research paper on successful business

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• 03 Oct 2023
• What Do You Think?

Do Leaders Learn More From Success or Failure?

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• 05 Sep 2023

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When we slip up, we are often filled with shame, and our instinct is to hide. Instead, people and businesses should applaud smart risk-taking, even when things don't work out, and closely examine their failures to learn from them, says Amy Edmondson.

Failing Well: How Your ‘Intelligent Failure’ Unlocks Your Full Potential

We tend to avoid failure at all costs. But our smarter missteps are worthwhile because they can force us to take a different path that points us toward personal and professional success, says Amy Edmondson.

• 14 Dec 2017
• Working Paper Summaries

Personality Traits of Entrepreneurs: A Review of Recent Literature

This paper brings together recent findings in the academic literature on the prevalence of various personality traits among entrepreneurs and their impact on venture performance. It focuses on three themes: (1) personality traits of entrepreneurs and how they compare to other groups; (2) attitudes towards risk that entrepreneurs display; and (3) overall goals and aspirations that entrepreneurs bring to their pursuits.

• 06 Sep 2017

Class Matters: The Role of Social Class in High-Achieving Women's Career Narratives

This analysis of interviews with 40 female executives and entrepreneurs highlights five distinct types of career narratives that high-achieving women employ to explain their own career success. These narratives vary with the women’s family-of-origin social class. Among its contributions to practice, the study sheds light on the diversity of approaches possible in a successful career.

• 27 Feb 2017
• Research & Ideas

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Reputation and resilience are key ingredients that determine whether companies will survive tumultuous markets, according to a new paper by Geoffrey Jones, Tarun Khanna, Cheng Gao, and Tiona Zuzul. Open for comment; 0 Comments.

• 21 Aug 2012

How to Sink a Startup

Noam Wasserman, author of the recently released book "The Founder's Dilemmas: Anticipating and Avoiding the Pitfalls That Can Sink a Startup," discusses ill-advised entrepreneurial behavior. From the HBS Alumni Bulletin. Closed for comment; 0 Comments.

• 06 Sep 2011

Cheese Moving: Effecting Change Rather Than Accepting It

In his new business fable, I Moved Your Cheese, Professor Deepak Malhotra challenges the idea that change is simply something we must anticipate, tolerate, and accept. Instead, the book teaches readers that success often lies in first questioning changes in the workplace and, if necessary, in effecting new changes ourselves. Q&A plus book excerpt. Closed for comment; 0 Comments.

• 02 Jun 2010

How Do You Weigh Strategy, Execution, and Culture in an Organization’s Success?

Summing up: Respondents who ventured to place weights on the determinants of success gave the nod to culture by a wide margin, says HBS professor Jim Heskett. (Online forum now closed. Next forum opens July 2.) Closed for comment; 0 Comments.

• 02 Feb 2009

The Success of Persistent Entrepreneurs

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• 29 Aug 2005

How Organizations Create Social Value

A study of smart practices by social and business organizations in Iberoamerica. Research by HBS professor James Austin, HBS senior researcher Ezequiel A. Reficco, and UNIANDES professor Roberto Gutiérrez. Closed for comment; 0 Comments.

• 08 Mar 2004

Secret to Success: Go for “Just Enough”

Being the very best in your chosen field is, paradoxically, a matter of accepting your limitations. A book excerpt by Harvard Business School’s Laura Nash and Howard Stevenson. Closed for comment; 0 Comments.

• 24 Jun 2002

Four Keys of Enduring Success: How High Achievers Win

What is success to you? HBS professor Howard Stevenson offers insights from research he and HBS senior research fellow Laura Nash are conducting on the meaning of success for high achievers. Closed for comment; 0 Comments.

• 02 Apr 2001

Not All M&As Are Alike—and That Matters

In this Harvard Business Review article, Professor Joseph L. Bower shares some of the results of his year-long study of M&A activity sponsored by HBS. Discover how five distinct merger and acquisition strategies scenarios play out—and his recommendations for success. Closed for comment; 0 Comments.

A Systems View Across Time and Space

• Open access
• Published: 22 February 2022

Defining entrepreneurial success to improve guidance services: a study with a comprehensive database from Andalusia

• Manuel Chaves-Maza 1 &
• Eugenio M. Fedriani   ORCID: orcid.org/0000-0002-1707-3308 1  

Journal of Innovation and Entrepreneurship volume  11 , Article number:  22 ( 2022 ) Cite this article

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The aim of this paper is to assess the level of success achieved by entrepreneurs. The concept of success has many subjective facets, and it needs to be evaluated to reach other higher objectives, such as improving support systems for entrepreneurs. The usual pre-existing focuses for the evaluation of business performance are analysed and adapted. Based on a real case, the most relevant variables for detecting success are studied and an algorithmic process (based on decision trees) is established to ascertain whether an entrepreneur has achieved success. The data refers to entrepreneurs from Andalusia, the European region with the highest unemployment rates and where support for entrepreneurship is on the agenda of all political parties. The model specifies a minimal set of variables to evaluate success in each case. Subsequently, a simple set of 29 questions is also offered, serving to classify most entrepreneurs (over 98% of 2221 individuals in the case analysed) by their level of success. An objective procedure to measure the success of entrepreneurs is given. Such method is based on artificial intelligence and on three focuses: positioning, expectations and evolution. Both the variables used in this case and the 29 questions necessary to classify the entrepreneurs by their level of success are explicitly provided.

Introduction

Entrepreneurship has always existed, though it seems to have become particularly relevant in recent times, perhaps due to the latest economic crisis (Chadha & Dutta, 2020 ). Society asks for entrepreneurship, above all, of those who do not find their place in the labour market. Entrepreneurs, for their part, dream of finding a good idea and designing a business model that will improve their situation. This desire, predefined or not (Hovig et al., 2018 ), underlies part of the definition of the ‘business success of the entrepreneur’.

It appears obvious that success is a subjective concept (Al Issa, 2021 ), and so it cannot have a universally valid quantitative definition. However, it is only by means of a reasonable definition of success that it is possible to perform a rigorous analysis of the factors that produce or facilitate it, and so it makes sense to use mathematical and statistical tools to attempt to understand success on the basis of objective information taken from cases of entrepreneurship.

One of the first consequences of the quantitative approach is that there is no single level of success. There could be up to a different level for each entrepreneurship project, but that would not be useful; to allow an accurate ranking, we will minimize the number of levels of success. The opposite pole to successful entrepreneurship is failure, understood as the failure of the entrepreneur’s business idea to survive. In view of the complexity of the concept being evaluated, it would appear useful to add, at least, a third category so as to distinguish between successful entrepreneurs and entrepreneurs who simply survive. That third category could be called: moderate success or survival (without proper success); it will be referred to as "survival" from now on. Thus, the simplest classification that would be generated goes as follows: success, survival, and failure. In fact, we will adopt this three-level classification.

The aim of entrepreneur support institutions should be to minimise failure, if possible, while maximising success. These institutions should take into account the idea of success held by each individual entrepreneur and they should be able to evaluate their positioning with respect to this goal at all times (Zhai et al., 2019 ). Recognition of success is, therefore, fundamental to offer personalised support and advice to the entrepreneur. We must also admit that society benefits from successful entrepreneurs (Bazan et al., 2020 ; Khalilov & Yi, 2021 ), so, we cannot just settle for minimizing failure.

From another point of view, to evaluate the support services offered to entrepreneurs, it is necessary to know whether or not the users of these services have achieved their desired success (Kiyabo & Isaga, 2020 ). For example, in Morris et al. ( 2005 ), the success of the entrepreneur was implicitly identified with the amount of money generated. However, the intention here is not exactly to measure the performance of an entrepreneur; it implies something more subjective which cannot be measured through economic variables alone (Maehr & Sjogren, 1971 ). It does involve the level of effectiveness of the business, but not only that.

Neither the methodology nor the structure of this paper is exactly the usual in the field. As business success is a concept that comprises many definitions and interpretations, the next section of this article is a bibliographic review of the variables directly related to business success. A description is then given of a procedure which offers a composite definition of success based on decision trees, and on three focuses: positioning, expectations and evolution. Both the variables and the 29 questions necessary to classify the entrepreneurs by their level of success are explicitly provided. The validity of the model designed is checked using the data of the Andalusian entrepreneurs who are used to illustrate the model. The article ends with the presentation and discussion of the most relevant results and conclusions.

Literature review

Future entrepreneurs may or may not enjoy the support of a public institution (Alkahtani et al., 2020 ; Woods et al., 2020 ). In all events, to take decisions during the early years of a new company’s existence, it is crucial to understand, at least, the most important factors which determine survival, growth and success (Gómez-Villanueva, 2008 ). For this reason, experts have analysed the success or failure of entrepreneurs from many perspectives, for example: would the beneficiary of that success be the entrepreneur, taking into account the personal and professional satisfaction they enjoy? (Khan et al., 2021 ; Maehr & Sjogren, 1971 ) Or would it be the project itself, as a business, considering its net return? Or would it be the employees, with the professional benefits and repercussion that it produces? Or perhaps society, thanks to the economic development and the low impact on resources produced by the activity? (Bazan et al., 2020 ; Claire, 2012 ). Logically, very different indicators have also been used to measure that benefit (Harms et al., 2007 ). However, the greater part of research into this type of evaluation focuses on different ways of measuring the results in companies, without specifying the particular case of the entrepreneurs.

Thus, Gómez-Gómez et al. ( 2016 ) point to the main problems in evaluating business excellence: many dimensions are involved, and each dimension can refer to different variables, each variable can be measured on different scales and the data on different companies is rarely comparable. As if this were little enough, determining the minimum level of excellence for each dimension is a subjective process.

Perhaps the most apparently objective dimension is the survival of the company (Audretsch, 1991 ), but the relationship between survival and success should be analysed carefully (Nikolic et al., 2019 ), as should the relationship between failure and returns. Though it is true that companies with low returns tends to disappear, depending on the type of company, sector, environment and other factors, this could be understood as part of the life-cycle of the business and it can be taken as natural that the entrepreneur should go on to create another company. That is, there is no purely linear relationship between business returns and the survival of the company created: it may be better to close the company as soon as losses begin to accumulate, sell the business or even declare bankruptcy (Cefis & Marsili, 2011 ).

Taking this aspect into account, it can be seen that it is difficult to establish the exact relationship between the phenomenon studied and the dimension analysed. It is even more difficult to find relationships that remain unaltered when the context is changed. For example, newly created businesses suffer a higher percentage rate of ‘mortality’ than older companies (incidentally, this high rate of disappearance is usually associated with a deficient evaluation of the business plan in the preliminary stages of assistance). Despite this ‘failure’ and its possible personal and financial consequences, whether or not another attempt is made is a question of culture, and so the lack of survival does not always correspond with the same level of failure (Nikolic et al., 2019 ).

Many authors even distinguish between company closures due to insolvency and voluntary closures and this can also be reflected by an additional dichotomous variable. However, using official data, it is difficult to distinguish the businesses that have ceased trading from those which have failed. Headd ( 2003 ) found that 66 per cent of the businesses which closed were not successful, but the rest closed despite achieving success. The factors that were deduced to be significant for survival were similar to those found in other studies, but those identified for companies that closed (such as being a new company or not having initial capital) could lead to failure or to success despite the closure of the company.

Finally, there remains the difficulty of correctly defining each variable and for those variables to be easily measured. In the previous example, with respect to the dates of companies’ entering and leaving the market, different alternatives have been considered, in line with the reference literature and depending on whether or not there is access to data on registration or deregistration in the official companies register. The registration or deregistration of the entrepreneur or the workers is usually also considered, with the registration usually being taken as the year prior to the hiring of the first worker in the company and deregistration as the last year. Some also take this data from a sample, from panel data or from official statistics (Strotmann, 2007 ). Unfortunately, the study of the above variables differs as a result of different characteristics in the country analysed.

