Measuring Regional Variations of Entrepreneurial Intent in India

Abstract

This study reports the first use of a newly developed approach to measuring entrepreneurial intent (EI) among regional populations in India. An improved EI scale is needed to avoid the problems that undermine previous attempts to accurately measure EI—especially the confounding of intentions with similar but theoretically distinct constructs such as beliefs, attitudes and expectations. This new 12-item scale is employed to measure EI levels of 335 participants in three regions of India: Mumbai, Kolkata and Chennai. Moderately high levels of EI are observed in each location, but the level in Kolkata is found to be significantly higher than the other two regions, contrary to popular expectations. The use of this improved scale is recommended to researchers and policymakers interested in entrepreneurship in India.

Keywords

Entrepreneurial intent entrepreneuring India regional variation scale development

The importance of entrepreneurship as a source of national economic benefit has been clearly observed in many countries. The effect is so well established that many developing countries such as India now actively seek to create policies to increase entrepreneurial intentions among the population, and to increase the economic multiplier effects of any new ventures that result. India is primarily a factor-driven economy that competes on its basic endowments of unskilled labour and natural resources (Schwab & Sala-i-Martin, 2013). According to the Global Entrepreneurship Monitor (GEM), when compared to the other BRIC factor-driven economies, India shows less favourable attitudes towards entrepreneurship, and exhibits total early-stage entrepreneurial activity levels of only 6.6 per cent (Singer, Amoros & Moska, 2014). Indeed, this rate places India at the bottom of the group of 10 factor-driven economies studied by GEM (average 23.3 per cent, maximum 37.4 per cent in Cameroon).

Despite this relatively low overall level of entrepreneurship in India, some regions of the country have achieved dramatically higher levels of entrepreneurship, while others suffer untested but popular perceptions that they are not very entrepreneurial (Damodaran, 2013). Such wide perceptual differences are sometimes attributed to differences in government policies (Asher, 2014; Bobbio, 2012), to general differences in regional attitudes and culture (Medhora, 1965; Saraf & Banerjee, 2013) or to the specific presence or absence of communities that are commonly thought to be especially entrepreneurial, such as Marwaris, Parsis, Chettiars or Dalits (Blake Willis & Rajasekaran, 2007; Medhora, 1965; Soundara Rajan & Senthil, 2007). The limited data about entrepreneurship distributions in India support the idea of strong regional variations. The GEM, for example, found that ‘while comparing across regions, Western India comes across more favourable toward entrepreneurship. While South and North India fare closer to average, Eastern India shows a conservative attitude toward entrepreneurship’ (Schwab & Sala-i-Martin, 2013, p. vii).

Broader international research has generally found that entrepreneurship within any given country (the prevalence, type or success rate of entrepreneurship) is affected by the supportive on inhibitory conditions within that country (Sternberg & Wennekers, 2005; Valliere & Peterson, 2009). These conditions include the policies and social institutions, as well as the cultural norms that guide individual attitudes and behaviours, which can vary widely among regions in a single country. One study found that both cultural and institutional factors influence entrepreneurship within regions of a single country, but the cultural factors have greater influence (Alvarez et al., 2011). These regional factors seem to have particularly strong influence on the survival prospects of new ventures (Falck, 2007).

Indian policies and programmes for enhancing entrepreneurship will therefore need to be sensitive to the regional variations that exist across the country, in cultures and institutions, and in entrepreneurial intentions and actions across the country. Reliable and accurate sources of data will be needed to assess regional conditions and to measure the effectiveness of policy actions. While programmes such as the GEM (Saraf & Banerjee, 2013) may be useful in providing views into current entrepreneurial prevalence rates, reliable and valid new measures of Indian entrepreneurial intent (EI) will also be needed to be predictive of future outcomes and to assess the effectiveness of programmes aimed at making entrepreneurship more desirable. This article therefore examines the flawed state of currently available measures of EI, develops an improved measurement scale suitable for regional measurements in India and demonstrates the application of this new scale by measuring the EI levels of sample groups in three regions of India.

Literature Review

Most theoretical approaches to understanding EI have been based on Ajzen’s Theory of Reasoned Action or the successor Theory of Planned Behaviour (TPB) (Ajzen, 1987, 1991). Theory of Planned Behaviour posits that intent resides in a nomological net that includes the related but distinct constructs of beliefs, attitudes and behaviours. According to TPB, beliefs influence attitudes towards any prospective behaviour, and these attitudes create an intention to perform the behaviour, which finally causes the individual to act. Beliefs represent an individual’s information about an object or act of interest and are formed by a number of different inference processes on the basis of direct observation of attributes as well as by the individual’s prior beliefs (Fishbein & Ajzen, 1975). Attitudes are an evaluative or affective feeling of favourableness or unfavourableness towards the object in question and are formed through information processing based on the set of beliefs about the object. Thus, beliefs and attitudes about entrepreneurship lead to the formation of EI.

