Amplifying organizational performance from business intelligence: Business analytics implementation in the retail industry.

• Author: Paulino, Emmanuel P.

INTRODUCTION

The perpetual revolution of information technology has genuinely dictated how industries conduct business today. Nevertheless, with the Fourth Industrial Revolution's advent, the fusion of technologies blurs the lines between the physical, digital, and biological spheres. It elevates the importance of data to become an integral part of conducting business (Beltran, 2018). Data have become the most important intangible resource of a firm, especially in the retail industry.

Retail trade in the Philippines has blossomed in recent years. The Philippine National Statistics Office (2019) counted 637,325 establishments engaged in retail trade in 2018. The Philippine retail industry has fragmented as different overseas companies enter the country. However, it is assumed that the industry will likely experience more significant consolidation and respond to incoming paradigm shifts (Rabo & Ang, 2018). Gomez, Arranz, and Cillan (2012) suggested that in order to be successful, it is critical for retailers to make use of its information resources efficiently and to pursue new strategies promptly. This may be achieved with the help of analytics in the retail supply chain (Gutierrez, 2014). In the Philippines, building customer loyalty is a primary goal for retailers. A retailer's ability to plan and implement measures toward customer retention may be provided by comprehensive business intelligence systems.

Beltran (2018) affirmed that there is still a gap between available technology and how firms use such technology to improve their business efficiency and customer response, both of which lead to better performance. It is where the concept of business analytics comes in. Several studies have already supported how BA generates Bl and how these technologies impact organizational performance. But even if some studies have proved that BA generates Bl, it can have different results for different situations and locations (Corte-Real, Oliveira, & Ruivo, 2016; Aydiner et al., 2019; Akter et al., 2016; Ashrafi et al., 2019). Current studies also lack exploration as to how each

BA capability generates Bl and its specific impact on the key performance metrics of marketing, finance, and business process performance. This study also finds rationale for several contradicting results found in the previous literature on the impact of Bl on organizational performance (Laursen & Thorland, 2010; Sharma et al., 2010; Aydiner et al., 2018; Bedeley et al., 2016; Grover et al., 2018).

Based on these gaps, this study aims to formulate a structural model that may predict and/or explain the quantitative relationship between business intelligence generated by BA capabilities and organizational performance, further translated to the key performance metrics of finance, marketing, and business efficiency. A survey was conducted on 62 retail companies in the Philippines that have already been implementing business analytics for at least three years. These companies represented by 124 respondents (one from top management and one analytics implementer for each company) already using business analytics are surveyed through questionnaires. This is the first empirical study in the Philippines to assess how business analytics and business intelligence impact organizational performance.

LITERATURE REVIEW

This study draws its framework from the knowledge-based view (KBV) of the firm, first theorized by Grant (1996) and extended by Kaplan et al. (2001). According to this theory, knowledge is the most significant intangible resource of a firm. It proposes a model that relates knowledge with the firm's capabilities by which it increases organizational performance. Such knowledge can be taken from internal and external sources. It also perceives the firm's resources as the key factors in its performance, thus suggesting that management should focus on harnessing internal capacities and capabilities rather than cogitating on external factors over which the firm has no control. Proponents of this view argue that organizations should focus on the inner strength of the company for its competitive advantage instead of comparing themselves with the competition. It is the knowledge that is the source of organizational performance (Wickramasinghe & Lubitz, 2007).