We shall now review the efforts to measure the success of companies to attempt to adapt them to the case of entrepreneurs. Broadly speaking, there are different statuses: failure or non-survival, marginal survival and success or high growth (Cooper et al., 1989 ). In fact, the classification proposed below in this article adopts a similar approach.

Different methodologies are available to address the problem generally and there is no consensus. Thus, Morris et al. ( 2005 ) offer groups of key questions to evaluate the success of a company: value creation, the beneficiaries of value creation, competitiveness, positioning, growth plans. Other authors select a sample of successful companies, on the basis of their returns, growth, survival, etc., and another of unsuccessful companies. They then analyse variations and correlations to determine relationships between the different variables studied (Duchesneau & Gartner, 1990 ).

In summary, although the literature contains very diverse possibilities, a concept of success is established hereafter which comprises three fundamental focuses: positioning, expectations and evolution. Each of these can be analysed (in all the levels of success or categories set out in this paper) through different variables or characteristics (some totally objective and others not so objective; some quantitative and others qualitative), as described below.

Positioning

The first focus refers to the competitive position of the entrepreneur with respect to other market agents. In fact, one of the keys to the success of the entrepreneur is the capacity to adjust their projects to adapt to the activities and meet the interests of the rest of the agents active in the same sphere (Brown et al., 2009 ).

Competitive success or business competitiveness has been defined by many authors (Camisón, 1997 ; García & Álvarez, 1996 ; Kester & Luerhrman, 1989 ; Viedma, 1992 ) as the capacity to generate sustainable competitive advantages to produce goods and services, creating value or to compete with rival companies for the same market niche. Each strategy involves different skills and requirements for success (Porter, 1991 ). Other definitions consider competitiveness as the capacity to achieve a favourable competitive position, in rivalry with other companies; that will lead to performance superior to that of competitors (Aragón & Rubio, 2005 ). Many authors refer here to comparisons with other companies or entrepreneurs who undertake the same type of activity. They hold that the external framework is a constant influence on all the stages of the organisational life cycle and, by extension, on the concept of success, with the moral support network (which is also closely related to the ultimate aim of this research) being the factor most highly valued by entrepreneurs (González, 2003 ).

When measuring competitiveness, the objective components of the success of an entrepreneur should be studied, that is, the consideration should be based on facts and not on the opinion of the entrepreneur. As well as the survival of the business (which has already been mentioned), competitiveness is usually measured through quantitative indicators: economic, financial, contextual and others (Amorós & Poblete, 2013 ; Garzón, 2017 ). Table 1 shows those indicators most commonly used for this purpose.

Expectations

Generically, expectation is the reasonable possibility that something should occur. Therefore, the expectations of entrepreneurs can be analysed taking into account previous results of the variables that define success in their business segment (Orozco & Arraut, 2018 ). However, the expectations of entrepreneurs vary significantly with respect to their investment intentions and desired profitability (March, 1999 ). Many authors indicate the importance of the principles of social cognitive theory when considering both expectations of success and the perception of success itself, not just as elements which propitiate the acceleration of internal learning systems, but also for the achievement of success (Shin & Kim, 2019 ). This focus is supported by different authors, with this initial variable being that which helps to define the future project. For this reason, the decision whether or not to invest in a business can be predicted on the basis of its expectation of survival and future returns, due partly to the perception of the risk assumed by the entrepreneurs (Su & Wang, 2018 ). Logically, the role of public support will be different, depending on this perception and on the strategy taken by the entrepreneur as a follower or pioneer in the market (Shepherd, 1999 ).

To correctly understand the expectations and variables that should be taken into account so as to evaluate them, it is useful also to study the motivation of the entrepreneurs. The most frequent motivation is achievement, which coincides with the tendency to seek success in tasks which involve the evaluation of performance. The second motivation is power, which characterises the relationship between two persons in which one exercises control over the behaviour of the other. The third motivation is affiliation, which is defined as interest in establishing, maintaining or restoring a positive affective relationship with one or more persons (Montañés, 2002 ). It is clear that the expectations of entrepreneurs are particularly complex to evaluate, due to their subjectivity. They are usually measured in terms of the indicators included in Table 2 .

To complement the two above focuses, the trends and perspectives of the environment and the sector of the entrepreneur’s activity must be considered. This evolution of the corresponding variables is considered both from the point of view of economic indicators (economic and financial profitability, growth in turnover and employees), and of contextual indicators (such as the average probability of survival of companies in the segment, business dynamism and its concentration). Consequently, the indicators in Table 3 should be taken into account.

Note that the evolution of the business segment is usually measured through the average growth in economic profitability and financial profitability from the beginning of activity by companies in the same business segment, while the evolution of economic indicators in the environment refers to the probability of survival in the environment, the dynamism of the business segment, the concentration of the business segment, a summary indicator of the environment, etc.

Research methodology

The three focuses described above are fundamental to determine a set of variables related to business success which help to classify entrepreneurs into three key groups or categories (separate groups, as defined in the Introduction): success, survival and failure.

To date, attempts to decide whether success has been achieved in a specific case of entrepreneurship on the basis of a single indicator have failed. Does this mean that success cannot be defined quantitatively? The solution offered to this problem consists of a somewhat more complex procedure than the calculation of an indicator, as it incorporates qualitative variables and takes into account a certain degree of subjectivity.

In this paper we only use variables which are related to success, not those which justify it. That is, we try to identify which questions are the best to find out whether an entrepreneur has been successful or not. Using a decision-tree technique, the optimal questions are recursively selected to divide the data set according to the previously chosen characteristics, so that each data subset has the best classification process. Once the questions with which to define success (or survival or failure) have been determined, this definition could be used to design prediction models (from a priori variables).

To illustrate and check the model proposed to evaluate success, a data set was used relating to Andalusia, a region in the south of Spain with high levels of unemployment and where support for entrepreneurship is on the agenda of all of the politicians aiming to palliate the difficult situation, especially in the case of youth unemployment.

Specifically, information was obtained on 5341 entrepreneurs who had received advice or support since 2010 from the ‘Andalucía-Emprende’ support service. The support service collected information from all the entrepreneurs, since their consent for the processing of personal data for research is a condition of participation in the support program. In fact, entrepreneurs are willing to collaborate with Andalucía-Emprende motivated by the annual prizes awarded to the best entrepreneurs in the region. Hence, multiple variables are known for each of these entrepreneurs, relating to data on the company creation process and the monitoring that was carried out. In order for the information to incorporate aspects of positioning, expectations and evolution, of both the entrepreneurs and the context in their sectors, it was decided to complement the variables from the company database (the Iberian Balance System, ‘Sistema de Balances Ibéricos’, a database of financial information on the balance sheets of Spanish companies) with those of a geographical database (the Andalusian Municipal Information System, ‘Sistema de Información Municipal Andaluz’). There exists another element which justifies resort to other sources to evaluate SMEs and micro-SMEs: it is recommended the use of the subjective variables regarding performance and environment (Covin & Slevin, 1991 ); they better reflect those intangible factors that affect the early years of activity.

After analysing the large database and eliminating inconsistent information, the set of variables (described in the following section) and their corresponding useful levels to guarantee success (and survival or failure) were decided. Finally, all of the variables were integrated into a series of questions, and the final set of entrepreneurs consisted of 2221 individual cases. An in-depth analysis of this data set and the variables included can be found in Chaves et al. ( 2018 ).

Due to the great complexity and large number of tangible and intangible factors affecting success, depicted by both qualitative and quantitative variables, it was decided to use a multidimensional definition based on the three focuses defined above (expectations, positioning and evolution). Overall, these focuses serve to classify the variables into three groups which will be indicated by the first of two numbers in square brackets. These digits are used to design the variables finally included in the study.

From the articles referred to above (Peters & Waterman, 1982 ; Schmalensee, 1985 ; Covin & Slevin, 1990 ; McGahan, 1990 ; Audretsch, 1991 ; Rumelt, 1991 ; García & Álvarez, 1996 ; Camisón, 1997 , 1999 , 2001 ; Pelham, 1997 , 2000 ; Galán & Vecino, 1997 ; McGahan & Porter, 1997 ; Gadenne, 1998 ; Mauri & Michaels, 1998 ; Donrrosoro et al., 2001 ; Van Praag & Versloot, 2008 ; Ireland et al., 2009 ), it can be deduced that survival and economic profitability are the indicators most frequently used to measure success. They are, therefore, included here as fundamental elements. Other less commonly used indicators were considered to measure the situation of the sector and the geographical environment, where the activity took place. Finally, with the available information, summary indicators were constructed in order, for example, to ascertain whether the economic figures of the entrepreneur were favourable with respect to the mean value for companies in the business sector (the median value, for instance, is not available in the databases which have been accessed). The variables used are described in Appendix .

The proposed model attempts to identify the ideal questions to classify entrepreneurs. These questions are compatible with the existing literature and with the variables listed in Appendix . Andalucía-Emprende carries out a follow-up survey to all the entrepreneurs 2 years after the beginning of their activity. Hence, the answers to almost all the questions in our set come from this survey and refer to the end of the second year of each project. This being the case, we set that moment as the most appropriate to evaluate whether they have been successful or not. However, we are about to see that questions Q 1 and Q 3 refer specifically to survival during the first years, with special attention to what happens at the end of the first year, as will be explained later.

The process to determine the following minimal set of questions from the variables labelled above is based on the C4.5 algorithm developed by Quinlan ( 1993 ), which in turn is an extension of the ID3 algorithm: at each step, one chooses the question that minimizes the diversity (or entropy) of the resulting subsets (considering the three classes “success”, “survival”, and “failure”). The algorithm usually guarantees only a local minimum of the number of nodes. Globality is achieved through the heuristic pruning process through backtracking which, in this case, was carried out by hand. It was possible to carry out the calculations by hand thanks to the fact that the questions are “discrete” and this fact simplifies their execution, but it would be much more complex in the case of “continuous” questions. The procedure is straightforward but too long to be described here. Footnote 1 The risk of suffering the effects of overtraining is small, since entrepreneurs of almost all possible combinations of characteristics were classified (there are some impossible combinations). The final set of questions is listed below.

Q 1 : Was the entrepreneur surviving more than 1 year? Q 2 : Was the entrepreneur surviving after 2 years? Q 3 : Did the entrepreneur survive the whole first year? Q 4 : Were the objectives for which the entrepreneur created the company achieved? Q 5 : Is the turnover positive? Q 6 : Are the results positive? Q 7 : Are there zero operating results? Q 8 : Is external funding greater than 75 per cent? Q 9 : Is the economic profitability greater than the mean economic profitability of companies in the business segment with the same CNAE? Q 10 : Is the economic profitability greater than 10 per cent? Q 11 : Is the financial profitability greater than the mean for companies in the same business sector with the same CNAE? Q 12 : Is the financial profitability greater than 10 per cent? Q 13 : Does the entrepreneur have positive expectations of the future of the project? Q 14 : Is labour productivity greater than the minimum annual wage? Q 15 : Are the satisfaction indicators positive? Q 16 : Has the entrepreneur hired more employees since the commencement of activity? Q 17 : Is there a positive evolution? Q 18 : Are there positive indicators in the local environment, where the activity takes place? Q 19 : Are there positive expectations in the business context in which the activity takes place? Q 20 : Has the business segment of the entrepreneur grown since the activity commenced? Q 21 : Has there been a positive evolution since commencement and are current expectations positive? Q 22 : Has the environment seen positive expectations and growth since the commencement of the activity? Q 23 : Has the entrepreneur significantly increased investment since commencement of the activity? Q 24 : Is relative indebtedness greater than the mean value for companies in the same business sector? Q 25 : Is asset turnover greater than the mean value for companies in the same business sector? Q 26 : Is investment per employee greater than the annual minimum wage? Q 27 : Are there positive perspectives for the business of the entrepreneur and the possibility of increasing the number of employees in the following year? Q 28 : Is the monthly turnover per employee greater than the monthly minimum wage? Q 29 : Are the evolution indicators positive?