Despite the centrality of the EI construct to most theories of entrepreneurship, the construct itself is still somewhat loosely defined and operationalised in the literature (Thompson, 2009); therefore, it is important that it be clearly construed and accurately measured (Wang & Jessup, 2014). Previous studies of EI and its antecedents have used measures that have exhibited shortcomings that threaten the validity of past findings. In particular, some of these past measures have confounded the measurement of EI with measurements of the other key constructs in TPB. For example, some prior studies (e.g., Autio et al., 2001; Urbig et al., 2012) have measured respondent expectations, which differ from intentions by neglecting individual agency. Some prior studies (e.g., Brenner, Pringle & Greenhaus, 1991; Kolvereid & Moen, 1997) have measured attitudes in place of intentions, which confuses cause with effect and other prior studies (e.g., Carr & Sequeira, 2007; Días-Garcia & Jiménez-Moreno, 2010) have measured entrepreneurial behaviours in place of intentions, which confuses effect with cause. A review of prior entrepreneurship research has failed to find a pure measure of EI.

Another important antecedent factor in TPB-based models of entrepreneurship is the concept of subjective norms (Walker & Kopecki, 2013). These norms represent the effects of the institutional environment and of the surrounding culture on the formation of beliefs and attitudes (Hattab, 2014). Institutional context influences the extent and types of entrepreneurship in a country, and the ways in which entrepreneurs behave, particularly in emerging economies with an uncertain and turbulent institutional context (Şeşen & Pruett, 2014; Welter & Smallbone, 2011). Institutional supports or burdens also moderate the relationship between associational activities and new business formation by entrepreneurs (De Clercq, Danis & Dakhil, 2009). For example, institutions provide the cognitive or socio-political legitimacy necessary to help new ventures overcome liabilities of newness (Aldrich & Fiol, 1994).

National culture has also been observed to have an important influence on the subjective norms towards entrepreneurship in different countries (Autio, Pathak & Wennberg, 2013; George & Zahra, 2002; Stephan & Uhlaner, 2010). Culture impacts the ability to spot or conceptualise new business opportunities (Baker, Gedajlovic & Lubatkin, 2005; Hechavarria & Reynolds, 2009), to form entrepreneurial intention (Siu & Lo, 2013) and to therefore act entrepreneurially (Kreiser et al., 2010; Madichie, Nkamnebe & Idemobi, 2008). Some evidence suggests that cultural effects are generally weaker than institutional/nationality effects (Tan, 2002), although other researchers have suggested there may exist a broader international culture that is shared by entrepreneurs in several different countries, and that this culture may be unlike the national culture that generally prevails in those countries (Mitchell et al., 2002).

There is also some evidence in the literature that religion plays a significant role in the subjective norms promoting or inhibiting an entrepreneurial career path. In context of entrepreneurial motivations and intent, religion has been viewed in three ways: (i) as a synthesis of societal or national meaning systems and moderator of environmental munificence factors, (ii) as an individual linkage of faith to specific entrepreneurial behaviours and (iii) as a symbolic social role that can be enacted by entrepreneurs (Drakopoulou-Dodd & Seaman, 1998). Religious views have been observed to influence individuals in the perception of opportunities and in their evaluation of the desirability of taking entrepreneurial actions to exploit these opportunities (Valliere, 2008). In a recent study of religious influence on entrepreneurship in India, Audretsch, Bönte and Tamvada (2013) found that Muslims and Jains in India are significantly more likely to seek self-employment than are Hindus, while Buddhists are significantly less likely (their study did not yield significant findings about Sikhs or Christians). While the study controlled for the effects of so-called ‘backward classes’, it did not control for possible caste effects among the Hindu participants, nor for other social effects that may be correlated with membership in a particular religious community (e.g., minority status, social marginality or access to the formal economy).

In summary, this literature indicates two challenges to the accurate assessment of EI levels in India. First, a purified scale instrument should be developed to give measurement of EI, without the misleading or confounding effects of unintended simultaneous measurement of beliefs, attitudes or expectations. Second, the proposed new scale instrument should be properly validated in the Indian context, not just in developed Western countries, and should be shown to be robust to the kinds of regional variations that may exist in India.

There is therefore a pressing need for the ability to accurately measure EI levels in the Indian context, and to use this ability to begin to understand the ways in which EI may vary among different regions, communities and other populations. The objective of this study is thus to create an appropriate measurement scale and to demonstrate its use through an initial application in India to explore possible regional variations in EI levels.

