Factors Influencing Business Performance Among Small Business Operators

Abstract

The development and contribution of Small Medium Enterprises (SMEs) are crucial for Malaysia to achieve high income country in the year of 2020. It is reported that the SMEs comprise 98.5% of total establishments in 2014, has contributed 32.7% of the Malaysian Gross Domestic Product (GDP) growth. This research focused on determining the significant factors that impact business performance among small business operators in a local university. As the research framework, six independent variables (Cost leadership strategy, Differentiation strategy, focus strategy, Entrepreneurial orientation, Business location and Productivity propensity), a dependent variable (Business performance) and a moderating variable (University support) were selected through the synthesizing previous studies. Data was collected via structured questionnaires from the 75 business operators within the University in Malaysia. Data was then analysed using SPSS17, and SmartPLS 3.0. Cost leadership strategy, Differentiation strategy and Productivity propensity were found to have significant impact on business performance of the small business operators; and University support had negative significant moderating effect on the relationship between Cost leadership and Business performance with an overall large effect size. As the contribution from this study, the business operators could focus on developing significant business strategies such as Cost leadership strategy and Differentiation strategy. On the other hand, the University could focus on its policies related to costs in order to ensure sustainable business performance of the small business operators and eventually contributes to the development of SMEs in the country.

Keywords: Business PerformancePorter’s Generic StrategiesEntrepreneurial OrientationBusiness LocationProductivity PropensitySmall Business Operators

Introduction

The demand of Small and Medium-size Enterprises (SMEs) has been increasing due to their contribution to the economy growth, creation of jobs and social development (Morrison, Breen, & Ali, 2003). Past researches have shown that SMEs play an important role in contributing about 20% to 45% full employment and equally contributing about 30% to 50% for rural income (Okpara & Wynn, 2007). The increasing demand of the SMEs in Malaysia is no exception. The development and contribution of SMEs are crucial for the country to achieve high income country in the year of 2020. In Malaysia, it is reported that the SMEs comprise 98.5% of total establishments in 2014, which these SMEs has contributed 32.7% of the Malaysian Gross Domestic Product (GDP) growth (SME Corp. Malaysia, 2014). According to SME Corp. Malaysia (2014), the average compounded growth rate (CAGR) of SMEs in year 2013 was 6.3% which is higher than the overall economy at CAGR of 4.7%. In addition, with the positive growth throughout the years, the contribution of SME to GDP increased from 29.4% in 2005 to 33.1% in 2013.

This research focuses on the business operators in USM which are currently facing some challenges. With the implementation of GST, the cost of running the business has increased. Besides that, minimum wage policy has impacted the business performance as all the business operators in USM are to obey the pricing policy set by the university. Hence, the aim of this research is to enhance the business performance of small business operators in USM through identifying the factors that impact business performance.

Problem Statement

SMEs in Malaysia have been playing an important role as they are the major contributors to country employment, GDP growth and productivity. In 2013, SMEs contributes to 57.5% of total employment and SME GDP has recorded a higher growth of 6.3%. On the other hand, the labour productivity gap between large firms and SMEs has improved gradually.

In current development, changes in the environment have been rapid such as globalization, political, social, economic and technological. The changes in environment pose a challenge to the SMEs. This research focuses on the business operators in USM which are currently facing some challenges. With the implementation of GST, the cost of running the business has increased. Besides that, minimum wage policy has impacted the business performance as all the business operators in USM are to obey the pricing policy set by the university. In this research, the aim is to enhance the business performance of small business operators in USM through identifying the factors that impact business performance.

Research Questions

The research questions will look into more detailed on the factors that impact business performance as well as the moderating role of the university support on the mentioned relationships. The research questions are as below

  • Does cost leadership strategy have positive significant influences on business performance?

  • Does differentiation strategy have positive significant influences on business performance?

  • Does focus strategy have positive significant influences on business performance?

  • Does business location have positive significant influences on business performance?

  • Does productivity propensity have positive significant influences on business performance?

