It is people, not technology, who make sense of data and give it meaning. This means that business intelligence resides not in the data warehouse but in the minds of people.
Due to the rapidly increasing popularity of business analytics (BA), investigation of the antecedents/determinants of the adoption of BA and the subsequent impact of the same to the firm performance has become an important research topic.
Drawing on the fundamentals of the resource-based view (RBV), this study proposes a model that examines the effects of the BA adoption on organisational performance and the moderating role that strategy and team plays in the relationship between the adoption of BA and firm performance.
When data are scarce, expensive to obtain, or not available in digital form, it makes sense to let well-placed people make decisions, which they do on the basis of experience they’ve built up and patterns and relationships they’ve observed and internalized.
Intuition is the label given to this style of inference and decision making.
People state their opinions about what the future holds what’s going to happen, how well something will work, and so on and then plan accordingly.
For particularly important decisions, these people are typically high up in the organization, or they are expensive outsiders brought in because of their expertise and track records. Many in the big data community maintain that companies often make most of their important decisions by relying on HiPPO the highest-paid person’s opinion.
Rapidly changing globalized business environment coupled with the unprecedented advancements in technology fronts enforce firms to become more innovative and agile in the way they identify and respond to their customers’ evolving needs and wants.
Success or mere survival depends on these businesses’ ability to effectively/accurately and efficiently quickly respond to the complex dynamics in the global marketplace.
Thus, information systems (IS) and information technologies (IT) become the metaphors that provide different tools and techniques to the businesses that intend to overcome the challenges of these environments (Sharda, Delen, & Turban, 2016). Recently, firms have been able to access to huge data generated through their operations undertaken in electronic platforms.
It is also worthwhile to recognize the role of IT penetration into businesses to generate more digitalized firms that collect various types of structured and unstructured data. Availability and accessibility of these large data sets foster the importance of IS/IT techniques to understand the business environment and markets for the firms striving for making meaningful business decisions to create a competitive advantage (Bichler, Heinzl, & van der Aalst, 2017; Sharma, Mithas, & Kankanhalli, 2014).
IS have numerous applications that involve various tools and techniques to deal with the processing of extensive data sets. To add value and to support/drive decisions for businesses, these tools and techniques statistically and quantitatively analyze a huge collection of data sources, and are collectively called business analytics (BA) nowadays (Delen & Zolbanin, 2018).
They are aimed at dealing with the big data phenomenon (ever-increasing volume, variety, and velocity of data) compiled by organizations and also end users (Sharda et al., 2016). Largely due to its promise, investments on the BA enablers are constantly and exponentially growing in recent years, and the expenditures to these tools by businesses have been reaching billions of dollars.
They are among the most prioritized expense-worthy tools and applications by especially medium-level and high-level managers (Cosic, Shanks, & Maynard, 2015). According to the study conducted by Accenture and General Electric, 89% of firms believe that they might lose their market if they do not adopt big data and BA (Columbus, 2014).
Despite this growing popularity of BA, there is an ambiguity about how the adoption of BA impacts firm performance (FP) (Akter, Wamba, Gunasekaran, Dubey, & Childe, 2016; Ramanathan, Philpott, Duan, & Cao, 2017; Sharma et al., 2014; Troilo, Bouchet, Urban, & Sutton, 2016).
Although investments on the BA enablers are constantly and exponentially growing in recent years and the expenditures to these tools by businesses have been reaching billions of dollars. Despite this growing popularity of BA, there is an ambiguity about how the adoption of BA impacts firm performance.
Various anecdotal research suggests that insight-generation and decision-making processes associated with the use of business analytics often do not involve key stakeholders from functional areas who will be responsible for implementing those decisions (Shanks et al, 2010; Shanks and Sharma, 2011). Although cross functional teams are often employed to work with business analytics, key stakeholders who own the resources required to implement decisions are often not a part of those teams.
Researchers have also noted that technological, managerial, and human factors contribute to the BI problems or failure in an organisation (Moss, Atre 2003; Stangarone, 2014).
While much discussion has focused on the ability of business analytics to generate better insights and decisions, the focus on the potential of business analytics to capture value has been limited. While high-quality decisions may be a good starting point, it is by no means certain that those decisions will be successfully implemented.
Indeed, prior research argues for at least two criteria characterising good decisions. One criteria refers to the quality of the decision, that is, whether the decision is capable of achieving its objectives; the other refers to the acceptance of the decision, that is, its acceptance by subordinates and other stakeholders responsible for the successful implementation of the decision (Drucker, 1967; Vroom and Yetton, 1973; Sutanto et al, 2008 2009).
While IT has greatly expanded opportunities to collect, store and process data, how to improve the use of data to make informed, fact-based decisions needs to be seen through the lens of people, not technology. It requires a deeper understanding of how, through the use of data, insight is generated. Just as providing someone with a hammer does not make a carpenter, deploying IT tools does not automatically improve decisions or the process of knowledge discovery.
Data matters. Not everything that can be counted counts and not everything that counts can be counted attributed to Albert Einstein.
