Using Analytics To Examine Small Businesses: An Empirical Study On Business Analytics and Minority Business Enterprises
| Author | Derek |
This study examines market behavior of minority-owned business enterprises (MBE) and measures those characteristics with business analytics. The study provides an overview of the data sources and methodology used in the study, presents the key findings and discusses the implications of those findings.
The issues that affect minority business enterprises (MBE) have been well documented in the literature. Considering these issues, the business risks that minority entrepreneurs encounter are even more widespread. Business failure among minority businesses is extensive and well researched. In addition to gender bias, discrimination, minority businesses’ restricted access to capital has been well documented. The prior research on minority entrepreneurs and minority-owned businesses is extensive.
The rate of growth of minority business enterprises continues to grow dramatically in the U.S. State and federal public policies have been advocates of building the growth of MBEs. However, minority business enterprises still struggle and face issues that affect their success in the market place. The researcher wanted to investigate the problem with MBEs through empirical research and attempt to examine MBEs as a focus group.
This study is an extension of previous research on small business enterprises (SME). This study attempts to replicate the previous study by focusing specifically on minority business enterprises (MBE). The driving impetus of this study was to investigate if MBEs were unique in their behavioral characteristics; and to see if they were different from non-minority SMEs. In an attempt to further investigate this inquiry, this study used business analytics to measure MBE behavior.
This study was driven by three research questions: (a) what are the most significant factor analytics that are endemic to minority business enterprises (MBE); (b) how many factor analytics affect MBEs; and (c) how are the factor analytics different from non-minority SMEs?
This study builds on the prior research on minority business enterprises (MBE). The researcher will examine the market forces that affect the behavior of minority business enterprises. This study focuses on endogenous and exogenous forces and their effect on minority business enterprises. This study attempts to examine if MBEs are affected differently to endogenous and exogenous variables. This study will examine market behavior of MBEs and measure it through business risk analytics. This study is organized four sections: (a) background of the study; (b) literature review,; (c) methodology; (d) results; and (e) discussion and conclusions, which will elaborate on the findings of the study.
This study used the previous theoretical model from the prior study on SMEs. This research examined two factor analytics: endogenous and exogenous factors, based on 22 market and risk metrics with minority business enterprises (MBE). The risks factor terms are distinct and dissimilar in nature. The terms for market behavior patterns and risk patterns used for this research are the two major factors of risks [see Figure 1 in attached file]).
This study used a: (a) non-experimental, exploratory research design; (b) cross-sectional research design strategy, which attempts to collect quantifiable or quantitative data with two or more variables; and (c) survey approach using three strategies: web-based surveys, personal administration and mail questionnaires. A scaled instrument was used for this study. A 5-point Likert-type scale (1 = Strongly Disagree to 5 = Strongly Agree) and a 32-item instrument were used for the study. The Likert Scale was most appropriate for this study because: (a) the likelihood of producing a highly reliable scale; and (b) the capability to measure more data. These strategies attempt to maximize accessing the highest number of participants through multiple data sources and multiple sites (Zikmund, 2003).
The following theoretical model is presented in more detail with the specific variables for the study. The model that follows gives the both the 22 endogenous and exogenous variables and thus appropriated separated by the two factor analytics. The terms for market behavior and entrepreneurial risk variables is illustrated [see Figure 2 in attached file].
The model proposes there are two rudiment market factors of risk analytics to be critical in affecting minority business enterprises. Figure 2 illustrates the priori factors and number of variables within each factor. Later in the paper, a comparison between the theoretical model and the conclusive factor analysis results will be examined and discussed. This will be done to understand the compare and/or contrast differences between the statistical factor loadings and the theoretical model; and also to validate or dispute the structure of the theoretical model.
The prior studies on minority business enterprises (MBE) have been critical in developing a foundation of research. Early approaches to examining minority businesses have been rooted in some important research. In the last 10 years (2001-2010) some of the prior studies on minority businesses have focused on self-employment, inner city economic development, inner city businesses, firm survival and business failure. There are some notable researchers that have conducted extensive work on minority businesses have been Robb (2002); Fairlie and Meyer (2003); Bates (2006); Bates and Bradford (2004); Fairlie and Robb (2007); Handy, 2008; and Shane (2008); Bates and Bradford (2009); and Bates (2010). The line of research suggests that minority business enterprises have challenges that are obstacles to success.
This research is a continuation of the prior work on small business enterprises (SME). The researcher wanted to investigate market behavior patterns with minority business enterprises (MBE). Compared to previous studies examining minority owned businesses, this study attempts to take a different approach. The researcher attempts to provide a new inquiry to the previous research and add to the body of knowledge. The researcher found an under researched area: research focuses on examining market behavioral factors on minority business enterprises (MBE).
The use of data analytics is emerging. It is emerging in such fields as healthcare, which is a key discipline for healthcare finance. The using business intelligence for competitive advantage is strategic for the execution of business strategy (Giniat, 2011). Historically, the use of analytics with credit scores in small business lending by community banks is surprisingly widespread (Berger, Cowan & Frame, 2010).
The use of analytics can be forward looking with Customer Relationship Management (CRM) strategies. CRM can leverage customer intelligence created by CRM analytics that enhances CRM operations, and conversely, CRM operations collect critical customer data for CRM analytics (Chan, 2005). The use of analytics is critical in talent management. Conventional workforce planning typically utilizes metrics of people, process and production to recommend hire, reduction and development actions (Shen, 2011).
Analytics have also been used with Continuous assurance (CA) as a methodology for the analytic monitoring of corporate business processes Continuous analytic monitoring-based assurance will change the objectives, timing, processes, tools, and outcomes of the assurance process (Vasarhelyi, Alles & Kogan, 2004). The use of in-memory analytics technology can allow operational data to be held in a single database that can handle all the day-to-day customer transactions and updates, as well as analytical requests – in virtually real time (Acker, Gröne, Blockus, & Bange, 2011). In addition to the real gains in performance and speed offered by in-memory analytics, these new systems can significantly improve the quality of the business and customer intelligence they generate.
The use of metrics in the study of small-business success is important both for researchers and entrepreneurs. Financial performance is the most cited performance metric. The majority of these were in the financial area, with survival, competitive, and "other" variables also being used. An
(Weinzimmer & Manmadhan, 2009). The retail industry has a history of using analytics and metrics. Scientific methods can be applied to the revenue-driving areas of merchandise such as assortment, pricing, placement, and promotion to obtain further insight and make more precise decisions. In addition to the four Ps, there are other decision-making areas of the retail business that can benefit from data-driven analytics. These include placement of stores, allocation of labor, and the option to include services such as free shipping, gift wrapping, or layaway (Bata, Beard, Egri & Morris, 2011; Harikumar & Nagadevara, 2012).
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