Results and discussion

The application of the method to the sample of 2221 Andalusian entrepreneurs revealed the 218 different cases shown in Table 4 . These cases constitute the possible scenarios obtained when applying decision-tree techniques to the data set.

Decision trees constitute a flexible and versatile methodology in different areas of knowledge. One could refer to different types of decision trees, depending on the purposes for which they are used: (i) to classify a data set; (ii) to ease some regression models; (iii) to solve optimization problems; (iv) to display an algorithm; (v) to choose the best strategy, as in Game Theory; etc. For instance, Chaves et al. ( 2018 ) use a decision tree to find out what factors (or a priori characteristics) would best serve to predict entrepreneurial survival; they could not predict success, because they did not have an appropriate definition of such concept.

The common feature of all decision trees is that the underlying topological graph has no cycles. On the contrary, there are crucial questions in some trees (such as the search for optimal paths, the determination of errors or the cross-validation) that have no interest in others. In our case, decision trees solve the problem of finding a reduced set of questions (or nodes) to classify a complex set of data. To do this, we use a variant of the C4.5 algorithm (Quinlan, 1993 ). Without decision trees, it would have been practically impossible to optimally organize the most efficient variables to classify individuals. Note that the starting point was a set of hundreds of variables that characterized several thousand individuals. The challenge was to obtain questions that were answered with the previously collected variables and that allowed to classify the individuals according to the level of success each entrepreneur presented. Once applied the decision-tree technique, the classification proposed for each case coincides with the perceptions of experts and support services. Later we will highlight the importance of this fact.

Regarding the ease of use of the definition, the maximum number of questions necessary to classify an entrepreneur was 12, and the appropriate set of questions valid to classify each entrepreneur can be extracted from Table 4 . In the case study, 130 cases of success were classified (5.85% of the total), 1002 of survival (45.11%), 1058 of failure (47.64%) and 31 could not be classified as a result of a lack of sufficient information (1.40%). The three most frequent combinations in the database were:

#90 (301 entrepreneurs, 14.12% of the database): Q 1 yes; Q 2 no; failure;

#11 (236 entrepreneurs, 11.07% of the database): Q 1 yes; Q 2 yes; Q 4 do not know/no answer; Q 5 yes; Q 6 yes; Q 9 no; Q 10 yes; Q 12 yes; Q 13 yes; Q 14 no; survival;

#9 (220 entrepreneurs, 10.32% of the database): Q 1 yes; Q 2 yes; Q 4 do not know/no answer; Q 5 yes; Q 6 yes; Q 9 yes; Q 11 no; survival.

The first (and most frequent) type of companies have failed, because they have not reached the 2-year border, unlike the second group that have had a positive performance, have achieved their objectives, and profitability has been higher than the rest of the companies in their business sector with the same CNAE. Finally, we highlight a third group that despite having exceeded 2 years, having positive performance and results, does not answer whether they achieved the objectives that were set and does not have a financial profitability above those of its own CNAE.

Reviewing cases such as these can help us reach an idea of whether the new definition makes sense. In fact, checking that the definition is reasonable or consistent would be a first step to validate it. As the tree outputs were all checked by experts, the credibility of the definition is high. Furthermore, the definition of success does not seem to depend on the characteristics that affect its achievement (such as characteristics of the entrepreneur or the project).

Second, we can ask whether the definition is generalizable. The answer is affirmative, but this does not mean that exactly the same table can be applied to evaluate success in another region, different from Andalusia. There are some aspects that should be adapted. For instance, it may affect the idiosyncrasy of the Spanish entrepreneur, who rarely considers that there can be something positive without survival. In any country, an important group of entrepreneurs consider survival as their principal objective of existence, hence it is a “moderate success” for them to maintain their activity. However, outside of Spain, it is more common to find entrepreneurs who value their learning as another step on their way to success (Soto-Simeone et al., 2021 ).

Another characteristic that may differ from another case study is the relevance of the first year after the start of the project. In Andalusia, the public support for entrepreneurs lasts exactly 1 year: the free assignment of a space, the training they receive, the web hosting, the participation in fairs and activities, etc. In other words, the first commitment of both parties (Andalucía-Emprende and each entrepreneur) lasts 1 year and it is quite common for the company to be extinguished at the end of that year. For this reason, a project that lasts more than 365 days differs from another that lasts exactly only 1 year.

Despite the above differences, the greatest difficulty in applying the definition in another region is probably the lack of a sufficiently complete and reliable data set. According to the interviews maintained by the authors and by the specialized publications reviewed, public entrepreneur support services have a lot of interesting data, but they rarely make use of it in a systematic way. Not using that information is probably one of their weakest points.

From now on, let us focus on the positive side: the flexibility in the use of the paper in other contexts. To do that, we will start with the variables used, which are common in any entrepreneur support service (or some equivalent variables). Quantitative and, above all, qualitative variables are used in the procedure, which facilitates its application. Besides, algorithm C4.5 was used instead of ID3, because it allows the presence of missing data (Quinlan, 1993 ).

The next aspect to consider is whether the definition is really useful. Entrepreneurship is not only essential to reduce unemployment, but also to improve the competitiveness of economy (Al Mamun & Fazal, 2018 ; Spulber, 2014 ). After the latest economic crisis, growing agents of change like entrepreneurs are more amenable to new opportunities and challenges. We hold that our definition of success (and survival and failure) serves a dual purpose in improving entrepreneurship.

On one hand, it is necessary to define success to evaluate the situation of entrepreneurs and the support policies that they can benefit from. On the other hand, the establishment of an objective variable makes it possible to apply optimization, inference and simulation techniques with which to analyze the relevant factors to increase the success of entrepreneurs. Specifically, the results of this paper serve to improve those of other research works that sought to modernize support services for entrepreneurs.

Chaves et al. ( 2018 ) presented several factors that are important for survival in entrepreneurship: the total number of support services in the first months, passing a previous pre-incubation process, being present in an application for incentives or employment plan, as well as others such as the type of company or the geographic location. The forecast was acceptable for the entrepreneurs who survived; not so much for the others. Furthermore, only survival was analyzed and not success, because there was no appropriate definition of this last concept.

Chaves and Fedriani ( 2020 ) showed that a computer program can correctly guide an entrepreneur or, at least, help in this task to a support service for entrepreneurs. To do this, they developed a way to predict the success or survival of entrepreneurs based on the information available before starting the business venture. The system was based on Artificial Intelligence (specifically, a self-organizing map and a multi-layer perceptron), and they consider objective variables compatible with the definitions explained in the present paper. The self-organizing map detected the following variables as the ones which affect the prediction highly: local productive environment; employment plan; type of business accommodation; financial profitability of the environment; supporting services; economic indicators of the environment; activity of the company; investment; financing; educational level; legal form; employees. According to the multilayer-perceptron, the more influential variables were: the sector; the probability of survival of the environment; the number of employees; the province; and the number of support services. Summarizing, a replicable entrepreneurial success prediction model was built, and it determined a different effect of certain actions in the counselling depending on the characteristics of the entrepreneur. However, artificial intelligence only works if provided with a proper definition of success; the same is true whatever inference technique you try to use for the same purpose.

Therefore, we hold that this paper will serve as the basis for the design of a scorecard that will make it possible to determine and monitor the evolution of the supported entrepreneurs, but also to improve the corresponding counselling process. For future research, we propose to analyse the relationship between survival and long-term success of the business project, since the definition of success in this paper uses information exclusively from the first 5 years of life of the entrepreneurial projects.

Conclusions

Much has been written on the evaluation of business performance. This article has attempted to adapt the most commonly used indicators to the specific case of entrepreneurship, which has been studied much less (Choi & Williams, 2016 ). The proposed definition of success is not the only one possible, but it is consistent, generalizable, and useful. Therefore, we can affirm that this paper provides a tool (based on the decision trees) which can serve to analyse the factors for success and, even, to estimate the probability of success. It would be reasonable, on the basis of the formulae for calculating this probability, to consider in the future a more precise definition of success for each entrepreneur, and this would facilitate the evaluation of measures to encourage entrepreneurship. This, in turn, would serve to improve systems to support entrepreneurs, an ultimate aim of undoubted economic and business interest.

The proposed definition of business success has been designed on the basis of all the variables introduced above (in Sect. 3.2), through a decision tree (or expert system), rooted in the bibliography consulted and expert recommendations, allowing a measurement of success to be established that is more precise than those proposed by other authors, especially for the type of entrepreneur studied in this research. In the case study, over 98 per cent of the entrepreneurs, (all those who were classifiable) were correctly classified with the combination of questions used, in accordance with the available information (from the public support service ‘Andalucía-Emprende’). The method has been generalised so that it also allows the classification of other hypothetical cases (with combinations of values not found in the sample). It has not yet been checked whether the methodology can be adapted to evaluate other types of company or to study entrepreneurs when the available information is not so complete.

A significant proportion of the papers consulted to estimate business success are based on accounting information available from the registry, taking into account all of the legal considerations and recommendations for the calculation of indicators. This is due to the fact that companies are often reluctant to provide data (Covin & Slevin, 1990 ; Pelham, 1997 ) and in many cases, the data is not entirely reliable. In the case study presented here, the data comes from an official, public source. Nevertheless, a possible response to each question is ‘data not available’, which demonstrates that the method can classify correctly despite abundant missing data.

In the event of replicating this study for another region, the greatest difficulty would be to obtain such a comprehensive and reliable database. Despite the little consideration given to business failure in Andalusia, most of the questions considered in this paper can be used to correctly classify entrepreneurs in any other country with a public service to support entrepreneurs.

The use of Artificial Intelligence is what makes it possible to consider so many variables. In turn, the use of such a complete database allows the achievement of reliable results, even when the information is incomplete or imprecise. This opens a range of possibilities for future research in the field of entrepreneurship and the evaluation of business success.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available due individual privacy, but are available from the corresponding author on reasonable request.

A detailed description of this process can be requested from the authors.

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Acknowledgements

We thank the anonymous Reviewer #1 whose comments helped improve and clarify this manuscript.

This research was supported by Pablo de Olavide University (Spain) and data was collected from Andalucía Emprende Fundation that participates in the European Erasmus for Young Entrepreneurs project through the project called MOVE YE.

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EF mainly contributed to the study’s conception and design. The preparation of the materials, data collection and analysis were performed by MC. The first draft of the manuscript was written by MC and corrected by EF. Both authors read and approved the final manuscript.

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M.C.-M. is PhD in Economy (2020, Pablo de Olavide University, Spain); Associate Professor at Pablo de Olavide University (Spain) in the Department of Economics, Quantitative Methods and Economic History. His research interests include entrepreneurial orientation and business assessment. 

E.M.F. is PhD in Mathematics (2001, University of Seville, Spain); Full Professor at Pablo de Olavide University (Spain) in the Department of Economics, Quantitative Methods and Economic History. His research interests include Lie algebras, Graph Theory and the application of mathematical methods to Economics and Business.

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Correspondence to Eugenio M. Fedriani .

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Variables considered as indicators for entrepreneurial success

Survival of the business [1.1]: the action and effect of a company surviving, depending on whether or not it is registered, with regard to the legal and administrative procedures necessary, after a given period of time following its creation. In the case of entrepreneurs, this variable has a very strong inverse correlation with failure.

Turnover [1.2]: the volume of the turnover produced by the exchange of goods and services by the company (measured in euros).

Results [1.3]: this represents the amount of money that remains in the company when total expenditure on operations is deducted from total revenue from the company’s operating activities (after interest and taxes).