  • Does entrepreneurial orientation have positive significant influences on business performance?

  • Does university support have significant moderating effect on the relationship between cost leadership strategy and business performance?

  • Does university support have significant moderating effect on the relationship between differentiation strategy and business performance?

  • Does university support have significant moderating effect on the relationship between focus strategy and business performance?

  • Does university support have significant moderating effect on the relationship between entrepreneurial orientation and business performance?

  • Does university support have significant moderating effect on the relationship between business location and business performance?

  • Does university support have significant moderating effect on the relationship between productivity propensity and business performance?

Purpose of the Study

The main purpose of this research is to identify the key factors that impact business performance among the business operators in Universiti Sains Malaysia (USM) as shown in Figure 01 .

Figure 1: Figure 01. Research Theoretical Framework
Figure 01. Research Theoretical Framework
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Underpinning theory

Resource Based View has been used as the basis to develop the research theoretical framework shown in Figure 1 . Resource Based View explains how a business firm would transform its tangible and intangible resources into competitive advantages in order to achieve outstanding business performance (Muthuveloo, R and Teoh, 2017). Resources of a business organization, which include the assets and skills, represent the source of business foundation for sustainable competitive advantage (Ambrosini & Bowman, 2009, Muthuveloo and Teoh, 2013). In addition, Masakure, Henson, and Cranfield (2009) revealed that a firm’s efficiency and success cannot be readily mimicked by competitors. For a small business operator, resources that lead to competitive advantages include generic strategies, entrepreneurial orientation, business location and productivity; which are a function of a firm’s asset, which could form the heterogeneity of a firm and its competitive advantages (Masakure et al., 2009).

Hypotheses Development

The Porter’s Generic Strategies (1980) comprises of three dimensions called cost leadership, differentiation and focus. The Cost Leadership Strategy is defined as being the leader in terms of cost in your industry or market with highest marker share. The Differentiation Strategy means making your products or services different from and more attractive than those of your competitors, which typically involve features, functionality, durability, support and brand image that the customers value. The Focus Strategy concentrate on particular niche markets by developing uniquely low-cost or well-specified products for the market.

A competitive or business strategy outlines how a business unit competes within its industry (Parnell, 2008). According to Porter (1980) framework, a business can pursue superior performance by either establishing a cost leadership position or differentiating its products and services from those of its rivals. Either approach may be accompanied by focusing efforts on a given market niche. Parnell (2011) revealed that there is positive and significant association between each of Porter’s strategy emphases cost leadership, differentiation and focus, and organizational performance. Parnell, Lester, Long, and Köseoglu (2012) revealed that SMEs which employ cost leadership strategies outperform than other business. Similarly, Parnell et al. (2012) has demonstrated that businesses achieve high performance when they concentrated on focus strategy. Hence the researcher concluded the hypothesis as below:

Hypothesis 1a: Cost leadership strategy has a significant positive effect on the business performance.

Hypothesis 1b: Differentiation strategy has a significant positive effect on the business performance.

Hypothesis 1c: Focus strategy has a significant positive effect on the business performance.

Entrepreneurial orientation (EO) has been found to relate to business performance. EO refers to “the process by which firms notice opportunities and act to creatively organize transactions between factors of production so as to create surplus value (Jones & Butler, 1992). Lumpkin and Dess (1996) defined EO as an attribute that represents an approach towards entrepreneurship and innovation, reflecting an ongoing processes and corporate culture. While many studies largely focus on large enterprises, Vora, Vora, and Polley (2012) has applied EO to a domestic US firm to investigate the applicability of EO in medium sized firm. Oly Ndubisi and Agarwal (2014) found that having greater emphasis on EO is a strategy that SME can apply in achieving great business performance. Wolff, Pett, and Ring (2015) found relationship between EO and firm performance in SMEs. Hence the researcher concluded the hypothesis as below:

Hypothesis 2: Entrepreneurial orientation has a significant positive effect on the business performance.