Methodology

The new scale used in the research was created by following the widely accepted methods of Hinkin (1995) and DeVellis (2003). In the first step, in this method, a working definition of EI was proposed as follows: Entrepreneurial intent is a personal conviction of an individual to take one or more specific actions in the process of newly exploiting a business opportunity. This definition was used to generate potential scale items and to validate these items with experts in the measurement of EI.

Item Generation

To begin forming a valid scale for measuring EI, a pool of potential scale items must first be created. This pool should be broad enough to fully capture the topic of interest, but must not be contaminated by related but distinct theoretical constructs in the nomological network (Schriesheim et al., 1993). For example, the items should include all activities in the opportunity exploitation process, but exclude other small business activities that are not specifically directed towards opportunity exploitation (such as routine planning and control, or managing employees). The item pool should also cover all the diverse possible theoretical conceptualisation of the construct but not be confounded by other constructs (Clark & Watson, 1995). A top-down approach to item generation was therefore employed, informed by theories of causation, effectuation and bricolage.

The initial items comprised questions designed to assess the respondent’s intention to perform various specific acts in the entrepreneuring process. Standard five-point Likert scales (anchors ‘strongly agree’ and ‘strongly disagree’) were employed to provide good granularity without creating undue respondent fatigue and the associated potential for biases. Items were not written with negative wording, despite the potential mitigating effect on pattern biases, because negative wording has been demonstrated to excessively reduce item response validity (Schriesheim & Hill, 1981). The time horizon used in questions of future expectations was selected to balance competing goals (Kautonen, van Gelderen & Fink, 2015; Randall & Wolff, 1994). If the horizon were too long, the item might not detect the step-wise nature of entrepreneuring and therefore suffer the same all-or-nothing shortcomings of previous scales. But if the horizon were too short, the item might be biased by transient mood. In this study, a 12-month horizon was therefore used for questions involving expectations of the future.

Item Validation

Construct adequacy of the 45 items in the initial pool was then judged by a panel of experts comprising six practicing entrepreneurs and academic researchers experienced in the use of measures of EI. They were asked to review the items for clarity, to suggest any additional items that may capture aspects of the construct that may have been omitted and to assess the relevance of items with reference to the working definition of the EI construct. This validation process used the modification of Tucker’s (1966) method recommended by Schriesheim et al. (1993), in which the experts individually rate the relevance of each item to the underlying construct, and these relevancy ratings are then factor analysed to quantify the content adequacy of the items. This factor analysis yielded a single factor that accounts for 44.0 per cent of the variance in the expert ratings, demonstrating a high degree of commonality in the relevance of the proposed measures for the underlying EI construct. Only seven items had inconsistent ratings by the experts, and were therefore dropped from the pool.

The initial questionnaire of the remaining 38 items was then given to the respondents, who were randomly selected to be broadly representative of typical future populations for the use of this new instrument (Clark & Watson, 1995). The samples were obtained in Mumbai, Kolkata and Chennai by random intercept in major shopping markets that draw diverse customers from the surrounding urban areas. A total sample of 337 valid responses was obtained from these three locations (102 in Mumbai, 35 in Kolkata and 200 in Chennai).

Paper-based surveys were administered in the language of the respondent’s preference from the available choices of English, Hindi, Bengali or Tamil. Written translations of the survey were translated by native speakers in India and were confirmed by back-translation to English. Respondents whose first language was not English, yet who completed English surveys, were able to confer with bilingual researchers to ensure their correct understanding of any items that may have been unclear to them. Completed surveys were transcribed into a single electronic dataset.

Scale Structure

Inter-item correlations were examined using factor analysis. Principal component analysis was employed using varimax rotation with Kaiser normalisations, which converged after 10 iterations. Using a threshold of eigenvalues greater than unity, seven orthogonal factors were extracted (Kaiser–Meyer–Olkin [KMO] = 0.951, Bartlett’s sphericity < 0.001) that explain 61.6 per cent of the variance in the data. Table 1 summarises the item loadings on the seven extracted factors and their communalities.

Selected items were then trimmed to make a more pure and parsimonious scale (Clark & Watson, 1995). Items with communalities below 0.40 were first trimmed (items Q03, Q04, Q08, Q09, Q16, Q19 and Q34). Next, items were trimmed that exhibited very little variance in response or that exhibited mean values within one standard deviation of a scale anchor, indicating likely truncation of respondent variation and correspondingly low information (items Q01, Q02, Q05–Q07, Q13, Q20, Q21 and Q35). Finally, items were progressively trimmed due to cross-loading on the other factors at greater than 0.4 (items Q29, Q28, Q12, Q37, Q38, Q23, Q25, Q17, Q11 and Q10). The final version of the scale therefore retained the 12 items that load purely onto a single factor and explain 47.5 per cent of the variance in the data. Table 2 provides the scale based on these items.