Relative economic profitability [1.4]: a comparison of net earnings and total assets, with respect to the mean value for companies in the business segment with the same National Economic Activity Code (‘ Código Nacional de Actividades Económicas ’, or CNAE).

Relative financial profitability [1.5]: a comparison of net earnings (or profits or losses after interest and taxes) and stockholders’ equity, with respect to the mean value for companies of the sector with the same CNAE.

Productivity [1.6]: operating earnings divided by the number of employees.

Investment/employment [1.7]: the volume of investment per employee.

External funding [1.8]: the percentage of external funding with respect to total company capital.

Competitive position of the company [1.9]: this is a multidimensional or vector indicator which compares the indicators of the company with the mean values for companies in its business sector with the same CNAE regarding: economic profitability, financial profitability, asset turnover and indebtedness.

Contextual expectations [2.1]: the coefficient of the values of the entrepreneur and those of the context with respect to economic profitability, financial profitability and asset turnover.

Business and employment perspectives for the following year [2.2]: subjective opinion of the entrepreneur regarding the prospects of the business and the capacity to hire more employees in the following year.

Achievement of the objectives laid down [2.3]: this is the subjective assessment by the entrepreneur of the achievement of the objectives set at the beginning of the business activity.

Satisfaction indicators [2.4]: this measures the satisfaction of the different stakeholders related to the company (employees, customers, owner of the company; for the latter, in terms of the results, growth in sales and growth of the workforce).

Increase in investment [3.1]: a dichotomous variable that indicates whether the activity has required additional investment over and above the initial investment.

Increase in the number of employees [3.2]: this variable indicates whether the company has more employees than at the commencement of its activity.

Evolution of the business segment [3.3]: the growth in the main indicators, economic and financial profitability, in the business segment with the same CNAE as the entrepreneur.

Evolution of the growth in turnover and employees [3.4]: increase in the turnover and employee variables since the commencement of the activity up to the present. This is, in fact, a vector variable with two components, the first with three values (‘increased’, ‘unchanged’ or ‘reduced’) and the second with four (the fourth being ‘did not have workers under contract’).

Expectations of the sector [3.5]: a qualitative indicator that summarises the components of indicators 1.9; in the case that the components are all favourable, its value is ‘positive’; otherwise, it is ‘negative’.

Evolution and expectations of the business segment: a qualitative indicator that summarises indicators 2.1 and 3.3; if the indicators are all favourable, its value is ‘positive’; otherwise, it is ‘negative’.

Economic indicators of the environment [3.6]: representative values of the municipality, where the activity of the entrepreneur takes place, with respect to the probability of survival in that environment, the dynamism of the business segment and the concentration of the business segment.

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Chaves-Maza, M., Fedriani, E.M. Defining entrepreneurial success to improve guidance services: a study with a comprehensive database from Andalusia. J Innov Entrep 11 , 22 (2022). https://doi.org/10.1186/s13731-022-00213-8

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• Entrepreneurship
• Achievement
• Decision trees
• Classification

An empirical study on entrepreneurial traits and their impact on enterprise success

Vilakshan - XIMB Journal of Management

ISSN : 0973-1954

Article publication date: 27 December 2021

Issue publication date: 31 July 2023

Enterprise success is driven by enterprise actions, which, in turn, is influenced by entrepreneurial behaviours. Behaviours are guided by traits. Hence, it is highly likely that personality traits of entrepreneur are critical to enterprise success. This paper aims at finding the relationship between entrepreneurial traits and enterprise success, identify underlying construct and examine how successful and unsuccessful entrepreneurs differ across traits. It also attempts enterprise profiling based on these traits and test predictive validity of entrepreneurial traits on enterprise success.

Design/methodology/approach

In this study, 396 micro, small and medium enterprises comprising both successful and unsuccessful ones are studied together across 11 personality traits. Data was analysed using various statistical techniques like co-relation, t -test, factor analysis, cluster analysis and regression to test hypothesis and arrive at given findings.

This study finds there is strong positive co-relations between traits and enterprise success. It establishes that successful and unsuccessful enterprises display distinct traits and significantly differ from each other. Entrepreneurial traits affect enterprise success, and the former has significant predictive value on the later (R-squared = 0.866).

Practical implications

The findings have implications to entrepreneurs in relation to enriching the existing traits and inculcating new ones. Financial institutions like banks can peruse the findings and include traits and behavioural aspects in borrower selection, credit appraisal, evaluation and credit decisioning, to make it more holistic. It also generates scope for further academic research.

Originality/value

This study contributes to existing literature and validates existing findings. It also finds that traits are contagious in nature, together of which can be grouped to build an entrepreneurs’ traits index which exerts strong influence on enterprise success.

• Personality traits
• Success factors
• Small business
• Behavioural traits
• Enterprise success

Pattanayak, S. and Kakati, M. (2023), "An empirical study on entrepreneurial traits and their impact on enterprise success", Vilakshan - XIMB Journal of Management , Vol. 20 No. 2, pp. 277-291. https://doi.org/10.1108/XJM-09-2021-0249

Emerald Publishing Limited

Copyright © 2021, Sthitaprajnya Pattanayak and Munindra Kakati.

Published in Vilakshan – XIMB Journal of Management . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Enterprise studies have emerged interdisciplinary, encompassing diversified branches of social sciences like economics, commerce, management and sociology. However, psychological approach to the discipline was lagging until 1990s. This was possibly because, researchers then believed, personality structure has less to do with business motives of entrepreneur, which was otherwise perceived to be predominately identified as maximising profit. Hence, it was believed that economic theories are only capable of explaining enterprise position ( Brandstätter, 1997 ). Another possible reason might have been that, personality studies of the time were inconclusive due to confusing personality variables, unknown reliability and lack of theoretical justifications ( Chandler and Lyon, 2001 ; Gartner, 1988 ). Therefore, some of the researchers even argued to the extent of abandoning future researches using traits paradigm ( Chell, 1985 ; Gartner, 1988 ; Robinson et al. , 1991 ).

However, renewed interests in the area were observed after Costa and McCrae (1992) came up with famous five factor model (FFM) of personality traits. FFM organised a vast varieties of traits into a small groups five meaningful constructs, read in acronym of OCEAN (Openness to experience, Conscientiousness, Extraversion, Agreeableness and Neuroticism). This explained broad human behaviours. It also helped researchers to analyse traits with respect to enterprise studies and derive meaningful relationships ( Zhao and Seibert, 2006 ). Since then, there are numerous studies, which examined personality traits and entrepreneurship. However, most of them picked individual trait and analysed its impact on either success or failure. There are few studies, which, considered a set of traits and studied their impact on a mixed set of enterprises simultaneously to understand their relation, differentiating capabilities and predictive validity. The present study attempts to understand this using various statistical tools.

2. Objective of study

This study relates to micro, small and medium enterprises (MSMEs). MSMEs are important for economy but fragile in nature and vulnerable to quick failures. Beaver (2002) found, if anything is constant, it is the very high rates of failures among the small and young firms. While many enterprises succumb to failures, some of them survive and achieve phenomenal success. Beaver (2002) further argues, every business start-up is a unique event. Success factors and the circumstances to success are intangible and vary from entrepreneur to entrepreneur. It is difficult to identify because individual traits do not lend itself easily to measurability, replication and generalizability ( Covin and Slevin, 1991 ). Researchers like, Caliendo et al. (2011) believed, intersection of psychology and economics holds strong prospects for conducting entrepreneurship research.

The study becomes more pertinent in the wake of substantial thrust on make-in-India concept, which rides significantly on the back of MSME performances. In India, MSMEs contribute about 45% of the total manufacturing output, 40% of the total exports and around 8% of the country’s gross domestic product. They are the second largest employment generators after agriculture, providing employment to nearly 50 million people. There are about 63 million MSMEs, which account for nearly 90% of industrial units (Source: www.makeinindia.com ). Hence, the knowledge of effective entrepreneurial traits for enterprise success, which this study attempts to bring, becomes subject of further vital interest.

personality traits and their relationship with enterprise success;

if there exists any difference between successful entrepreneurs and not-so-successful ones in terms of personality traits;

do these traits measure any underlying construct for successful and unsuccessful enterprises?; and

do these traits have discriminating capability for grouping successful and not-so-successful enterprises? Do successful entrepreneurs pose similar traits and so do the unsuccessful ones?

3. Literature review and conceptual framework

3.1 personality traits.

Personality traits are set of stable pattern of behaviours through which a person can be described and identified with. This includes his thoughts and emotions, which influence the behaviours ( McCrae and Costa, 2003 ). Earliest definitions of entrepreneur also identified it through traits. In 1755, Frenchman Cantillon defined entrepreneur as an individual with “foresight and ingenuity,” who is eager to “accept the uncertainty” within the framework of economic market, and actively “pursue the profit” ( Küçük, 2005 ). Similarly, Schumpeter’s (1934) defined entrepreneur as a “visionary” and an “innovator,” who goes for creative destruction of existing combinations and makes new combination by way of a new product, process or a new market.

Traits are descriptive variables, which are capable of describing how an individual think and behave ( Parks-Leduc et al. , 2014 ). Entrepreneurial behaviours are reflected in entrepreneurial actions. Bayarçelik and Özşahin (2014) termed it as entrepreneurial orientation (EO). Lumpkin and Dess (1996) called in leadership style.

Epstein and O’Brien (1985) argued, while the FFM explained aggregated constructs of human behaviour, they may not be capable of predicting specific behaviours like that of an entrepreneur. Hence, it is likely that predictive validity of FFM may be low in entrepreneurship research ( Rauch and Frese, 2007 ). Therefore, better proximal constructs related to the tasks of entrepreneurs should be used ( Baum and Locke, 2004 ).

Researchers like Miller (1983) and Lumpkin and Dess (1996) analysed it from the context of EO and concluded with five dimensions: pro-activeness, risk taking, innovation, autonomous and aggressive to competitor. Gull et al. (2021) concurred to this view and further extended the role of EO as enabler for global performance of firms and termed them international EO (IEO).

As such, entrepreneurial traits, behavioural aspects and leadership style are three distinct concepts. However, when it comes to enterprise study, they share certain common underlying objectives in their role as catalyst to enterprise success. Jaroliya and Gyanchandani (2021) argue that team performance is building block to achieve organisational goals and key variable for team performance is leadership style of team leaders. Performance of the group is largely reliant on authority style of the leader. When it comes to enterprise, entrepreneur is the first leader. Hence, it becomes pertinent to view the entrepreneurial traits from the lens of leadership traits also. In this regards, researchers have championed transformational leadership as tool for augmenting team performance. It is characterised by higher behavioural aspects like advanced thinking ( Aragon-Correa et al. , 2007 ), providing autonomy to employees, encouraging organisational learning and allowing creativity ( Bass and Avolio, 1980, 1997 ; Gumusluoglu and Ilsev, 2009 ; Avolio, 1999 ; Judge and Piccolo, 2004 ). In a specific study Lather et al. (2009) found, leadership style boosts motivation and effectiveness of employees and certain leadership style play a role in dispute resolution process, thereby augmenting enterprise success.

Other researchers have also given emphasis to one or other specific trait like willing to bear risk ( Say, 1971 ), ability to innovate ( Subramaniam and Youndt, 2005 ; Boz and Ergeneli, 2014 ), eagerness for independence and competitive nature ( Frese et al. , 2002 ), need for achievement, locus of control and self-efficacy ( Rauch and Frese, 2007 ; Kets de Vries, 1977 ; Schmitt-Rodermund, 2004 ; Kumbul Guler and Tinar, 2009 ).

The above literature review is perused to shortlist a set of trait variables for the present study. Details of these are given in subsequent paragraphs.

3.2 Micro small and medium enterprises

As per the latest amendment to Micro Small and Medium Enterprises Development Act 2006 in July, 2020, Government of India defines MSMEs on the basis of combination of investment in plant and machineries and turnover, which is as given in Table 1 .