A location choice of a business is an important and strategic decision for long term success (Chou, Hsu, & Chen, 2008; Craig, Ghosh, & McLafferty, 1984; Timor & Sipahi, 2005; Muthuveloo and Teoh, 2014). A good business location attracts a large number of customers, allows ready access and impacts the business performance (Craig et al., 1984). Li and Liu (2012) found that the selection of a location contributed to the store performance. Lado-Sestayo, Otero-González, Vivel-Búa, and Martorell-Cunill (2015) showed that the performance of Spanish hotels in terms of profitability were highly depending on the location in which they operates. Thus, the hypothesis is offered as follow:

Hypothesis 3: Business location has a significant positive effect on the business performance.

Productivity can also be defined as the measure of how efficiently and effectively resources (inputs) are brought together and utilized for the production of goods and services (outputs) (Amah & Ahiauzu, 2013). Productivity orientation have been examined at the firm level (Marinova, Ye, & Singh, 2008), whereas individual level productivity orientation has been overlooked. Harris, Brown, Mowen, and Artis (2014) has introduced an individual construct, productivity propensity and revealed that productivity propensity is an important factor that impacts objective performance. Anitha (2014) and Muthuveloo & Rose (2005) found that there is a statistical significant impact of employee engagement on employee performance. Amah and Ahiauzu (2013) uncovered that there is significant positive relationship between employee involvement and business performance in terms of profitability, productivity and market share. Therefore the researcher concluded the hypothesis as below:

Hypothesis 4: Productivity propensity has a significant positive effect on the business performance.

University support is the moderating variable in this research. Forces in the complex external business environment can influence a business performance directly and indirectly; which include socio-cultural factors, economic development factors, technological factors, political factors and ecological factors. In this research, university support as a political factor is investigated to determine its influence on business operation within the university campus. María-Teresa, Galindo-Martín, and Ribeiro-Soriano (2012) mentioned that entrepreneurship is enhanced by the governance of a country. The government plays an important role in allocating the resources for entrepreneurship to grow and at the same time improve economic performance. Li, Zhou, and Si (2010) revealed that external environment has a positive moderating effect between innovation strategy and firm performance. Therefore the researcher concluded the hypothesis as below:

Hypothesis 5a: University support has significant moderating effect on the relationship between cost leadership strategy and business performance.

Hypothesis 5b: University support has significant moderating effect on the relationship between differentiation strategy and business performance.

Hypothesis 5c: University support has significant moderating effect on the relationship between focus strategy and business performance.

Hypothesis 5d: University support has significant moderating effect on the relationship between entrepreneurial orientation and business performance.

Hypothesis 5e: University support has significant moderating effect on the relationship between business location and business performance.

Hypothesis 5f: University support has significant moderating effect on the relationship between productivity propensity and business performance.

Research Methods

This study primarily assesses the business operators in USM campus. Business operator is the unit of analysis of this research. Quantitative was applied to identify the factors that impact business performance based on the theoretical framework. The measures of variables were adapted from past researches. The primary data is collected directly from the target respondents through the questionnaires, where the first-hand information is from the business operators in USM main campus. The researcher approached 95 available business operators in USM campus, however, there were only 75 business owners who were willing to fill up the survey questionnaires. In this research, 75 sets of questionnaire are sufficient to analyze the data as the population is small. The questionnaire contains the intended questions that measure the mentioned seven variables (four IVs, one MV and one DV), measuring the perceptions as well as the background of the firms and the respondents.

Findings and Discussion

This section elaborate on the findings of this study as below:

Findings

SPSS software and Smart Partial Least Squares Version 3.0 that comprised determination of descriptive statistics, testing of reliability, validity, and frequency distribution analysis are applied to perform preliminary data analysis. Structural equation modelling is applied to examine the validity and reliability as well for the proposed hypotheses. The summary of demographics is generated from SPSS while SmartPLS 3.0 is applied to clarify the relationships between the constructs that devote to the factors that impact business performance among business operations in USM.