Several goodness-of-fit measures were then computed, including χ² (219.90, p < 0.001), χ²/df (4.07), Comparative Fit Index (CFI) (0.899), Tucker–Lewis Index (0.854) and Root Mean Square Error of Approximation (RMSEA) (0.096). These results all confirm the new scale is unidimensional and an acceptable measure of EI (Hinkin, 1995; Steiger & Lind, 1980; Tucker & Lewis, 1972). Finally, the scale reliability was assessed with Cronbach’s alpha of 0.899, indicating high reliability.

Regional Measurement

This scale was then used to calculate initial estimates of the levels of EI in each of the three regions being studied. Scale values were calculated for each respondent, using unweighted scores for each scale question and then summing these for each respondent. Table 3 reports the means and standard deviations for the respondent EI scores in each region. The mean values are scored on a range of 12–60 points.

With a range of mean scores from 42.97 to 48.52, the Indian regions appear to have similar EI levels. An analysis of variance (ANOVA) calculation was then performed with post-hoc analysis to determine if any of the small differences among these mean scores were significant. This analysis demonstrated that the score for Kolkata region was sufficiently higher than that for others which is significant at p < 0.001 level. Thus, our results demonstrate that, for the samples used, the levels of EI in Mumbai and Chennai regions are not meaningfully different, but the levels of EI in Kolkata region are meaningfully higher.

Table 1.

Principal Components Extracted

Item	F1	F2	F3	F4	F5	F6	F7	Comm
Q13 marketing strategy	0.778	0.280	0.148	0.164	0.121	0.097	–0.006	0.756
Q12 develop bizmodel	0.749	0.140	0.038	0.258	0.141	0.146	0.148	0.712
Q14 operations strategy	0.728	0.297	0.059	0.144	0.191	0.084	0.055	0.689
Q15 financial strategy	0.675	0.373	0.115	0.071	0.255	0.072	0.055	0.686
Q17 business plan	0.600	0.107	0.203	0.201	0.037	0.196	0.319	0.595
Q11 vision and goals	0.590	0.140	0.254	0.384	0.129	0.028	0.147	0.618
Q20 phone, website	0.550	0.158	0.502	0.227	–0.006	0.142	0.104	0.661
Q16 forecast financials	0.509	0.147	0.308	–0.015	0.204	0.203	0.293	0.545
Q18 create entity	0.493	0.260	0.295	0.067	0.237	0.052	0.391	0.614
Q35 calculate funds required	0.484	0.316	0.202	0.319	0.304	0.180	–0.017	0.602
Q02 id customer needs	0.461	0.311	0.060	0.428	0.079	–0.016	–0.061	0.507
Q05 analyze competitors	0.439	0.315	0.218	0.363	0.053	0.245	–0.078	0.540
Q28 develop prototype	0.227	0.734	–0.042	0.122	0.096	0.207	0.172	0.688
Q29 develop value prop	0.177	0.729	0.232	0.140	0.073	0.135	0.137	0.679
Q31 test value prop in market	0.248	0.708	0.207	0.213	0.013	0.093	0.079	0.666
Q27 create proof of concept	0.221	0.666	0.092	0.057	0.199	0.153	0.141	0.587
Q32 refine product/market fit	0.251	0.630	0.175	0.215	0.209	0.019	–0.001	0.581
Q30 create MVP	0.126	0.620	0.255	0.239	–0.041	0.155	0.277	0.625
Q33 scale production methods	0.205	0.615	0.241	0.090	0.275	0.107	0.067	0.578
Q23 purchase major equipment	–0.008	0.266	0.658	–0.006	0.051	0.145	0.209	0.571
Q22 obtain more resources	0.247	0.167	0.643	0.229	0.130	0.262	0.102	0.650
Q09 purchase a business	0.219	0.113	0.519	–0.032	0.234	–0.218	0.214	0.479
Q21 arrange banking services	0.462	0.215	0.501	0.176	0.144	0.181	0.096	0.604
Q34 arrange suppliers and distributors	0.257	0.390	0.464	0.154	0.346	–0.070	–0.103	0.593
Q06 search for business opportunities	0.186	0.102	–0.006	0.651	0.253	0.245	0.247	0.654
Q01 research a business opportunity	0.332	0.377	0.234	0.558	0.126	–0.125	–0.016	0.651
Q04 learn about solving problems	0.279	0.254	0.142	0.548	0.041	0.179	0.152	0.520
Q07 evaluate a business opportunity	0.322	0.205	–0.058	0.521	0.165	0.393	0.167	0.629
Q03 involve stakeholder in an opportunity	0.125	0.146	0.420	0.489	0.286	–0.080	0.086	0.548
Q38 obtain outside investment	0.191	0.092	0.221	0.213	0.745	–0.028	0.174	0.726
Q37 develop an investor pitch	0.217	0.297	0.091	0.107	0.708	0.279	0.092	0.742
Q36 id potential investors	0.354	0.300	0.118	0.262	0.493	0.294	–0.050	0.631
Q08 create a founder team	0.327	0.066	0.253	0.219	0.350	0.270	0.322	0.522
Q26 use resources in new ways	0.116	0.395	0.139	0.018	0.186	0.594	–0.003	0.576
Q25 experiment with resources	0.334	0.288	0.067	0.253	0.074	0.556	0.078	0.583
Q24 work around obstacles	0.337	0.193	0.417	0.228	0.058	0.473	–0.071	0.609
Q19 commit my own time and money	0.146	0.236	0.128	0.112	0.008	0.097	0.693	0.595
Q10 quit my existing job	0.065	0.137	0.243	0.174	0.302	–0.273	0.554	0.585