Success relates to the attainment of pre-decided goals and objectives. It is a multidimensional phenomenon having different forms such as; financial or non-financial, tangible or intangible, short term or long term. Bayarçelik and Özşahin (2014) argue that organisations have grown complex in their structures. Measuring success only through financial parameters is inadequate. It needs multidimensional measurement system which includes both subjective as well as objective measures. Therefore, Varadarajan and Ramanujam (1986) and Chittithaworn et al. (2011) suggested two-dimensional scheme covering both financial and operational indicators.

For the purpose of this study, “success” is being measured as a combination of both financial and non-financial parameters. Financial parameters considered are growth in sales revenue, profitability, indebtedness, sales realisation/receivable realisation and conduct of meeting external obligations. Non-financial parameters include achievement in terms of customer satisfaction, quality standard, brand building, employee satisfaction and self-satisfaction. Respondents are asked to evaluate their business enterprise on these parameters on Likert scale of 1–5 where 1 is the lowest and 5 is highest. Those scoring 25 or more are grouped in successful group and those scoring below 25 are placed in not-successful group.

4. Research gap

After review of literatures on previous studies, we observed, following are some of the research gaps, which the present study attempts to address.

Most of previous studies analysed either a group of successful enterprises or a group of failed ones and identified traits influencing success or failure. Thus, findings from these studies came with their inbuilt limitations of generalizability, as they owed their origin either to successful group or to failed ones. Present study evaluates a set of successful and not-so-successful enterprise together across entrepreneurial traits.

Even though certain influencing traits were identified, previous studies did not test the differentiating and discriminating capability of those traits. Here, an attempt is made to test this by enterprise profiling based on differentiating and discriminating values of these traits and verify, if the traits, which worked for success were actually absent in unsuccessful ones and vice versa.

Because most of previous studies focussed on either successful or failed group, the samples were picked up accordingly with prior information. Either all known successful samples or all known failed ones were studied. This often involved element of preferential biases, though unintended. All traits observed in successful ones were perceived to be good even if some of them might not be good and everything in unsuccessful ones were suspected to be bad even if some were not really bad. This study has attempted to eliminate this bias by taking combined group of enterprises and common set of traits.

5. Research methodology

5.1 methodology.

The present study is a descriptive research using primary data collected through self-administered survey questionnaire and discussion. The subjects of study are MSME enterprises of Assam in North East of India. The region was selected for it offered mixed scope of challenges and opportunities. Challenges are geographical difficulties, connectivity issues, socio-political issues and limited resource availabilities. Opportunities are like strategic geo-position, Assam being gateway to entire North East, link to rest of India and other Eastern neighbouring countries (like Nepal, Bhutan and Bangladesh). Therefore, maximum development focus of Government and upbeat industrial activities are witnessed in the region in last decades.

As per Annual Report of Ministry of MSME, Government of India for Financial Year, 2020, there are 12.14 Lacs MSMEs in Assam, who constitute the population for this study. Out of this 396 samples were drawn which was well above the minimum sample size of 384 numbers, as verified from two separate sample adequacy calculators: Raosoft Sample Size Calculator ( http://www.raosoft.com/samplesize.html ) and Creative Research System ( https://www.surveysystem.com/sscalc.htm ). A total of 550 samples were surveyed with self-administered structured questionnaire. The data collection period was from 2018 to 2020. Out of this, responses were received from 435 respondents. However, 39 responses were either incomplete or valid response was not there. Hence, finally 396 valid responses were received which is 72% and considered good.

5.2 Personality traits considered for present research

After going through the previous researches, related literature reviews and interactions with entrepreneurs, 11 items of personality traits were shortlisted as given in Table 2 . Responses were obtained on these variables and the respondents were asked to score on a Likert scale of 1–5 representing strongly disagree to strongly agree, and this was followed by a discussion with the entrepreneurs

5.3 Questionnaire design and reliability of the instrument

The responses were obtained on a structured questionnaire, carrying three parts. Part A with eight items measured demographic variables, Part B contained 11 behavioral trait items as above and Part C measured success level of the enterprise through ten items. This included five financial items (like revenue, profit, indebtedness and sales/receivable realisations) and five non-financial items (like conduct of account, quality standard, brand building, level of customer satisfaction and self- satisfaction). Items are scored on Likert scale of 1–5. It has total peak score of 50. Enterprises, which scored 25 and above, are grouped as successful enterprise and those who scored below 25 are group as not-so-successful.

Reliability of the instrument was evaluated using the Cronbach’s alpha (α) in SPSS. Alpha value of traits items and success parameters were 0.952 and 0.970 which is above 0.70 and considered good reliability ( Zikmund et al. , 2017 ).

6. Data analysis, testing of hypothesis and discussion of results

6.1 relationship between personality traits and success.

There is significant relationship between personality traits and success of an enterprise

The result suggests, there is strong positive co-relation between personality traits and success of an enterprise, ρ (396) = 0.911, p < 0.000, hence the hypothesis was accepted ( Table 3 ).

6.2 In terms of personality traits, do successful entrepreneurs differ from not-successful ones?

There is significant difference in the personality traits of successful entrepreneurs from not-successful entrepreneurs.

An independent t -test was conducted to compare successful group ( N = 207, M = 43.03 and SD = 3.900) and not-successful ones ( N = 189, M = 25.93 and SD = 4.864). Homoscedasticity condition was not met as Levene’s test for equality of variances has come significant ( p = 0.012  < 0.05). This is practical in a real world (Zikmund et al. , 2017). There was a significant difference in the scores of successful group and the not-successful ones: t = 38.374, p = 0.000. Effect size calculated using Cohen’s D test found large effect d = 3.878 > 0.8.

These results suggest that successful enterprises differ significantly from the non-successful ones in terms of the given set of personality traits ( Tables 4 and 5 ).

6.3 Identifying the personality factor(s)

6.3.1 factor analysis..

Exploratory factor analysis was conducted over 11 trait items to understand underlying constructs within trait variables. As submitted above, in the research instrument, data in Part A measured demographic variables and data in Part C measured success level to group the samples. Hence, variables in Part A and Part C are not suitable for factor analysis as they are not going to give any meaningful conclusion, and they are not considered for factor analysis. Only the data in Part B, i.e. the 11 items of trait variables are considered for factor analysis to arrive at meaningful conclusion of identifying underlying construct(s). Keiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.954, which is above the middle level of 0.5 and close to meritorious. KMO measures the quality of co-relations among variables to find suitability for a factor analysis. It compares the zero-order co-relations among the items with the partial co-relation among them. The score ranges from 0 and 1. A score closure to 1 indicates strong co-relation among the items and small partial co-relation, whereas score closure to 0 indicates weak co-relation and higher partial co-relation. In the present case, a KMO of 0.954 indicates strong co-relation among items. As can be seen from the subsequent analysis wherein an inter-item co-relations test is run among the 11 trait items ( Table 8 ) and found that there is strong and positive co-relation among the trait items. A high KMO score backed by high inter-item co-relations supports and substantiates each other. At the same time, it also establishes that the test is appropriate. Further, Bartlett’s test of sphericity was significant ( χ 2 (55) = 3,736.181, p = 0.000) which justifies appropriateness of EFA. Orthogonal rotation with Varimax option and Kaiser normalization was accepted to find factor loading ( Table 6 ).

The test result shows, all 11 items personality traits were loaded on one factor with eigenvalue > 1, which explains cumulative variance of 68.78% and considered a good loading (cumulative variance explained is between 40% and 70% it is considered satisfactory. In case it is below 40%, it is poor and above 70% is rare) ( Table 7 ).

6.3.2 Labelling the factor: Entrepreneurs’ Rraits Index.

All 11 items of personality traits have been loaded onto one factor. This may be so because the traits seem to be strongly co-related with each other, hence contagious in nature. Therefore, a successful entrepreneur possessing one trait, is in all likelihood also possess other traits. It is like, the one, who is risk taking is also decisive and pro-active and by all chance accept changes and innovative. Reverse is true for the unsuccessful set of entrepreneurs which are typically identified with traits like risk averseness, indecisive, resistance to change and so on. Hence, we name this single factor as Entrepreneurs’ Traits Index (ETI). This can be presented in a diagram as below ( Figure 1 ).

To validate this, a co-relation test was run taking all 11 traits. Table 8 shows co-relation co-efficient among variables are positive and strong; hence, they are contagious and grouped under one factor.

6.4 Cluster analysis – enterprise profiling based on traits

To find how the entire group of successful and not-successful enterprises align themselves across these personality traits, cluster analysis technique was used. To determine optimum number of clusters, hierarchical agglomerative method with Ward’s minimum variance was used. After looking into all solutions, finally two clusters were identified. The following table details the output of cluster analysis along with mean of cluster scores. Interpretation of the clusters was done keeping the criteria that variable wise group mean which is differentiated from the global mean by 0.5 standard deviation or more calculated from the raw, untransformed data (as suggested by Birley and Westhead, 1990 )

Tables 9 and 10 show the distribution between the clusters across the traits.

Cluster 1 represents a group of 218 enterprises out of which 201 (92%) were successful and 17 (8%) were not successful. This group displayed high mean score across all 11 items of personality traits.

Cluster 2 consists of 178 enterprises out of which 172 (97%) failed and only six (3%) are successful. Members of this clusters scored lower than global mean across all parameters.

The cluster analysis further reinforced the findings of factor analysis. Cluster 1 housed pre-dominantly successful enterprises (92%) with cluster mean higher than the global mean across all trait items. This implies that successful entrepreneurs displayed all traits in ETI while practicing the managerial tasks. However, small numbers of failed enterprises (17 nos., i.e. 8%) in Cluster 1 also possessed similar personality traits but failed. This may be because of some other unidentified reasons. But the analysis establishes that with right attributes in place chances of success may be substantially high.

Cluster 2 housed 97% of the unsuccessful enterprises who scored substantially lower than the mean across all traits. This shows that the failed entrepreneurs lacked these leadership traits. Only 3% from Cluster 2 succeeded even though they lacked all traits. This can be treated as exceptions or success attributed to chance factors which may not sustain. This indicates that chances of success are low when entrepreneur lacks positive personality traits ( Tables 9 and 10 ).

6.5 Regression analysis

To ascertain, if personality traits could explain success, a simple linear regression was conducted. The predictor was the aggregate of all trait scores and the outcome was aggregate of success parameters. The predictor variable was found to be statistically significant (B = 0.930, p = 0.000), indicating that for every one unit increase in independent variable (trait scores) the dependent variable (outcome) changed (+) 0.930 unit. The model explained approximately 86.60% of the variability (R-squared = 0.866) ( Tables 11 and 12 ).

7. Discussion and analysis

If the findings of all statistical tests are analysed together, it gives a strong insight into role of personality traits in enterprise success. The test results show that there is positive and strong co-relation between the two. Traits are contagious having strong co-relation within them. A successful business leader possessing one trait is highly likely to possess others as well. Together they measure single underlying construct of several positive leadership traits which can be grouped under one factor and labelled as ETI. This is further substantiated by cluster analysis. Majority of successful entrepreneurs (92%) are grouped under one cluster (Cluster 1) scoring high across all traits and the reverse in Cluster 2. This supports the findings of Beaver (2002) who argued that success factors and the circumstances to success vary from entrepreneur to entrepreneur. t -Test also supported this. Successful entrepreneurs are a different breed.

Finally, regression model underscores the importance of personality traits in explaining enterprise success. The model explains 84.3% of the success. This validates the previous findings of Miller (1983) and Lumpkin and Dess (1996) who viewed this as “entrepreneurial orientation” or that of Avolio (1999) and Judge and Piccolo (2004) who viewed these traits as “transformational leadership, and found them to be critical for enterprise success.”