Convergent validity was established through computing factor loadings, average variance extracted (AVE), composite reliability and Cronbach’s Alpha. Discriminant validity was computed to measure the correlation among dissimilar latent variables. Reliability of respective indicators of each constructs was evaluated through the loadings. The outer loading of each indicators that was lesser than 0.5 were dropped so that minimum value of AVE could be achieved. The rule of thumb for a good AVE reliability estimate was 0.5 or higher (Urbach & Ahlemann, 2010). For this research, several indicators had been dropped to achieve a good AVE. As a result, all the average variance extracted (AVE) and main loading were above minimum value of 0.5.

According to Sekaran and Bougie (2010) and Tavakol and Dennick (2011), the value of Cronbach’s Alpha value between 0.5 and 0.7 was considered at moderate whereas the value that was larger than 0.7 was considered as acceptable and reliable. Value of composite reliability should be above 0.7 (Akter, D'Ambra, & Ray, 2011). All composite reliability value were above 0.7. Thus, it could be concluded that an adequate reliability and validity level was supported in measurement of this study. Discriminant validity assessment was used to analyze relationships between latent variables. In this study, HTMT was used to assess discriminant validity. The HTMT value in result report has to be below 0.90 to ensure discriminant validity is established between two reflective constructs. Results for this study demonstrated that discriminant validity of the model had been verified.

A structural model analysis helped to test the hypothesized theoretical relationships among the constructs in this research conceptual framework. First of all, the path analysis outcome for Direct Relationships showed a value of 0.464 for R2, indicating that 46.4% of the variance in business performance would be justified by the independent variables. Similarly, the path analysis outcome for Indirect Relationship illustrated that the R2 for the indirect relationship had a value of 0.644, indicating that 64.4% of the variance in business performance would be justified by the independent variables and moderating variable. Next, the bootstrapping of SmartPLS was employed to obtain the result of hypothesis tests with 500 bootstrap subsamples. According to Hair et al. (2013), the values for one-tailed test are 1.645 and 2.33 for the significance level of 5% and 1% respectively. Table 01 presents the summary of the structural model of this study for the direct relationships.

Table 1 -
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The independent variables such as cost leadership (H1a: β = 0.344, t value = 3.018), differentiation (H1b: β = 0.299, t value = 2.658) and productivity propensity (H4: β = 0.347, t value = 4.033) had direct positive effects on business performance. In contrast, the hypothesis with independent variables such as focus (H1c: β = -0.015, t value = 0.129), entrepreneurial orientation (H2: β = -0.127, t value = 1.124), and business location (H3: β = 0.043, t value = 0.427) did not have significant effect on business performance but with focus strategy and entrepreneurial orientation being negatively related to business performance. Table 02 presents the summary of the structural model of the present research for the indirect relationship, in which university support acted as a moderating variable. In this research, two-tailed test was employed to study the influence of the moderator

Table 2 -
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The values for two-tailed test were 1.96 and 2.58 for the significance level of 5% and 1% respectively. University support (H5a: β = -0.353, t value = 2.44) had significant negative moderating effect on the relationship between cost leadership strategy and business performance. In contrast, university support did not have significant effect on the relationship between other independent variables such as differentiation strategy (H5b: β = 0.212, t value = 1.537), focus strategy (H5c: β = 0.351, t value = 1.782), entrepreneurial orientation (H5d: β = 0.042, t value = 0.278), business location (H5e: β = 0.042, t value = 0.398), and productivity propensity (H5f: β = -0.201, t value = 1.738) and business performance. The calculated effect size for this research model was 0.506 (large). GoF index in the present study was 0.541, implying that the model was in good fit.

