CHAPTER 1 INTRODUCTION AND BACKGROUND 1

CHAPTER 1
INTRODUCTION AND BACKGROUND
1.0 Introduction
The study seeks to assess the effects of the adoption and diffusion of big data analytics on sales performance of Supermarket chains in Zimbabwe. The chapter will provide an outline comprising of the background to the study, the statement of the problem, objectives, significance of the study, limitations and delimitations, and provide a brief literature review of various authors on the subject area.
1.1 Background to the Study
Big Data Analytics (BDA), being an emerging technology, is used in many echelons of business and management. Organizations need to assimilate it in a full-scale and deep level to fully realize its benefits and therefore worthy of study (Crick, 2018). According to Salwa et.al (2016, p.55) “Small businesses have become major movers of the economy in most of the developing countries mobilizing local resources, engendering employment and making goods available to customers in the local markets”.
Big data, as the name suggests, refers to large datasets that are challenging to store, share, search, visualize, and analyse and here the orders of magnitude exceed conventional data processing and the largest of data warehouses. Big Data, coming from various sources, whether an airline jet collecting ten terabytes of sensor data for every half an hour of flying time, New York Stock Exchange collecting one terabyte of structured trading data each day, or a conventional structured corporate data warehouse sized in terabytes and peta-bytes, is sized in peta-, exa-, and even in zetta-bytes. It also signals that it is not just about volume, the approaches to analysis compete with data content and structure that can neither be anticipated nor predicted. There is a need to put proper analytics and the science behind them to filter low value or low-density data and divulge high value or high-density data. Also, big Data has a broad array of interesting architecture challenges and thus, new analytical techniques are required to adopt.

Usually data volume, velocity, and variety describe big data, but the unique attribute of Big Data is the manner in which the value is revealed. In conventional business intelligence, the simple summing of a known value reveals a result, such as order sales becoming year-to-date sales. But big data requires a refining modelling process to discover any value, i.e. making a hypothesis, creating statistical, visual, or semantic models, validating, and then making a new hypothesis. And for this, it either takes a person interpreting visualizations or making interactive knowledge-based queries, or by developing ‘machine learning’ adaptive algorithms that can discover business meaning (Agrawal, 2013). With the growth of big data, firms in emerging economies have also started investing in solutions that interpret consumer behaviour, detect fraud, and even predict the future using big data analytics (BDA).

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According to Rima (2015), it is no wonder that the marketing strategy has attracted numerous contributions from researchers and practitioners that attempt to develop frameworks for strategy formulation, antecedents of marketing strategy and factors mediating strategy and its outcomes. The small businesses essentially focus on utilizing local human and material resources and are adaptable to changing customer needs. They act as an important source of employment in the rural areas of the country. The role and contribution of these small businesses to the economy has become crucial.

Undoubtedly, the rise of big data in prominence has led to a titanic focus on exploring how organizations can harness information to gain a competitive advantage (Vellante, 2015). However, despite big data’s well documented benefits, it would be important to investigate, how organizations across the globe are putting it to use and in which way? Recently, Big Data Analytics (BDA) has emerged as a new technology to enhance overall efficiency of management through productivity, performance, and better decision-making of the organizations in real-time.
However, recent research in information management lack focus on BDA adoption which is just one part of an adoption process, and it cannot ensure wide-scale exploitation and usage of BDA. Therefore, without wide-scale adoption, the benefits of BDA cannot be fully realized. Thus, the adoption stages of assimilation are especially worthy of a focused study (Fichman, 2012, Zhu, Dong, and Kraemer, 2016a), especially in emerging economies, like China and India. The economic status and regulatory environment of such countries are different from developed countries where BDA technology already has established usage at huge level. Thus, it would be worthy to investigate how innovation assimilation gets influenced by contextual factors in such environments.
When compared to traditional analytics system, BDA is able to enhance the productivity and performance of organizations in real-time (Taghian, 2010). Diffusion of innovations can greatly accelerate adoption and utilization of Big Data, even though there are challenges faced by developing countries which limit capability and utilization of these technologies effectively. The number of big data projects and people using big data are increasing in developing countries indicating a diffusion of big data technologies. Diffusion of innovations can greatly accelerate adoption and utilization of Big Data, even though there are challenges faced by developing countries which limit capability and utilization of these technologies effectively.

Supermarkets sell millions of products to millions of people every day. It’s a fiercely competitive industry which a large proportion of people living in the developed world count on to provide them with day-to-day essentials. Supermarkets compete not just on price but also on customer service and, vitally, convenience. Having the right products in the right place at the right time, so the right people can buy them, presents huge logistical problems. Products have to be efficiently priced to the cent, to stay competitive. And if customers find they can’t get everything they need under one roof, they will look elsewhere for somewhere to shop that is a better fit for their busy schedule (Vellante, 2015).

1.2 Statement of the Problem
The rise of big data has led to a huge focus on exploring how organizations can harness information to gain a competitive advantage. McKinsey report (May, 2011) predicted a 60% margin increase for retail companies who are able to harvest the power of big data. However, despite the numerous big data benefits that are well documented, how many organizations across the globe are putting it to use and in which way? BDA is increasingly emerging as a new technology that increases overall efficiency of management and better decision-making. The study seeks to assess the effects of the adoption and diffusion of big data analytics on sales performance of Supermarket chains in Zimbabwe.