Author’s own.

Table 2.

Indian EI Scale

For each item below, indicate the degree to which you agree with the statement

Within the Next 12 Months I Intend to …	Strongly Disagree … Strongly Agree
Develop an operations strategy	12345
Develop a financial strategy	12345
Create a new business organisation and complete the regulatory paperwork	12345
Obtain more resources (e.g., people, money and equipment) for a new business	12345
Develop ways to work around obstacles, rules, standards and norms	12345
Reuse resources for clever purposes other than their original design	12345
Develop a proof of concept	12345
Develop a minimum viable product to deliver my value proposition to customers	12345
Test my value proposition in the market	12345
Refine my choice of market or product/service, based on market feedback	12345
Develop some scalable production methods	12345
Identify some potential sources of new investment	12345

Source: Author’s own.

Table 3.

Regional EI

Region (n)	Sample EI	Std. Dev.	Population EI^**
Mumbai (102)	44.30	8.99	42.53–46.07
Kolkata (33)	48.52^*	7.36	45.91–51.13
Chennai (200)	42.97	9.40	41.66–44.28

Source: Author’s own.

Notes: (i) ^* Sample mean differs at p < 0.001, one-way ANOVA.

(ii) ^** Confidence intervals listed at p = 0.05.

Discussion

The scale that was created for measuring EI features 12 items that load cleanly onto a single unidimensional factor. The scale items include measures of investigating available strategic options (items Q14, Q15, Q24, Q26 and Q32) and measures of activities that address practical challenges of getting a new venture launched and successfully offering a product/service to the market (items Q18, Q22, Q27, Q30, Q31, Q33 and Q36). Both types of measures are reasonable indicators of real intention to work out which business to create and how to get it successfully started. In the process of refining and purifying this scale, several items were trimmed that refer to the spotting or assessment of potential business opportunities (five of the eight items trimmed due to possible data truncation on the right-hand tail). The uniformly high scores on these items suggest that a general alertness to potential opportunities may be widespread in the Indian population. But this alertness does not correlate highly with the items retained in the scale and therefore might not be a reliable indicator of an entrepreneurial intention to subsequently do anything with the opportunities that are spotted. It is likely that there are many individuals who may be alert to opportunities but are not forming significant intent to exploit them through further entrepreneurial actions.

The application of this scale to discover the level of EI in the various sample populations in India is reasonably straightforward. One simply administers the final scale instrument of Table 2 and calculates the sum of the resulting item scores. These are then averaged to yield the mean level of EI in the sample. These mean levels could be easily normalised to fall within a 0–1 range if desired. From these scores, confidence intervals for mean EI levels of the corresponding populations can then be inferred from the sample means and variances.

In the case of the three regions sampled for this particular study, the scale has identified moderate levels of EI in all the regions. Comparison of the regions led to some interesting observations, somewhat at odds with commonly held beliefs. First, as noted above, the EI level for the Kolkata sample was actually significantly higher than in the other two regions. The second interesting observation is that the EI level for the Chennai sample was not significantly lower than the other regions. The results obtained in this study would suggest such expectations are erroneous and would similarly warrant further investigation to confirm this. Findings like this certainly warrant further investigation to see whether these results are repeatable with larger and more powerful samples, and whether they can be attributed to local institutional supports (e.g., systems and programmes) or to local cultural factors.