Discussion

Hypothesis 1a suggested that cost leadership had positive effect on business performance. Statistical results from hypothesis testing revealed that cost leadership strategy was indeed positively and significantly influencing business performance (H1a: β = 0.344, t value = 3.018). Hypothesis 1a was supported. Cost leadership typically emphasized on cost reduction by having tight overhead control, general cost containment and aggressive construction of efficient-scale facilities (Köseoglu, Topaloglu, Parnell, & Lester, 2013). Many firms were concentrating on cost leadership strategy due to its greater short-term payoff (Koseoglu, Karayormuk, Parnell, & Menefee, 2011). This result was consistent with the previous study from Eraslan (2008), that there was a positive relationship between cost leadership strategy and firm performance. The finding of this research showed that a large proportion of respondents perceived that cost leadership strategy would help in achieving business performance. Some plausible reasons could be business operators in USM were to follow old selling price control by USM.

Hypothesis 1b suggested that differentiation strategy positively influenced business performance. Statistical results from hypothesis testing indicated that differentiation strategy was proven to have positive significant effect on business performance (H1b: β = 0.299, t-value = t value = 2.658). Hypothesis 1b was supported. This finding was consistent with previous studies by Parnell (2011) that there was a positive significant relationship between differentiation strategy and organizational performance in retail industry in United States. This was in lined with the Porter’s framework which a business can achieve superior performance by implementing differentiation strategy such as differentiating its products and services from its competitors. The outcomes of this empirical study demonstrated that apart from cost leadership strategy, differentiation strategy indeed applied a positive impact on business performance. Some plausible reasons could be the products and services the business operators could provide were determined upfront by USM according to business location. At every business location such as hostel cafeteria, every business operators were to provide different products and services. Therefore, it implied that if business operators provided products or services which were different from other business operators, the business performance would be enhanced.

Hypothesis 1c suggested that focus strategy had positive significant effect on business performance. However, statistical results from hypothesis testing showed that there was no significant positive effect of focus strategy on business performance (H1c: β = -0.015, t value = 0.129). Hence, Hypothesis 1c was not supported based on the data collected. This finding was contradicted with past studies from Parnell et al. (2012) which demonstrated that businesses in China achieved high performance when they concentrated on focus strategy. Nonetheless, the finding of this research was consistent with a study done by Parnell (2011), which stated that there was no significant relationship between focus strategy and business performance for retail industries in Argentina. Different industry might result in different strategic group. In this study, the probable reason of the negative relationship would be business owners found it difficult to integrate focus strategy into existing protective business environment with low uncertainty. The market mainly consisted of students and lecturers who had different spending power. With pre-determined price policy and products or services offered, business operators in USM did not have the freedom to focus on student market or lecturer market only.

Hypothesis 2 suggested that entrepreneurial orientation had positive significant influence on business performance. Statistical results from hypothesis testing showed that entrepreneurial orientation was negatively related to business performance and did not have significant impact on business performance (H2: β = -0.127, t value = 1.124). Therefore, Hypothesis 2 was not supported. This study uncovered that entrepreneurial orientation did not play an essential role in influencing business performance. This finding was contradicted with past studies from Jalali, Jaafar and Ramayah (2014) that proved that EO was positively related to the performance of SMEs. The finding was consistent with George, Robley Wood Jr, and Khan (2001) which the study was unable to find a significant relationship between entrepreneurial orientation and business performance. Several studies had demonstrated that level of education played a vital role in influencing the effect of entrepreneurial orientation on business performance (De Clercq & Arenius, 2006; Peters, 2002). Therefore, one apparent reason for the Hypotheses 2 to be rejected could be that highest education level of the majority respondents (53.33%) was secondary school only. Baycan Levent, Masurel, and Nijkamp (2003) had revealed that lack of education was an obstacle to entrepreneurship.

Hypothesis 3 suggested that business location had positive significant effect on business performance. Statistical results from hypothesis testing revealed that business location did not have positive significant effect on business performance (H3: β = 0.043, t value = 0.427). Hence, the Hypothesis 3 was not supported. This finding was contrasted with past studies which indicated that location was recognized as an element influencing business performance (Turhan, Akalın, & Zehir, 2013). Nonetheless, the finding was consistent with the study conducted by Barnard, Kritzinger, and Kruger (2011) that business location did not have significant effect on business performance as the nature of business determined where the business is located. In this research, all the business were located within university campus and 76% of the business operators are located at “Desasiswa”. Every location within USM campus had similar population characteristics, economic factors and indirect competition. In addition, all the business operators were assigned the business location by the university. Therefore, respondents perceived that business location within USM had no significant impact on business performance.