The ability to take data to be able to understand it, to process it, to extract value from it, to visualize it, to communicate it thats going to be a hugely important skill in the next decades . . . Hal Varian, Chief Economist, Google
Just as it is critical to generate meaningful insights, it is as vital to transform insights into decisions that can create value. Insights, which refer to deep and intuitive understanding of phenomena, need to be leveraged by analysts and managers into strategic and operational decisions to generate value (Sharma et al, 2010; Lycett, 2013).
Vroom and Yetton (1973) suggest that the level of influence and participation that subordinates and key stakeholders have on a decision has an important bearing on its acceptance and, presumably, its successful implementation.
BA covers a broad range of applications, technologies, and processes related to collecting, storing, retrieving, and analyzing big data (Bayrak, 2015). As a part of the BA development process, big data indicate the complexity of the unstructured huge amount of data that are only possible to analyze and understand with special tools, such as BA (Bayrak, 2015).
Chae et al. (2014) pointed out that BA extensively used data, statistical and quantitative analysis techniques, as well as explanatory and PRED models using mathematical and computer-based algorithms to gain insight about business operations (Appelbaum et al, 2017). BA helps to build up a fact-based management system (Bayrak,2015; Holsapple, Lee-Post, & Pakath, 2014), and it is explained as a set of business and technical activities with a collection of tools for manipulating, mining, and analyzing environments (Sharda et al., 2016; Sun, Strang, & Firmin, 2017).
According to W?jcik (2015), a competitive business strategy in the future will concentrate on organizational renewal capability, where BA enhancing learning experience and adopting the knowledge discovered may lead the organizations to revitalize their businesses and gain significant performance improvements (Ramanathan et al., 2017).
Research Objectives
Research Questions
The RBV is mainly built around the idea of developing abilities to utilize resources for the achievement of competitive advantage (Barney, 1991; Cosic et al., 2015; Delen & Zolbanin, 2018; Gunasekaran et al., 2017).
Some organizations are more successful than others in the process of resource accumulation and resource deployment to create distinct capabilities (Peppard & Ward, 2016). To gain sustainable competitive advantage through these distinctive capabilities, resources should be valuable, inimitable, rare, and non-substitutable (VIRN) (Cosic et al., 2015; Gunasekaran et al., 2017; W?jcik, 2015).
In terms of BA adoption, data are considered as one of the key resources for an organization to capture, harness, and understand its business operations to improve.
According to the International Institute for Analytics, by 2020, businesses using data will see $430 billion in productivity benefits over competitors who are not using data.
Companies succeed in the big data era not simply because they have more or better data, but because they have leadership teams that set clear goals, define what success looks like, and ask the right questions. Big datas power does not erase the need for vision or human insight.
On the contrary, we still must have business leaders who can spot a great opportunity, understand how a market is developing, think creatively and propose truly novel offerings, articulate a compelling vision, persuade people to embrace it and work hard to realize it, and deal effectively with customers, employees, stockholders, and other stakeholders. The successful companies of the next decade will be the ones whose leaders can do all that while changing the way their organizations make many decisions.
Research Framework
Moderating Variables
Hypothesis
H1: Strategy and team are the important factors for successful implementation of business analytics
H2: Successful implementation of business analytics optimises firm performance.
Research Design & Methods
Positivism has been defined as a means which makes it possible to study reality objectively by employing the methods of the natural sciences (Angus, 1986; Marshall, 1999).
It is appropriate to use the post positivism philosophy in the current research because the objectives and questions concern the scientific knowledge that is needed to be explored.
Research type
The type of research is cross sectional.
Research Approach
The deductive approach has been used to carry out this research.
Design
The research design will be quantitative and questionnaires will be used. Questionnaires can be easily conducted and do not consume much time and effort to administer (Pedhazur, Schemelkin, 1991).
The questionnaire contains 31 questions: the first two questions are merely for respondents demographics; the remaining 29 questions are related to the corresponding research questions. A five (5) point Likert scale was used to measure the responses on the strategic planning and employee involvement.
According to Mertens (2005, p. 4), the target population is a group of persons who participate in a survey and whose answers are used to generalise the results. In the case of the current thesis, the managers in different departments are involved in data handling and the implementation of BI to improve the performance of their departments so that they can achieve their organisational objectives.
Approximately 100 middle and senior level managers in banking, telecom and IT companies using BI system will be studied and taken as the target population.
Simple random sampling:
Data Collection
The pretested administered questionnaires will be sent via mail to the managers through professional networks and groups
Data Analysis
Descriptive analysis will be conducted to compute the mean and standard deviations for the thesis variables. Data analysis will be conducted by SPSS.
Outcome of Research
This section provides an outline on the process of obtaining the results of the thesis, as well as on their relevance. The thesis describes the research questions and the level of the expected results. The research is expected to provide an overview picture of the critical driving factors that is strategy and team that lead to the successful implementation of BI while increasing organisational performance.
Managerial Implications
The research is expected to provide an overview picture of the critical driving factors that lead to the successful implementation of BI. BI technologies help the organization to have a competitive advantage over other organizations since the analyzed information from the relevant raw data helps the management to make decisions on best approaches that will help them gain market strength and thus compete better with other organizations.
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