Conclusions

The objective of this study was to create an appropriate EI measurement scale for India and to use this scale to investigate the possibility of regional EI variations. It can be seen that this objective was achieved. The scale presented in Table 2 provides a simple 12-item measurement instrument that has been validated with 332 respondents in three different regions. It provides a highly reliable, unidimensional measure of EI that can be used throughout India. The application of this scale to the regions of Mumbai, Kolkata and Chennai shows that moderately high levels of EI can be found in India, but that these levels are not uniform across different regions. This is an important finding for anyone involved in developing policies or programmes to influence the level or types of entrepreneurial activities in India, or for academic researchers investigating theories of entrepreneurship in the Indian context.

This study has developed a simple unidimensional scale for accurately measuring EI in India and has used this scale to demonstrate that variations in Indian mean EI levels exist between different regions of the country. It therefore has contributed to our knowledge of Indian EI in two ways. First is the ability for future researchers to accurately assess EI levels among different Indian populations without incurring the serious flaws that have compromised most approaches to EI measurement that previously existed in the literature. Unlike these earlier approaches, the new scale does not confound measurements of EI with simultaneous measurements of beliefs, attitudes or expectations. Therefore, this new scale permits researchers to obtain a pure measurement of EI levels in India. Second, the use in this study of this scale in three regions has contributed new insight into the apparent levels of EI in those regions and a first look into the degree to which regional variations in EI levels may exist in India.

Still, the results reported in this study should be taken in context of the various limitations that were faced. In the initial creation of the new EI scale these include the small sample sizes and the fact that this data come specifically from only three Indian urban settings. This version of the scale has not been validated with data from other cities of India or from rural settings. Further, confirmatory research will be required to discern whether the discovered factor structure is stable and robust to a diverse range of populations and settings, and is therefore more broadly generalisable. In the application of this new scale to measure EI levels in the three selected regions, the most significant limitations again include the relatively small sample sizes (especially in Kolkata region), which may limit the representativeness of the samples and the validity of the finding of elevated EI in Kolkata, and the fact that the dataset used for measurement is the same as the dataset used for the scale development. Moreover, the measurements are self-reports that have not been triangulated by any other measure, so may include some degree of common method variance.

The present discovery of regional variations in EI highlights the need for additional research to better inform the decisions and actions of Indian policymakers and entrepreneurial ecosystem participants. It is likely that previous measures of EI in the Indian context may have been inaccurate, confounded by other closely related constructs and not capturing the subtleties of the steps within the entrepreneuring process. As a result, policies and support system services that were decided on the basis of those measures may actually be inappropriate for the real Indian situation. Moreover, decisions based on simple national-level measures may not appropriately reflect the variations that can now be seen among various regions. For these reasons, it is important than more research be directed towards better understanding the true levels of EI in India, and that this research takes into consideration the potential for variations among regions and communities within the overall population. Such research would provide important insights for policymakers and ecosystem participants. The immediate next step that needs to be taken is to replicate the validation and measurement of EI using the scale developed here, with larger datasets and in more diverse regions of India.

Footnotes

Acknowledgements

The author gratefully acknowledges the research assistance of Mr Sudip Mallik and the support of the National Institute for Entrepreneurship and Small Business Development, IIT Madras and IIT Bombay. This research was funded by Shastri Indo-Canadian Institute with the support of Foreign Affairs, Trade and Development Canada/avec l’appui d’Affaires Étrangères, Commerce et Développement Canada.

References

Ajzen

(1987). Attitudes, traits, and actions: Dispositional prediction of behavior in social psychology. Advances in Experimental Social Psychology, 20, (pp.1–63), New York: Academic Press.

Ajzen

(1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

Aldrich

, & Fiol

C.M.

(1994). Fools rush in? The institutional context of industry creation. Academy of Management Review, 19(4), 645–669.

Alvarez

, Urbano

, Coduras

, & Ruiz-Navarro

(2011). Environmental conditions and entrepreneurial activity: A regional comparison in Spain. Journal of Small Business and Enterprise Development, 18(1), 120–140.

Asher

(2014, February 14). Growth drivers and Mr Modi’s economic reasoning. Pragati: The Indian National Interest Review. Retrieved from http://pragati.nationalinterest.in/2014/02/growth-drivers-and-mr-modis-economic-reasoning/

Audretsch

D.B.

, Bönte

, & Tamvada

J.P.

(2013). Religion, social class, and entrepreneurial choice. Journal of Business Venturing, 28(6), 774–789.

Autio

, Keeley

R.H.