Hypothesis 4 had suggested that productivity propensity had a significant positive effect on business performance. Statistical results from hypothesis testing showed that productivity propensity indeed had positive significant effect on business performance (H4: β= 0.347, t value = 4.033). Thus, Hypothesis 4 was supported. This finding was in line with the past studies from Harris et al. (2014) that productivity propensity was an important factor that impacted objective performance. Respondents of this study were requested to determine the degree to which they perceived that they were motivated and productive in getting jobs done. It was observed that majority respondents were productive and enjoyed accomplishing job-related tasks in timely manner. Such findings were in tandem with prior studies that effort and motivation had positive impact on objective performance (Churchill Jr, Ford, Hartley, & Walker Jr, 1985).

Hypothesis 5a, 5b, 5c, 5d, 5e and 5f were used to test the indirect effect of university support. The hypothesis had suggested that university support had moderating influence between the factors (independent variables) and business performance. Statistical results showed that university support had a large effect size of 0.506. Although the effect size of university support was large, only Hypothesis 5a was supported (H5a: β = -0.353, t value = 2.44). Hypothesis 5a suggested that university support had significant moderating effect on the relationship between cost leadership strategy and business performance. The findings uncovered that university support had negative moderate effect on the relationship between cost leadership strategy and business performance. This implied that cost leadership strategy had low effect on business performance when university support was high. One probable reason for this scenario was that business operators in USM did not need to depend on cost leadership strategy to achieve business performance after getting support from the university. University support provided business operators better financial subsidies and business opportunities that indirectly enhanced business performance. The same reason may possibly resulted in other indirect effect (5b to 5f) to be insignificant.

Conclusion

This research seek to identify factors impacting business performance among business operators in USM by integrating all recommended variables and theory. Nonetheless, there were a few shortcomings pertaining to this particular study. The variance explained R2 for the variable business performance was 46.4%, indicating that a balance of 53.6% remain unexplained. Likewise in the research with moderator variable university support, the variance explained R2 was 64.6%, implying there are 35.4% balance that remain unaccounted for. All respondents that participated in this study were dominated by business which were operating within USM campus only. Therefore, some of the variables tested might not have impact on general industry. The sample acquired for the future study with the similar topic should be collected in various universities. Future researches could consider relooking into current theoretical framework to distinguish if any of the factors can be included or further modified to adapt to the latest business environment. Future studies could also consider modifying current research framework to investigate if there’s any perceived mediating factors or effects related to business performance.

From the academic perspective, the outcome of present research provided the in-depth information on the factors that enhance business performance among business operators in USM. This research enriched further Resource Based View (RBV) theory as well. The present research was expected to be viewed as a reference for future researchers, regulators, social and investors by providing useful information regarding business performance. In terms of practitioner perspective, the present research uncovered the insightful information, in which cost leadership strategy, differentiation strategy and productivity propensity had most positive significant influence on business performance. The findings could be used by the business owners to develop strategies that emphasize on the significant variables in order to achieve an outstanding business performance. Therefore, the findings helped business operators to attain sustainable competitive advantage. Additionally, the findings revealed that university support had negative moderating effect on the relationship of cost leadership and business performance.