8. Conclusion and implications

The study finds relevance to multiple stakeholders. It has implication to entrepreneurs. Psychological studies emphasize that traits can be developed, practised and polished. Existing behaviours can be modified and new traits can be acquired. ETI comprising all such critical traits can be perused for augmenting managerial effectiveness.

It has takeaways for financial institutions like banks, who regularly evaluate enterprises for financial assistance. The test findings underscore the fact that a holistic evaluation of an enterprise is incomplete without considering the personality trait aspects. Enterprise actions are dependent upon entrepreneur’s decisions, which in turn depend on his personality traits. Hence, along with analysis of financial aspects, behavioural aspects also need to be attended to in order to have a holistic view.

This study has potential to contribute to existing and future academic research. So far, enterprise studies focussed on aspects of the enterprise like products, process, finance, marketing, distribution and so on. Very scant focus was on psychological dimensions which is essentially behavioral traits of the entrepreneur. The findings indicate that it is a promising area of study.

However, the study has certain limitations. Personality traits are not the only factor influencing enterprise success. There are other factors like motivation, financial, management and external factors. A holistic study of traits along with other factors will help bring better insights. Bigger sample with larger geographical coverage of the study can make it broad based. The study is based on primary data. MSMEs are unorganised and hardly any published financial information is available in public domain. If they are also included, it may further improve generalizability of the findings. Further, if personality traits are analysed along with other success factors, it will bring understanding of direct and indirect role personality traits on enterprise success that may be the potential area for future research work.

Entrepreneurs’ traits index

Revised MSME classification

Items measuring personality traits

Spearman’s rank correlations “personality traits and success”

Group statistics (personality traits)

Independent samples test

KMO and Bartlett’s test

Total variance explained (personality traits)

Pearson’s correlation matrix (among items in personality traits)

Final cluster centres

Model summary

Coefficients

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Acknowledgements

Declaration: This is to declare that the authors have not received any funding, financial assistance or grants in relation to the present study.

Corresponding author

About the authors.

Sthitaprajnya Pattanayak is pursuing PhD in Management from Assam Rajiv Gandhi University of Co-operative Management (ARGUCOM), Sivasagar, Assam. By profession, he is a banker and works in a commercial bank in India. He has over 19 years of experience in commercial banking in the areas of MSME advances, credit appraisal, operations and general banking. Other than this, he has keen research interest in enterprise studies, management and finance.

Prof (Dr) Munindra Kakati is a Professor of Finance, and presently Vice Chancellor of Assam Rajiv Gandhi University of Cooperative Management (ARGUCOM), Sivasagar. He is a distinguished academician and earlier, served as the Dean, Faculty of Management, Gauhati University, Guwahati. He is an alumnus of BITS (Pilani) and Gauhati University. He has several publications to his credit in national and international journals in the areas of management, enterprise studies, marketing and finance.

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What Makes a New Business Start-Up Successful?

• Published: May 2000
• Volume 14 , pages 165–182, ( 2000 )

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This paper seeks a good measure of new business performance, and then explains this measure by various dimensions of business strategy. Three criteria are used to create a one dimensional ordinal ranking of high, medium and low performance for new business starts: employment growth; return on capital employed; and labour productivity. It is shown that statistical cluster analysis provides a convincing separation of a sample of new business starts into high, medium and low performance categories, using a minimum distance criterion for clustering. An ordinal logit model (with selection) is then used to explain this performance ranking. The results indicate that many widely discussed features of small business strategy have little, or even negative, impact on performance. Of the numerous aims that owner managers may adopt (survival, growth etc.), only one appears to have a major impact on performance; the pursuit of the highest rate of return on investment. Many entrepreneurial perceptions of their own capabilities appear false or unimportant, with the exception of organisational features and systems.

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Lessons from Amazon’s Early Growth Strategy

If you’re interested in strategies for scaling start-ups, this episode is for you.

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So much has been written about Amazon’s outsized growth. But Harvard Business School professor Sunil Gupta says it’s the company’s unusual approach to strategy that has captured his scholarly attention. Gupta has spent years studying Amazon’s strategy and its founder and former CEO Jeff Bezos.

In this episode, Gupta shares how Amazon upended traditional corporate strategy by diversifying into multiple products serving many end users, instead of having a narrow focus.

He argues that some of Amazon’s simplest business strategies — like their obsession with customers and insistence on long-term thinking — are approaches that companies, big and small, can emulate.

Key episode topics include: strategy, innovation, leadership, scaling, Jeff Bezos, long-term thinking, customer focus.

HBR On Strategy curates the best case studies and conversations with the world’s top business and management experts, to help you unlock new ways of doing business. New episodes every week.

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HANNAH BATES: Welcome to HBR On Strategy , case studies and conversations with the world’s top business and management experts, hand-selected to help you unlock new ways of doing business.

So much has been written about Amazon’s outsized growth. But Harvard Business School professor Sunil Gupta says it’s the company’s unusual approach to strategy that has captured his scholarly attention.

Gupta has spent years studying Amazon’s strategy and its founder and former CEO, Jeff Bezos.

In this episode, Gupta shares how Amazon upended traditional corporate strategy by diversifying into multiple products serving many end users instead of focusing more narrowly.

And he argues that some of their simplest business strategies – like their obsession with the customer and insistence on long-term thinking – are approaches that companies, big and small, should emulate.

If you’re interested in innovation strategy, this episode is for you. It originally aired on HBR IdeaCast in November 2020. Here it is.

ALISON BEARD:  Welcome to the HBR IdeaCast from Harvard Business Review.  I’m Alison Beard.

If you had to name the most successful business leader alive today, who would you say?  I can’t hear you from my basement podcasting room, but I would bet that for many of you, the answer is Jeff Bezos, CEO of Amazon.  This is a man who over the past 25 years turned his online bookstore startup into a diversified company currently valued at $1.6 trillion.

Amazon is a digital retailing juggernaut, it’s also a web services provider, media producer, and manufacturer of personal technology devices like Kindle and Echo.  Oh, and Bezos also owns the Washington Post and Blue Origin, a space exploration company.  Forbes tells us he is the richest person in the world.

How did he accomplish so much?  How did he change the business landscape?  What mistakes has he made along the way?  A new collection of Bezos’s own writing, which full disclosure, my colleagues at Harvard Business Review Press have published, offer some insights.  Here’s a clip from one speech that’s included.  The book is called Invent and Wander.

And our guest today, who has spent years studying both Amazon and Bezos, is here to talk with me about some of the key themes in it, including the broad drivers of both the company and the CEO’s success.  Sunil Gupta is a professor of business administration at Harvard Business School and cochair of its executive program, and cochair of its executive program on driving digital strategy, which is also the title of his book.  Sunil, thanks so much for being on the show.

SUNIL GUPTA:  Thank you for having me, Alison.

ALISON BEARD:  So Invent and Wander.  I get that Bezos is inventive.  You know, he created a new way for us to buy things – everything.  How is he also a wonderer?

SUNIL GUPTA:  So he’s full of experiments.  His company and his whole style is known for experimentation, and he says that in so many words that if you want big winners, then you have to be willing to have many failures.  And the argument is, one big winner will take care of a thousand failed experiments.  So I think that’s the wandering part.  But also his experiments are not aimless.  There is a certain thought and process behind what experiments to do and why they will connect to the old, old picture of what Amazon is today.

ALISON BEARD:  And your expertise is in digital strategy.  How does he break the traditional rules of strategy?

SUNIL GUPTA:  So for the longest time the way, at least I was taught in my MBA program and the way we teach to our MBA students and executives, is strategy is about focus.  But if you look at Amazon, Amazon certainly doesn’t look like it’s focusing on anything, so obviously Jeff Bezos missed that class, otherwise it’s a very, very different thing.

And then you’d say, why is it that so called lack of focus strategy seems to be working for Amazon?  And I think the fundamental underlying principle that he’s guiding his whole discussion of strategy is, he’s changed the rules of strategy.  So the old rules of strategy were, the way you gained competitive advantage is by being better or cheaper.  So if I am selling you a car, my car is better of cheaper.  But the inherent assumption in that strategy statement is, I’m selling one product to one customer.  And what Amazon is basically arguing is, the digital economy is all about connection.  We have got to connect products and connect customers.  Let me explain why that is so powerful.

So connecting products, here the idea is, I can sell you, this is a classic razor and blade strategy.  I can sell you a razor cheap in order to make money on the blade.  So I can sell you Kindle cheap in order to make money on the ebooks.  Now, at some level you might say, hey, razor and blade have been around forever.  What’s so unique today?  I think unique today is razor could be in one industry and blades could be in completely different industrys.

So for example, if you look at Amazon’s portfolio of businesses, you sort of say, not only Amazon is an e-commerce player, but also is making movies and TV shows, its own studio.  Well, why does it make sense for an e-commerce player, an online retailer to compete with Hollywood.  Well, Walmart doesn’t make movies.  Macy’s doesn’t make movies?  So why does it make sense for Amazon to make movies?

And I think once you dig into it, the answer becomes clear that the purpose of the movies is to keep and gain the Prime customers. Two day free shipping is fine, but if  you ask me to pay $99 or $119 for two day free shipping, I might start doing the math in my head, and say, OK, how many packages do I expect to get next year?  And is the Prime membership worth it or not?

But once you throw in, in addition to the two-day free shipping, you throw in some TV shows and movies that are uniquely found only on Amazon, I can’t do this math.  And why is Prime customers important to Amazon?  Because Prime customers are more loyal.  They buy three or four times more than the non-Prime customers, and they’re also less price sensitive.

And in fact, Jeff Bezos has said publicly that every time we win a Golden Globe Award for one of our shows, we sell more shoes.  So this is, and he said it in your book, Invent and Wander, also, that we might be the only company in the world which has figured out how winning Golden Globe Awards can actually translate into selling more products on the online commerce.

So this is a great example of the razor being in a very different industry and blade being in another industry.  Take another example.  Amazon has a lending business where they give loans to small and medium enterprises. If Amazon decides to compete with banks tomorrow, Amazon can decide to offer loans to the small merchants at such a low price that banks would never be able to compete.  And why would Amazon be able to do that?  Because Amazon can say, hey, I’m not going to make money on loans, as much money on loans, but I’ll make more money when these businesses, small businesses grow and do more transactions on my marketplace platform.  And I get more commissions.  So again, loan can become my razor in order to help the merchants grow and make money on the transaction and the commission that I get from that.  The moment I make somebody else’s, in this case the banks, core business my razor, they will make a very hard time competing.  So I think that’s the key change, the fundamental rules of strategy and competition in that direction.

The second part of connection is connecting customers, and this is the classic network effect.  So marketplace is a great example of network effects.  The more buyers I have, the more sellers I have.  The more sellers I have, the sellers I have, the more buyers I get, because the buyers can find all the items.  And that becomes flywheel effect, and it becomes a situation where it’s very hard for a new player to complete with Amazon.

ALISON BEARD:  In this diversification that Amazon has done, how have they managed to be good at all of those things?  Because they’re not focused.  You know, they’re not concentrated on an area of specific expertise.  So how have they succeeded when other companies might have failed because they lacked that expertise, or they were spreading themselves too thin?

SUNIL GUPTA:  So I think it depends on how you define focus.  Most of us, when we define focus, we sort of define focus by traditional industry boundaries, that I’m an online retailer, therefore going into some other business is lack of focus.  The way Amazon thinks about is focus on capabilities.

So if you look at it from that point of view, I would argue that Amazon had three fundamental core capabilities.  Number one, it’s highly customer focused, not only in its culture, but also in its capability in terms of how it can actually handle data and leverage data to get customer insight.  The second core capability of Amazon is logistics.  So it’s now a world class logistics player.  It uses really frontier technology, whether it’s key word, robotics, computer vision, in its warehouse to make it much more efficient.

