thesis Statements. The Writing Lab and The OWL. Purdue University; Pardede, Parlindungan. “Identifying and Formulating the Research Problem." Research in ELT: Module 4 (October 2018): 1-13; Walk, Kerry. Asking an Analytical Question. [Class handout or worksheet]. Princeton University; White, Patrick. Developing Research Questions: A Guide for Social Scientists . New York: Palgrave McMillan, 2009; Li, Yanmei, and Sumei Zhang. "Identifying the Research Problem." In Applied Research Methods in Urban and Regional Planning . (Cham, Switzerland: Springer International Publishing, 2022), pp. 13-21.

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How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

Podcast transcript

Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

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Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

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