References

  1. Akter, S., D'Ambra, J., & Ray, P. (2011). Trustworthiness in mHealth information services: an assessment of a hierarchical model with mediating and moderating effects using partial least squares (PLS). Journal of the American Society for Information Science and Technology, 62(1), 100-116.
  2. Amah, E., & Ahiauzu, A. (2013). Employee involvement and organizational effectiveness. Journal of Management Development, 32(7), 661-674.
  3. Ambrosini, V., & Bowman, C. (2009). What are dynamic capabilities and are they a useful construct in strategic management? International Journal of Management Reviews, 11(1), 29-49.
  4. Anitha, J. (2014). Determinants of employee engagement and their impact on employee performance. International Journal of Productivity and Performance Management.
  5. Barnard, S., Kritzinger, B., & Kruger, J. (2011). Location decision strategies for improving SMME business performance. Acta Commercii, 11, 111-128.
  6. Baycan Levent, T., Masurel, E., & Nijkamp, P. (2003). Diversity in entrepreneurship: ethnic and female roles in urban economic life. International journal of social economics, 30(11), 1131-1161.
  7. Chou, T. Y., Hsu, C. L., & Chen, M. C. (2008). A fuzzy multi-criteria decision model for international tourist hotels location selection. International Journal of Hospitality Management, 27(2), 293-301.
  8. Churchill Jr, G. A., Ford, N. M., Hartley, S. W., & Walker Jr, O. C. (1985). The determinants of salesperson performance: A meta-analysis. Journal of marketing research, 103-118.
  9. Craig, C. S., Ghosh, A., & McLafferty, S. (1984). Models of the retail location process-A Review. Journal of retailing, 60(1), 5-36.
  10. De Clercq, D., & Arenius, P. (2006). The role of knowledge in business start-up activity. International Small Business Journal, 24(4), 339-358.
  11. Eraslan, I. (2008). The effects of competitive strategies on firm performance: A study In Turkish textile and apparel industry considering the mediating role of value chain activities. Yayımlanmamış Doktora Tezi, Boğaziçi Üniversitesi, İstanbul.
  12. George, G., Robley Wood Jr, D., & Khan, R. (2001). Networking strategy of boards: implications for small and medium-sized enterprises. Entrepreneurship & Regional Development, 13(3), 269-285.
  13. Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Editorial-partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1-12.
  14. Harris, E. G., Brown, T. J., Mowen, J. C., & Artis, A. (2014). Exploring the role of productivity propensity in frontline employee performance: its relationship with customer orientation and important outcomes. Psychology & Marketing, 31(3), 171-183.
  15. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 1-21.
  16. Jalali, A., Jaafar, M., & Ramayah, T. (2014). Entrepreneurial orientation and performance: the interaction effect of customer capital. World Journal of Entrepreneurship, Management and Sustainable Development, 10(1), 48-68.
  17. Jones, G. R., & Butler, J. E. (1992). Managing internal corporate entrepreneurship: An agency theory perspective. Journal of Management, 18(4), 733-749.
  18. Koseoglu, M. A., Karayormuk, K., Parnell, J. A., & Menefee, M. L. (2011). Competitive intelligence: evidence from Turkish SMEs. International Journal of Entrepreneurship and Small Business, 13(3), 333-349.
  19. Köseoglu, M. A., Topaloglu, C., Parnell, J. A., & Lester, D. L. (2013). Linkages among business strategy, uncertainty and performance in the hospitality industry: Evidence from an emerging economy. International Journal of Hospitality Management, 34, 81-91.
  20. Lado-Sestayo, R., Otero-González, L., Vivel-Búa, M., & Martorell-Cunill, O. (2015). Impact of location on profitability in the Spanish hotel sector. Tourism Management, 52, 405-415.
  21. Li, Y., & Liu, L. (2012). Assessing the impact of retail location on store performance: A comparison of Wal-Mart and Kmart stores in Cincinnati. Applied Geography, 32(2), 591-600.
  22. Li, Y., Zhou, N., & Si, Y. (2010). Exploratory innovation, exploitative innovation, and performance: Influence of business strategies and environment. Nankai Business Review International, 1(3), 297-316.