Business analytics and its capabilities

The earliest literature on business analytics (BA), such as Davenport and Harris (2007), defines it as a successive process of gathering, storing, analyzing, and interpreting meanings of data in order to improve decision-making and organizational performance. Definitions have somehow evolved through time as more organizations become more perceptive of what BA is all about. Stubbs (2013) defined business analytics as the generation of data-driven insight to produce value. It does so by requiring business relevancy actionable insight, performance measurement, and value measurement. Laursen and Thorlund (2017) concluded that BA goes beyond just providing intelligent reports. Min (2016) connected business analytics with various quantitative techniques such as statistics, data mining, optimization tools, and simulation. Defining BA becomes more extensive as time passes. BA capabilities that generate competitiveness can be perceived through customer and product dashboards (Glaister et al., 2008). These are real-time reports of the current customer engagement on the different products of the company. It concluded that BA's predictive ability could bring the right raw material and products to the company's delivery chain at the right time and place. BA is also seen to go beyond the advantages of traditional financial analysis (Ouahilal, El Mohajir, Chahhou, & El Mohajir, 2016), stating that with the explosion of data made through digital technology, data analysis has acquired greater prominence than mere financial accounting to be the basis for financial analysis. The most crucial capability of BA is its ability to support decisions based on data analysis. Through the inputs of data, the BA system can see through its algorithms the factors that cause different yields of business operations that guide managers in coming up with sustainable decisions (Glaister et al., 2008; Mithas et al., 2011; Ordanini & Rubera, 2009; Santhanam & Hartono, 2003; Ramanathan et al., 2017; Troilo et al., 2016.).

The researcher used the variables data dashboard, financial analysis, business process management, and decision support systems of business analytics based on the aforementioned literature.

Business analytics generating business intelligence

Sabherwal and Fernandez (2011) supported the idea that "organizations derive strategic decisions from hierarchical layers from data to intelligence." Business intelligence (Bl) is the outcome of careful analysis of data through the support of analytics technology (Grossman & Rinderle-Ma, 2015). It can be considered the result of the manifestations of technology, methodology, practices, systems, and techniques involved in analyzing data to help an organization understand its operations, leading to timely decisions. Foley and Gullemente (2011) concluded that Bl is a function of business analytics capabilities. Mishra, Hazra, Tarannum, and Kumar (2016) also supported such a conclusion by finding that business analytics significantly affects business intelligence, especially in decision-making activities. Chen et al. (2012) described BA as factors and part of Bl. The main difference between Bl and BA is the fact that BA is more specific in its focus, and an argument can be made that BA are factors of Bl (Mashingaidze & Backhouse, 2017). This description aligns with the relationships described in Kowalczyk and Buxmann (2014), Chen et al. (2012), and Williams (2016). Bl can be manifested through a descriptive understanding of the market. Firms can predict growth opportunities from internal and external data (Parra & Halgamuge, 2018). With the help of business analytics, companies are able to reflect on their internal strengths by deepening their knowledge of what is really happening inside the walls of their organization (Kearns & Sabherwal, 2007; Larson & Chang, 2016). Sabherwal (2007) suggests that Bl can be assessed through understanding customer preferences, coping with competition, identifying growth opportunities, and enhancing internal efficiency. Also, Larson and Chang (2016) confirmed that Bl is an enabler for the organization to work smarter, which rises from the fact that data analyses are turned into useful information. Bl-related factors indeed affect the perceived decision quality (Visinescu, Jones & Sidorova, 2016). Overall, Bl is seen when each executive in the organization confidently makes decisions backed up by rigorous data analysis (Grossman & Rinderle-Ma, 2015). Through the understanding of the discussion above, this hypothesis is therefore formulated:

H1: Business analytics capabilities are significant factors of business intelligence.

Business analytics and business intelligence linked with organizational performance

The importance of BA and Bl in improving corporate and organizational performance is well acknowledged in the literature (Wixom et al., 2013). Several pieces of literature provide evidence of a relationship between Bl, BA, and organizational performance. Price optimization and profit maximization are found to be outputs of comprehensive business intelligence (Davenport & Harris, 2007; Schroeck et al., 2012). Sales, profitability, and market share are greatly affected by analytics implementation (Manyika et al., 2011). Aydiner et al. (2018) found that business analytics capabilities generating business intelligence affect the overall business performance of the firm. According to Wixom et al. (2013), Bl can increase performance by increasing productivity, which has both concrete (i.e., reduced paper reporting) and intangible (i.e., improved business reputation) benefits. Thus, a firm that creates...