, Klofsten

, Parker

G.G.C.

, & Hay

(2001). Entrepreneurial intent among students in Scandinavia and the USA. Enterprise and Innovation Management Studies, 2(2), 145–160.

Autio

, Pathak

, & Wennberg

(2013). Consequences of cultural practices for entrepreneurial behaviors. Journal of International Business Studies, 44(4), 334–362.

Baker

, Gedajlovic

, & Lubatkin

(2005). A framework for comparing entrepreneurship processes across nations. Journal of International Business Studies, 36(5), 492–504.

10.

Blake Willis

, & Rajasekaran

(2007). Dalit entrepreneurs on the edges of caste and class: Ethnic minority entrepreneurship in India. In Dana

L.P.

(Ed.), Handbook of research on ethnic minotiry entrepreneurship: A co-evolutionary view on resource management. (pp. 599–614), Northampton, MA: Edward Elgar Publishing.

11.

Bobbio

(2012). Making Gujarat vibrant: Hindutva, development and the rise of subnationalism in India. Third World Quarterly, 33(4), 657–672.

12.

Brenner

O.C.

, Pringle

C.D.

, & Greenhaus

J.H.

(1991). Perceived fulfullment of organizational employment versus entrepreneurship: Work values and career intentions of business college graduates. Journal of Small Business Management, 29(3), 62–74.

13.

Carr

J.C.

, & Sequeira

J.M.

(2007). Prior family business exposure as intergenerational influence and entrepreneurial intent: A theory of planned behavior approach. Journal of Business Research, 60(10), 1090–1098.

14.

Clark

L.A.

, & Watson

(1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319.

15.

Damodaran

(2013, May 5). Whither Bengali enterprise? The Hindu. Retrieved from http://www.thehindubusinessline.com/opinion/columns/harish-damodaran/whither-bengali-enterprise/article4686526.ece

16.

De Clercq

, Danis

W.M.

, & Dakhil

(2009). The moderating effect of institutional context on the relationship between associational activity and new business activity in emerging economies. International Business Review, 19(1), 85–101.

17.

DeVellis

R.F.

(2003). Scale development: Theory and practice. Thousand Oaks, CA: SAGE Publications.

18.

Días-Garcia

, & Jiménez-Moreno

(2010). Entrepreneurial intention: The role of gender. International Entrepreneurial Management Journal, 6(3), 261–283.

19.

Drakopoulou-Dodd

, & Seaman

P.T.

(1998). Religion and enterprise: An introductory exploration. Entrepreneurship Theory & Practice, 23(1), 71–86.

20.

Falck

(2007). Survival chances of new businesses: Do regional conditions matter? Applied Economics, 39(16), 2039–2048.

21.

Fishbein

, & Ajzen

(1975). Belief, attitude, intention and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley.

22.

George

, & Zahra

S.A.

(2002). Culture and its consequences for entrepreneurship. Entrepreneurship Theory & Practice, 26(4), 5–8.

23.

Hattab

H.W.

(2014). Impact of entrepreneurship education on entrepreneurial intention of university students in Egypt. Journal of Entrepreneurship, 23(1), 1–18.

24.

Hechavarria

D.M.

, & Reynolds

P.D.

(2009). Cultural norms & business start-ups: The impact of national values on opportunity and necessity entrepreneurs. International Entrepreneurial Management Journal, 5(4), 417–437.

25.

Hinkin

T.R.

(1995). A review of scale development practices in the study of organizations. Journal of Management, 21(5), 967–988.

26.

Kautonen

, Van Gelderen

, & Fink

(2015). Robustness of the theory of planned behavior in predicting entrepreneurial intentions and actions. Entrepreneurship Theory & Practice, 39(3), 655–674.

27.

Kolvereid

, & Moen

(1997). Entrepreneurship among business graduates: Does a major in entrepreneurship make a difference? Journal of European Industrial Training, 21(4), 154–160.

28.

Kreiser

P.M.

, Marino

L.D.

, Dickson

, & Weaver

K.M.

(2010). Cultural influences on entrepreneurial orientation: The impact of national culture on risk taking and proactiveness in SMEs. Entrepreneurship Theory & Practice, 34(5), 959–983.

29.

Madichie

N.O.

, Nkamnebe

A.D.

, & Idemobi

E.I.

(2008). Cultural determinants of entrepreneurial emergence in a typical sub-Saharan African context. Journal of Enterprising Communities, 2(4), 285–299.

30.

Medhora

P.B.

(1965). Entrepreneurship in India. Political Science Quarterly, 80(4), 558–580.

31.

Mitchell

, Smith

J.B.