And the third part of Amazon’s skill or the capability is its technology.  And a good example of that is Amazon Web Services, or AWS.  And I think if you look at these three core capabilities, customer focus and the data insight that it gets from that, the logistics capability, and the technology, everything that Amazon is doing is some way or the other connected to it.  In that sense, Amazon, and there’s no lack of focus, in my judgment on Amazon.

Now, if he starts doing, starts making cream cheese tomorrow or starts making airplane engines, then I would say, yes, it’s got a lack of focus.  But one of the other things that Jeff Bezos has said again and again is this notion of work backwards and scale forward.  And what that means is, because you’re customer obsessed, you sort of find ways to satisfy customers, and if that means developing new skills that we don’t have because we are working backwards from what the customer needs are, then we’ll build those skills.

So a good example of that is, when Amazon started building Kindle, Amazon was never in the hardware business.  It didn’t know how to build hardware.  But Bezos realized that as the industry moved, people are beginning to read more and more online, rather, or at least on their devices, rather than the physical paper copy of a book.  So as a result, he says, how do we make it easier for consumers to read it on an electronic version?  And they’re spending three years learning about this capability of hardware manufacturing.  And by the way, Kindle came out long before iPad came out.  And of course, that capability now has helped them launch Echo and many other devices.

ALISON BEARD:  Right.  So it’s the focus on the customer, plus a willingness to go outside your comfort zone, the wander part.

SUNIL GUPTA:  Exactly.

ALISON BEARD:  Yeah.  How would you describe Bezos’s leadership style?

SUNIL GUPTA:  So I think there are at least three parts to it.  One is, he said right from day one that he wants to be a long-term focus.  The second thing is being customer obsessed.  And many times he has said that he can imagine, in the meetings he wants people to imagine an empty chair.  That is basically for the customer. And he says, we are not competitor focused.  We are not product focused.  We are not technology focused.  We are customer focused.  And the third is, willingness to experiment.  And fail, and build that culture in the company that it’s OK to fail.

ALISON BEARD:  What about personally, though?  Is he a hard charger?  Is he an active listener?  What’s it like to be in a room with him?

SUNIL GUPTA:  Oh, he’s certainly a hard charger.  I mean, he’s also the kind of guy, when he hires people, he says, you can work long, hard, or smart.  But at Amazon, you can choose two out of three.  And I think this is similar to many other leaders.  If you look at Steve Jobs, he was also a very hard charging guy.  And I think some people find it exhilarating to work with these kind of leaders.  Some find it very tough.

ALISON BEARD:  Do you think that he communicates differently from other successful CEOs?

SUNIL GUPTA:  So the communication style that he has built in the company is the very famous now, there’s no PowerPoints.  So it’s a very thoughtful discussion.  You write six-page memos, which everybody, when their meeting starts, everybody sits down and actually reads the memo.

In fact, this was a very interesting experience that I had.  One of my students, who was in the executive program, works at Amazon in Germany.  And he is, he was at that point in time thinking of moving to another company and becoming a CEO of that company.  So he said, can I talk to you about this change of career path that I’m thinking about?  I said, sure.  So we set up a time, and five minutes before our call, he sends me an email with a six-page memo.  And I said, well, shouldn’t he have sent this to me before, so I could at least look at it?  He says, no, that’s the Amazon style.  We’ll sit in silence and read it together.  And so I read it together, because then you’re completely focused on it.  And then we can have a conversation.  But this discipline of writing a six-page memo, it’s a very, very unique experience, because you actually have to think through all your arguments.

ALISON BEARD:  You also mentioned the long term focus, and that really stood out for me, too, this idea that he is not at all thinking of next year.  He’s thinking five years out, and sometimes even further.  But as a public company, how has Amazon been able to stick to that?  And is it replicable at other companies?

SUNIL GUPTA:  I think it is replicable.  It requires conviction, and it requires a way to articulate the vision to Wall Street that they can rally behind.  And it’s completely replicable.  There are other examples of companies who have followed a similar strategy.  I mean, Netflix is a good example.  Netflix hadn’t made money for a long period of time.  But they sold the vision of what the future will look like, and Wall Street bought that vision.

Mastercard is exactly the same thing.  Ajay Banga is giving three year guidance to Wall Street saying, this is my three-year plan, because things can change quarter to quarter.  I’m still responsible to tell you what we are doing this quarter, but my strategy will not be guided by what happens today.  It will be guided by the three-year plan that we have.

ALISON BEARD:  There are so many companies now that go public without turning any profit, whereas Amazon now is printing money, and thus able to reinvest and have this grand vision.  So at what point was Bezos able to say, right, we’re going to do it my way?

SUNIL GUPTA:  I think he said it right from day one, except that people probably didn’t believe it.  And in fact, one of the great examples of that was, when he was convinced about AWS, the Amazon Web Services, that was back in the early 2000s, when a majority of the Wall Street was not sure what Jeff Bezos was trying to do, because they say, hey, you are an online retailer.  You have no business being in web services.  That’s the business of IBM.  And that’s a B2B business.  You’re in a B2C business.  Why are you going in there?

And Bezos said, well, we have plenty of practice of being misunderstood.  And we will continue with our passion and vision, because we see the path.  And now he’s proven it again and again why his vision is correct, and I think that could give us more faith and conviction to the Wall Street investors.

SUNIL GUPTA:  Oh, absolutely.  And he’s one of the persons who has his opinion, and you always surround yourself with people better than you.

ALISON BEARD:  How has he managed to attract that talent when it is so fiercely competitive between Google, Facebook, all of these U.S. technology leaders?

SUNIL GUPTA:  So a couple of things I would say.  First of all, it’s always good fun to join a winning team.  And all of us want to join a winning team, so this certainly is on a trajectory which is phenomenal.  It’s like a rocket ship that is taking off and has been taking off for the last 25 years.  So I think that’s certainly attractive to many people, and certainly many hard charging people who want to be on a winning team.

And a second thing is, Amazon’s culture of experimentation and innovation.  That is energizing to a lot of people.  It’s not a bureaucracy where you get bogged down by the processes.  So the two type of decisions that we talked about, he gives you enough leeway to try different things, and is willing to invest hundreds of millions of dollars into things that may or may not succeed in the future.  And I think that’s very liberating to people who are willing to take on the ownership and build something.

ALISON BEARD:  But don’t all of the tech companies offer that?

SUNIL GUPTA:  They do, but if you think about many other tech companies, they’re much more narrow in focus.  So Facebook is primarily in social media.  Google is primarily in search advertising.  Yes, you have GoogleX, but that’s still a small part of what Google does.  Whereas if you ask yourself what business is Amazon in, there are much broader expansive areas that Amazon has gone into.  So I think the limits, I mean, Amazon does not have that many limits or boundaries as compared to many other businesses in Silicon Valley.

ALISON BEARD:  So let’s talk a little bit about Bezos’s acquisition strategy.  I think the most prominent is probably Whole Foods, but there are many others.  How does he think about the companies that he wants to bring in as opposed to grow organically?

SUNIL GUPTA:  So some acquisitions are areas where he thinks that he can actually benefit and accelerate the vision that he already has.  So for example, the acquisition of Kiva was to improve the efficiency and effectiveness of the systems that he already put in place in his warehouse.  And logistics and warehouse is a key component or key part of Amazon’s business, and he saw that Kiva already was ahead of the curve in technology that he probably wanted to have that in his own company.  So that was obvious acquisition, because that fits in the existing business.

Whole Foods is kind of a slightly different story, in my judgment, because I some ways, you can argue, why is Amazon, an online player, buying an offline retail store, Whole Foods?  And in fact, they bought it at 27% premium.  So that doesn’t make sense for an online retailer commerce to go to offline channels.  And I think, in fact, part of the reason in my judgment is, it’s not just Whole Foods, but it’s about the food business, per se.  And why is Amazon so interested in food?  In fact, Amazon has been trying this food business, online food delivery for a long period of time without much success.  And Whole Foods was one, another way to try and get access to that particular business.  And why is that so important to Amazon, even though you could argue, food is a low margin business?

And I would say, part of the reason is, food is something, grocery is something that you buy every week, perhaps twice a week.  And if I, as Amazon, can convince you to buy grocery online from Amazon, then I’m creating a habit for you to come onto Amazon every week, perhaps twice a week.  And once you are on Amazon, you will end up buying other products on Amazon.  Whereas if you are buying electronics, you may not come to Amazon every day.

So this is a habit creation activity, and again, it may not be a very high margin activity to sell you food.  But I’ve created a habit, just like Prime.  I’ve created a loyal customer where you think of nothing else but Amazon for your daily needs, and therefore you end up buying other things.

ALISON BEARD:  And Amazon isn’t without controversy.  You know, and we should talk about that, too.  First, there are questions about its treatment of warehouse employees, particularly during COVID.  And Bezos, as you said, has always been relentlessly focused on the customer.  But is Amazon employee centric, too?

SUNIL GUPTA:  So I think there is definitely some areas of concern, and you rightly said there is a significant concern about the, during the COVID, workers were complaining about safety, the right kind of equipment.  But even before COVID, there were a lot of concerns about whether the workers are being pushed too hard.  They barely have any breaks.  And they’re constantly on the go, because speed and efficiency become that much more important to make sure customers always get what they are promised.  And in fact, more than promised.

Clearly Amazon either hasn’t done a good job, or hasn’t at least done the public relations part of it that they have done a good job.  Now, if you ask Jeff Bezos, he will claim that, no, actually, they have done things.  For example, they offer something called carrier choice, where they give 95% tuition to the employees to learn new skills, whether they’re relevant to Amazon or not.  Pretty much like what Starbucks does for its baristas, for college education and other things.  But I think more than just giving money or tuition, it requires a bit of empathy and sense that you care for your employees, and perhaps that needs, that’s something that Amazon needs to work on.

ALISON BEARD:  And another challenge is the criticism that it has decimated mom and pop shops.  Even when someone sells through Amazon, the company will then see that it’s a popular category and create it itself and start selling it itself.  There’s environmental concerns about the fact that packages are being driven from warehouses to front doors all over America.  And boxes and packaging.  So how has Bezos, how has the company dealt with all of that criticism?

SUNIL GUPTA:  They haven’t.  And I think those are absolutely valid concerns on both counts, that the small sellers who grow to become reasonably big are always under the radar, and there are certainly anecdotal evidence there, small sellers have complained that Amazon had decided to sell exactly the same item that they were so successful in selling, and becoming too big is actually not good on Amazon, because Amazon can get into your business and wipe you away.  So that’s certainly a big concern, and I think that’s something that needs to be sorted out, and Amazon needs to clarify what its position on that area is, because it benefits from these small sellers on his platform.

And your second question about environmental issues is also absolutely on the money, because not only emission issues, but there’s so many boxes that pile in, certainly in my basement, from Amazon.  You sort of say, and it’s actually ironical that Millennials who are in love with Amazon are extremely environmentally friendly.  But at the same time, they would not hesitate to order something from Amazon and pile up all these boxes.  So I think Amazon needs to figure out a way to think about both those issues.

ALISON BEARD:  And at what point will it have to?  I mean, it seems to be rolling happily along.

SUNIL GUPTA:  Well, I think those issues are becoming bigger and bigger, and it’s certainly in the eye of the regulators, also, for some of these practices.  And not only because it’s too big, and there might be monopoly concerns, but these issues will become larger, and any time you become a large company, you become the center of attraction for broader issues than just providing shareholder value.

ALISON BEARD:  Yeah.  So those are weaknesses possibly for the company.  What are some of Bezos’s personal weaknesses that you’ve seen in studying him and the company?

SUNIL GUPTA:  So I think one thing that stands out to me, and at least in the public forums, I have not seen any empathy.  And it’s, I mean, we talk about that the leaders have, should have three qualities.  They should be competent.  They should have a good character.  And they should have compassion.  So he’s certainly very competent.  I mean, he’s brilliant in many aspects, right, from the computer vision and AI and machine learning, to the nuances of data analytics, to the Hollywood production, etc.  He also seems to have good character, at least I have not heard any personal scandals, apart from his other issues in his personal life, perhaps.

