Modeling the Critical Success Factors for Business Intelligence Implementation: An ISM Approach

Introduction

The swift upsurge in the volume of data in all the organizations and its significance for management decision making has made it apparent that determining the most relevant factors concerning BI adoption has a profound influence on the choice to engage them. (Hou, 2013, 2014). To survive in today's unpredictable circumstances, companies are gradually endeavouring to create, gather, and change their data into information (Delen & Demirkan, 2013). Business intelligence systems have been progressively accepted in organizations while comprehending the characteristics of impacting factors on such adoption decisions requires getting ample academic attention. Business Intelligence and Analytics have developed as an essential area of study for both practitioners and researchers, showing the degree of influence of data-related problems on modern business organizations(Chen et al.,2012).

Business Intelligence (BI) term refers to a combination of architecture, databases, data warehouses, analytical tools, and applications (Sharda et al., 2017). It has been noted by some researchers that business intelligence (BI) is designed to give various corporates specific solutions suiting their needs (Martins, Oliveira, & Popovič, 2014; Petrini & Pozzebon, 2009). Vukšić, Bach & Popovič (2013) alleged that BI is used to analyse the accessible information and turned them into valuable knowledge to abate informational needs. Previous studies have ultimately shown the importance of using BI and are among the main concerns of most CIO's, i.e., chief information officers in organizations (Howson, 2007; Jones, et.al 2012).

BI implementation for any corporate is a considerably long process and continues for an extended period. To deal with problems arising in the process, holistic knowledge and various organizational factors play a vital role(Melody et al., 2010). However, a strong, dedicated, and adaptive leadership style can implement BI regardless of any obstacles (Melody et al., 2010). The present study is related to these organizational factors responsible for the successful implementation of Business Intelligence systems. This research focuses on studying interactions among these factors, using Interpretive Structural Modelling (ISM). The ISM approach has been used to generate cognizance, and provide an improved understanding of the critical success factors and added to the existing literature on Business Intelligence. Research studies have been steered earlier in India, detailing different factors and variables impacting different sectors. However, there is no known research on success factors related to Business intelligence done through ISM methodology.

ISM (Interpretive Structural Modelling) is an extensively used methodology in several fields and other complex systems because it converts complex problems into precise structural models (Luthra et al., 2014). ISM methodology can propose understanding the complex interactive relationships among an intricate system's factors and, therefore, overcome the restrictions and complications of traditional approaches, such as weighted score (Shen et al., 2016) and structural equation modeling (Tarka, 2018). Studies on complicated systems have applied ISM methodology as it offers better comprehension of interrelationships among variables (Luthra et al., 2014), grow understandings (Shen et al., 2016), recognize focus areas (Kumar et al., 2018), and supports policy analysis (Attri et al., 2013). Therefore, ISM delivers an efficient and suitable technique (Luthra et al., 2014; Gan et al., 2018) to develop a structural model for a multifaceted system and improve system behaviour understanding. In this study, the authors have identified the BI success factors and have utilized the ISM methodology to build and understand interrelationships between them, followed by MICMAC analysis. The objectives of the study undertaken, thus, are as follows:

• • To identify the success factors of business intelligence implementation

• in organizations.

• • To unearth the interrelationships among the success factors using ISM methodology.

• • To identify the driving power of various success factors of BI implementation using MICMAC analysis.