, Morsem

E.A.

, Seawright

K.W.

, Peredo

A.M.

, & McKenzie

(2002). Are entrepreneurial cognitions universal? Assessing entrepreneurial cognitions across cultures. Entrepreneurship Theory & Practice, 26(4), 9–32.

32.

Randall

D.M.

, & Wolff

J.A.

(1994). The time interval in the intention– behaviour relationship: Meta-analysis. British Journal of Social Psychology, 33(4), 405–418.

33.

Saraf

, & Banerjee

(2013). Global entrepreneurship monitor 2013: India report. New Delhi, India: Institute of Management Technology, Indian School of Business, Entrepreneurship Development Institute of India.

34.

Schriesheim

C.A.

, & Hill

K.D.

(1981). Controlling acquiescence response bias by item reversals: The effect on questionnaire validity. Educational and Psychological Measurements, 41(4), 1101–1114.

35.

Schriesheim

C.A.

, Powers

K.J.

, Scandura

T.A.

, Gardiner

C.C.

, & Lankau

M.J.

(1993). Improving construct measurement in management research: Comments and a quantitative approach for assessing the theoretical content adequacy of paper-and-pencil survey-type instruments. Journal of Management, 19(2), 385–417.

36.

Schwab

, & Sala-i-Martin

(2013). Global competitiveness report 2013–2014. Geneva CH: World Economic Forum.

37.

Şeşen

, & Pruett

(2014). The impact of education, economy and culture on entrepreneurial motives, barriers and intentions: A comparative study of the United States and Turkey. Journal of Entrepreneurship, 23(2), 231–261.

38.

Singer

, Amoros

J.E.

, & Moska

(2014). Global entrepreneurship monitor: 2014 global report. Babson Park, MA: GEM Consortium.

39.

Siu

W.S.

, & Lo

E.S.

(2013). Cultural contingency in the cognitive model of entrepreneurial intention. Entrepreneurship Theory & Practice, 37(2), 147–173.

40.

Soundara Rajan

S.N.

, & Senthil

(2007). Evolution of entrepreneurship in India. Global Journal of Business Management, 1(1), 35–42.

41.

Steiger

J.H.

, & Lind

(1980). Statistically-based tests for the number of common factors. Paper presented at the Annual Spring Meeting of the Psychometric Society. Iowa City, Iowa, USA.

42.

Stephan

, & Uhlaner

L.M.

(2010). Performance-based vs socially supportive culture: A cross-national study of descriptive norms and entrepreneurship. Journal of International Business Studies, 41(8), 1347–1364.

43.

Sternberg

, & Wennekers

(2005). Determinants and effects of new business creation using global entrepreneurship monitor data. Small Business Economics, 24(3), 193–203.

44.

Tan

(2002). Culture, nation, and entrepreneurial strategic orientations: Implications for an emerging economy. Entrepreneurship Theory and Practice, 26(4), 95–111.

45.

Thompson

E.R.

(2009). Individual entrepreneurial intent: Construct clarification and development of an internationally reliable metric. Entrepreneurship Theory & Practice, 33(3), 669–694.

46.

Tucker

L.R.

(1966). Factor analysis of relevance judgments: An approach to content validity. In Anastasi

(Ed.), Testing problems in perspective. (pp. 369–379), Washington, DC: American Council on Education.

47.

Tucker

L.R.

, & Lewis

(1972). A reliability coefficient for maximum likelihood factor analysis. Psychometrika, 38(1), 1–10.

48.

Urbig

, Weitzel

, Rosenkranz

, & Van Witteloostuijn

(2012). Exploiting opportunities at all cost? Entrepreneurial intent and externalities. Journal of Economic Psychology, 33(2), 379–393.

49.

Valliere

(2008). Exploring Buddhist influence on the entrepreneurial decision. International Journal of Entrepreneurial Behaviour & Research, 14(3), 172–191.

50.

Valliere

, & Peterson

(2009). Entrepreneurship and economic growth: Evidence from emerging and developed countries. Entrepreneurship & Regional Development, 21(5–6), 459–480.

51.

Walker

J.K.

, & Kopecki

(2013). The role of perceived abilities, subjective norm and intentions in entrepreneurial activity. Journal of Entrepreneurship, 22(2), 181–202.

52.

Wang

, & Jessup

L.M.

(2014). A review and synthesis of entrepreneurship research: Towards an integrative model of dependent variables. Journal of Entrepreneurship, 23(2), 163–199.

53.

Welter

, & Smallbone

(2011). Institutional perspectives on entrepreneurial behavior in challenging environments. Journal of Small Business Management, 49(1), 107–125.