Those characteristics of competence and character make people respect you.  What makes people love you is when you show compassion, and at least I haven’t seen compassion or empathy that comes out of him.  I mean, he certainly comes across as a very hard charging, driven person, which probably is good for business.  But the question of empathy is perhaps something lacking right now.

ALISON BEARD:  Yeah.  The other issue is his just enormous wealth.  He did invent this colossally valuable company, but should anyone really be that rich?

SUNIL GUPTA:  Well, I guess that’s, you can say that’s the good or the bad thing about capitalism.  But I think, and again, my personal view is there’s nothing wrong in becoming rich, if you have been successful and done it with hard work and ingenuity.  But how you use your wealth is something that perhaps will define Jeff Bezos going forward.  I think Bill Gates is a great example how he actually has used his wealth and his influence and his expertise and his brilliance into some certain thing that actually is great for humanity.

Now, whether Jeff Bezos does that down the road, I don’t know, whether his space exploration provides that sort of outlet which is both his passion as well as good for humanity, I don’t know.  But at some point in time, I think it’s the responsibility of these leaders to sort of say, my goal is not simply to make money and make my shareholders rich, but also help humanity and help society.

ALISON BEARD:  If you’re talking to someone who’s running a startup, or even a manager of a team at a traditional company, what is the key lesson that you would say, this is what you can learn from Jeff Bezos?  This is what you can put to work in your own profession?

SUNIL GUPTA:  So I would say two things that at least I would take away if I were doing a startup.  One is customer obsession.  Now, every company says that, but honestly, not every company does it, because if you go to the management meetings, if you go to the quarterly meetings, you suddenly go focus on financials and competition and product.  But there’s rarely any conversation on customers.  And I think, as I mentioned earlier, that Jeff Bezos always tells his employee to think of the imaginary chair in which a customer is sitting, because that’s the person that we need to focus on.  Howard Shultz does the same thing at Starbucks, and that’s why Starbucks is so customer focused.

So I think that’s the first part.  And the argument that Bezos gives is, customers are never satisfied.  And that pushes us to innovate and move forward, so we need to innovate even before the rest of the world even sees that, because customers are the first ones to see what is missing in the offering that you have.

And the second I would say that I would take away from Jeff Bezos is the conviction and passion with what you do.  And many times that goes against the conventional wisdom.  And the Amazon Web Services is a great example of that.  The whole world, including the Wall Street Journal and the Wall Street analysts were saying, this is none of Amazon’s business to do web services.  But he was convinced that this is the right thing to do, and he went and did that.

And part of that conviction may come from experiments.  Part of that conviction comes from connecting the dots that he could see that many other people didn’t see.  I mean, that’s why he went, left his job, and went to Seattle to do the online bookstore, because he could see the macro trends as to what the Internet is likely to do.  So, I think that’s the vision that he had.  And once you have the conviction, then you follow your passion.

ALISON BEARD: Sunil, thanks so much for coming on the show.

SUNIL GUPTA:  Thank you for having me. Alison.

HANNAH BATES: That was Harvard Business School professor Sunil Gupta, in conversation with Alison Beard on the HBR IdeaCast .

We’ll be back next Wednesday with another hand-picked conversation about business strategy from Harvard Business Review. If you found this episode helpful, share it with your friends and colleagues, and follow our show on Apple Podcasts, Spotify, or wherever you get your podcasts. While you’re there, be sure to leave us a review.

And when you’re ready for more podcasts, articles, case studies, books, and videos with the world’s top business and management experts, find it all at HBR.org.

This episode was produced by Mary Dooe, Anne Saini, and me, Hannah Bates. Ian Fox is our editor. And special thanks to Maureen Hoch, Nicole Smith, Erica Truxler, Ramsey Khabbaz, Anne Bartholomew, and you – our listener. See you next week.

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Meritocracy across Countries

Are labor markets in higher-income countries more meritocratic, in the sense that worker-job matching is based on skills rather than idiosyncratic attributes unrelated to productivity? If so, why? And what are the aggregate consequences? Using internationally comparable data on worker skills and job skill requirements of over 120,000 individuals across 28 countries, we document that workers' skills better match their jobs' skill requirements in higher-income countries. To quantify the role of worker-job matching in development accounting, we build an equilibrium matching model that allows for cross-country differences in three fundamentals: (i) the endowments of multidimensional worker skills and job skill requirements, which determine match feasibility; (ii) technology, which determines the returns to matching; and (iii) idiosyncratic matching frictions, which capture the role of nonproductive worker and job traits in the matching process. The estimated model delivers two key insights. First, improvements in worker-job matching due to reduced matching frictions account for only a small share of cross-country income differences. Second, however, improved worker-job matching is crucial for unlocking the gains from economic development generated by adopting frontier endowments and technology.

We thank Christopher Tonetti for an insightful discussion. We benefited from helpful comments and suggestions by seminar audiences at Columbia University, the University of Chicago, the University of Houston, the University of Oxford, the Federal Reserve Bank of Minneapolis, and Stanford University, as well as participants at the 2022 Conference on the Macroeconomics of Inequality at the Federal Reserve Bank of St. Louis, the 2022 and 2023 SED Annual Meetings, the 2023 Empirical Macroeconomics Workshop in Phoenix, the 2023 German Economists Abroad Conference, the 2023 and 2024 Columbia Junior Micro-Macro Labor Conferences, the 2024 Winter Meeting of the NBER Economic Fluctuations and Growth Program, and the 2024 Spring Meeting of the NBER Labor Studies Program. Moser also thanks the Federal Reserve Bank of Minneapolis and the Heller-Hurwicz Economics Institute at the University of Minnesota for their generous hospitality during a significant share of the period of work on this project. Any errors are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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A look at small businesses in the U.S.

Most U.S. adults (86%) say small businesses have a positive effect on the way things are going in the country these days, according to a recent Pew Research Center survey . Small businesses, in fact, receive by far the most positive reviews of any of the nine U.S. institutions we asked about, outranking even the military and churches.

Despite their name, small businesses loom large in the United States. These businesses – defined here as those with 500 employees or fewer – account for 99.9% of U.S. firms, according to the Small Business Administration . While most of these 33 million firms don’t have paid employees, about 6 million of them do . They account for just under half of total private sector employment (46%).

As National Small Business Week approaches, here’s a look at small businesses in the U.S. and public attitudes about them, based on federal data and Center surveys.

Pew Research Center conducted this analysis to provide a glimpse into the state of American small businesses ahead of National Small Business Week .

In this analysis, “small businesses” are defined as employer firms with fewer than 500 workers. The analysis relies primarily on data from several Census Bureau sources: the Annual Business Survey (ABS), the Business Dynamics Statistics (BDS), and the Business Formation Statistics (BFS).

The ABS – conducted annually since 2017 – includes all non-farm U.S. firms with paid employees and receipts of $1,000 or more. Majority business ownership is characterized in the survey as having 51% or more of the stock or equity in the firm. The Census Bureau counts multiracial firm owners under all racial categories they identify with; Hispanic firm owners may be of any race. Read more about the  ABS methodology .

Data on the age of small business comes from the BDS. Data on the annual number of high-propensity business applications in the United States is based on the number of Employer Identification Number applications used for tax purposes and is not seasonally adjusted. Read more about the BFS methodology . Per capita calculations use state-level resident population data from the Census Bureau; estimates are as of July 1, 2023.

This analysis also draws on findings from recent Center surveys. More information on the methodology for these surveys can be found by following the links in the text.

There’s no single way to define a “small business.” Economists sometimes use the size of the establishment or firm, or turn to industry-specific size standards based on average revenue. For this analysis, we’ve used the U.S. Small Business Administration’s broadest definition: employer firms with fewer than 500 workers .

An establishment is a business with one physical location. A firm is a business organization that may have multiple locations (i.e., multiple establishments).

Just how ‘small’ are small businesses ?

Among the roughly 6 million small businesses with employees, 49% have just one to four workers, according to the latest estimates for 2021 from the Census Bureau’s Annual Business Survey (ABS). About a quarter (27%) have between five and 19 employees; 8% have 20 to 99; and just 1% have 100 to 499 workers. The remaining 14% had paid employees at some point during the year, but not during the March 12 pay period, which the ABS uses to determine employment size.

Overall, small businesses employed an estimated 56.4 million workers in 2021 and brought in over $16.2 trillion in revenue, according to ABS data. Perhaps unsurprisingly, small businesses with more employees tend to account for larger shares of overall revenue than those with fewer workers.

Who owns and runs small businesses?

Some small businesses are family-owned, but the vast majority are not. Among small businesses that reported this type of information for 2021, 27% were family-owned and 73% were not.

So-called “mom and pop shops” account for a relatively modest share of small businesses for which information is available. Overall, 10% of small businesses in the U.S. were jointly owned and operated equally by spouses in 2021. Another 11% were jointly owned by spouses but separately operated, with men more likely than women to serve as primary operators.

Franchises aren’t very common among small businesses. Just 5% of small businesses that reported this information were fully or partially operated as franchises in 2021.

In terms of demographics, men own a greater share of small businesses overall. About six-in-ten small businesses (61%) were majority-owned by men in 2021, while 22% were majority-owned by women. Another 14% were owned equally by men and women. (The ABS defines majority ownership as having at least 51% equity in the firm.)

Looking at small businesses where estimates of majority owners’ race and ethnicity are available, most (85%) had majority-White ownership in 2021. Smaller shares were majority-owned by Asian Americans (11%), Hispanic adults (7%), and Black or African American adults (3%). About 1% were estimated to have either American Indian and Alaska Native, or Native Hawaiian and other Pacific Islander majority owners.

Related: A look at Black-owned businesses in the U.S.

Despite owning small shares of these firms overall, many Black and Asian Americans see entrepreneurship as a marker of success, according to Center surveys.

For example, 30% of Asian Americans say owning a business is important to their own view of the American dream, according to a Center survey conducted from July 2022 to January 2023 . And 36% of Black adults say owning a business is important to their personal definition of financial success, with another 22% saying it’s essential , according to a September 2023 survey .

Still, Black and Asian Americans are more likely to place emphasis on other measures asked about in these surveys, such as owning a home, having a good family life and being debt-free, among others.

How old are most small businesses?

Many small businesses have stood the test of time. In 2021, the majority of these firms (59%) had been operational for at least six years, according to the Census Bureau’s Business Dynamics Statistics . This includes 15% that had been in business for more than 25 years.

On the other end of the spectrum, about a third of small businesses (35%) had been running for five years or fewer in 2021, including 9% that had launched in the last year. (The bureau could not determine the age of the remaining 6% of firms.)

How often do new businesses open?

Small businesses have reported financial and staffing challenges in the years following the coronavirus pandemic . But federal data reveals the staying power of entrepreneurship in the U.S.

The number of high-propensity business applications – those that are highly likely to turn into businesses with payrolls – remained relatively stable between 2009 and 2019, according to Census Bureau data . But the number of applications has risen since before the pandemic: There were nearly 1.8 million high-propensity business applications in 2023, up from about 1.3 million in 2019.

On the state level, places with larger populations saw the most high-propensity business applications in 2023. Florida (225,809) topped the list, followed by California (221,571), Texas (151,888), New York (131,206) and Georgia (80,403). But Missouri, Wyoming, Delaware, Florida and Colorado had the most applications per capita that year.

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Rebecca Leppert is a copy editor at Pew Research Center

Majorities of adults see decline of union membership as bad for the U.S. and working people

A look at black-owned businesses in the u.s., from businesses and banks to colleges and churches: americans’ views of u.s. institutions, 2023 saw some of the biggest, hardest-fought labor disputes in recent decades, older workers are growing in number and earning higher wages, most popular.

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