  23. Lumpkin, G. T., & Dess, G. G. (1996). Clarifying the entrepreneurial orientation construct and linking it to performance. Academy of management review, 21(1), 135-172.
  24. María-Teresa, M., Galindo-Martín, M.-Á., & Ribeiro-Soriano, D. (2012). Governance, entrepreneurship and economic growth. Entrepreneurship & Regional Development, 24(9-10), 865-877.
  25. Marinova, D., Ye, J., & Singh, J. (2008). Do frontline mechanisms matter? Impact of quality and productivity orientations on unit revenue, efficiency, and customer satisfaction. Journal of Marketing, 72(2), 28-45.
  26. Masakure, O., Henson, S., & Cranfield, J. (2009). Performance of microenterprises in Ghana: A resource-based view. Journal of Small Business and Enterprise Development, 16(3), 466-484.
  27. Morrison, A., Breen, J., & Ali, S. (2003). Small business growth: intention, ability, and opportunity. Journal of small business management, 41(4), 417-425.
  28. Muthuveloo, R., & Rose, R. C. (2005). Typology of organizational commitment. American Journal of Applied Science, 2 (6), 1078-1081.
  29. Muthuveloo, R., & Teoh, A. P. (2013). Achieving Business Sustainability Via I-Top Model. American Journal of Economics and Business Administration, 5 (1), 15.
  30. Muthuveloo, R., & Teoh, A. P. (2014). Swim: Futuristic Framework For Strategic Management Process. American Journal of Applied Sciences, 1(9), 1703-1713.
  31. Muthuveloo, R. & Teoh, A. P. (2017). RACE: The Theory of Emergence for Strategic Entrepreneurship. International Review of Management and Marketing, 7(1), 164-168.
  32. Okpara, J. O., & Wynn, P. (2007). Determinants of small business growth constraints in a sub-Saharan African economy. SAM Advanced Management Journal, 72(2), 24.
  33. Oly Ndubisi, N., & Agarwal, J. (2014). Quality performance of SMEs in a developing economy: direct and indirect effects of service innovation and entrepreneurial orientation. Journal of Business & Industrial Marketing, 29(6), 454-468.
  34. Parnell, J. A. (2008). Strategy execution in emerging economies: assessing strategic diffusion in Mexico and Peru. Management Decision, 46(9), 1277-1298.
  35. Parnell, J. A. (2011). Strategic capabilities, competitive strategy, and performance among retailers in Argentina, Peru and the United States. Management Decision, 49(1), 139-155.
  36. Parnell, J. A., Lester, D. L., Long, Z., & Köseoglu, M. A. (2012). How environmental uncertainty affects the link between business strategy and performance in SMEs: Evidence from China, Turkey, and the USA. Management Decision, 50(4), 546-568.
  37. Peters, N. (2002). Mixed embeddedness: does it really explain immigrant enterprise in Western Australia (WA)? International Journal of Entrepreneurial Behaviour & Research, 8(1/2), 32-53.
  38. Porter, M. E. (1980). Competitive strategy: Techniques for analysing industries and competition. New York, 300.
  39. Sekaran, U., & Bougie, R. (2010). Research methods for business: A skill building approach. Wiley: London.
  40. SME Corp. Malaysia. (2014). SME Annual Report 2013/14.
  41. Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's alpha. International journal of medical education, 2, 53.
  42. Timor, M., & Sipahi, S. (2005). Fast Food Restaurant Site Selection Factor Evaluation by the Analytic Hierarchy Process. The Business Review, Cambridge, 4(1), 161-167.
  43. Turhan, G., Akalın, M., & Zehir, C. (2013). Literature review on selection criteria of store location based on performance measures. Procedia-Social and Behavioral Sciences, 99, 391-402.
  44. Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5-40.
  45. Vora, D., Vora, J., & Polley, D. (2012). Applying entrepreneurial orientation to a medium sized firm. International Journal of Entrepreneurial Behavior & Research, 18(3), 352-379.
  46. Wolff, J. A., Pett, T. L., & Ring, J. K. (2015). Small firm growth as a function of both learning orientation and entrepreneurial orientation: An empirical analysis. International Journal of Entrepreneurial Behavior & Research, 21(5), 709-730.

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Publisher

Future Academy

First Online

18.12.2019

Doi

10.15405/epsbs.2019.08.31

Online ISSN

2357-1330