1.3 Research Objective
The overall objective of this study will be to investigate the value attached and derived (if any) by Zimbabwean super market chains from big data and analytics. Woerner and Wixom (2015,pp.60) state that the core issue that leads to limited usage of big data is the fact that although the data is perceived as potentially useful, the recording, measurement and review structures that have been adopted by organisations are merely designed to aid decision making (descriptive analytics). In contrast, such structures do not aid in something as interactive as real time customer relationship management, prescriptive and predictive analytics (Woerner and Wixom, 2015).
1.3.1 Research Objectives
To ascertain how Zimbabwean Supermarket chains are defining big data and data analytics?
To assess the extent to which Zimbabwean retail companies using big data analytics?
To ascertain the value Zimbabwean supermarket chains gain from using big data analytics?
To highlight the barriers for Zimbabwean Supermarket Chains in implementing big data analytics?
To analyze the techniques and technologies Zimbabwean Supermarket Chains are using for analytics and big data?
To evaluate the vendor products available to Zimbabwean Supermarket Chains?
1.4 Research Questions
How are Zimbabwean Supermarket chains defining big data and data analytics?
To what extent are Zimbabwean retail companies using big data analytics?
What value can Zimbabwean supermarket chains gain from using big data analytics?
What are the barriers for Zimbabwean Supermarket Chains in implementing big data analytics?
What techniques and technologies are Zimbabwean Supermarket Chains using for analytics and big data?
What vendor products are available to Zimbabwean Supermarket Chains?
1.5 Significance of the Study
The study will be of immense significance to the corporate executives and business managers who want to leverage their information assets to gain a comprehensive understanding of markets, customers, products, distribution locations, competitors, employees and more. Furthermore, the findings can also contribute to the growing body of literature in BDA in Zimbabwean Supermarket Chains. The creators and producers of BDA technologies will gain from the findings of the study by addressing the shortcoming arising from the use or non-use of the technology in the Zimbabwean setup. Producers of goods and services would be interested in the findings of this study as this motivates them to invest in BDA to help them extract data from surveys, purchases, web logs, product reviews from online retailers, phone conversations with call centers with an aim of developing a nuanced understanding of why certain products and services succeed and why others fail. They will also be able to spot trends that will help them feature the right products in the right marketing media.
Gandomi and Haider (2015), noted that academia has been slow to react on issues pertaining to big data and the monumental benefits that BDA will have for business across all sectors, especially in Africa and in particular the Zimbabwean Supermarket chains.

1.6 Conceptual Framework
161926167005003166110232410Dependent Variable
00Dependent Variable
546100232410Independent Variable
00Independent Variable

3257550199391Big Data Analytics Adoption
00Big Data Analytics Adoption
695325199390Technical Factors
Organizational Factors
Environmental Factors
00Technical Factors
Organizational Factors
Environmental Factors

304799933718500277050533083500
2143125154304Moderating Variable
00Moderating Variable

2181225186690Firm Characteristics
Size of firm
Age of firm
Personal Characteristics
Age
Educational level
Gender
00Firm Characteristics
Size of firm
Age of firm
Personal Characteristics
Age
Educational level
Gender

Source: Jameson (2015)
The model is based on Tornatzky and Fleischer (1990) because it has been used to examine a number of technological innovations. Here, companies are the adopter of the new technology and BDA adoption is influenced by technical, managerial and environmental factors. The measurable technical variables will include technological competence and technical capacity; organizational variables will have the top management support, management culture and perceived financial cost as predictors that may influence the management of supermarkets to adopt BDA in their operation. Finally environmental variables will have competitive pressure, regulatory or government policy and data security and privacy as predictors of adoption. Firm characteristics such as firm size and age of the firm, personal characteristics such as level of education, gender and age of the workers may indirectly influence the adoption of BDA in companies.

1.7 Limitations of the Study
The limitations relates to the descriptive method that will be employed in this study. This method lacks predictive power. The research may discover and describe “what is” but is unable to predict “what would be.” The respondents may give false responses thereby affecting the validity of the findings.
Over and above the topic of big data analytics is a heavily debated issue. Particularly on the ethical view of how data is collected, interpreted, stored and disseminated. Although vitally important this study will not tackle such issues. However, Ncube (2016), highlights that there is a general lack of knowledge about legal protection of privacy. In addition, the Zimbabwean legislative framework does little to mitigate against this short coming because it is currently inadequate (Ncube, 2015).

With consideration of the nature of the study and complexity of the topic, gaining access to the right people to give insight on the topic requires time, in some cases months, time frames which are not practical with the current research schedule. Furthermore, given that fact that participants need to have a degree of knowledge on data analytics, consumer behavior and consumer engagement strategies in the Zimbabwean super market chains, the population of eligible and credible participants was very small.

1.8 Delimitations of the Study
The researcher delimited the investigation to establishing the the effects of the adoption and diffusion of big data analytics on sales performance of Supermarket chains in Zimbabwe.

1.9 Definition of Terms
Descriptive analytics are techniques used to describe and report on past events.

Prescriptive analytics is the set of techniques which allow to determine the best way to take based on a set of requirements and with the objective of improving business performance.

Predictive analytics are a combination of techniques used in statistical models and empirical methods on past data to create empirical predictions about the future or model the impact of one variable to another.

Big data analytics is the process of examining large and varied data sets i.e., big data, to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions.

1.10 Tools for Analyzing Big Data
There are five key approaches to analyzing big data and generating insight:

• Discovery tools are useful throughout the information lifecycle for rapid, intuitive exploration and analysis of information from any combination of structured and unstructured sources. These tools permit analysis alongside traditional BI source systems. Because there is no need for up-front modeling, users can draw new insights, come to meaningful conclusions, and make informed decisions quickly.

• BI tools are important for reporting, analysis and performance management, primarily with transactional data from data warehouses and production information systems. BI Tools provide comprehensive capabilities for business intelligence and performance management, including enterprise reporting, dashboards, ad-hoc analysis, scorecards, and what-if scenario analysis on an integrated, enterprise scale platform.

• In-Database Analytics include a variety of techniques for finding patterns and relationships in your data. Because these techniques are applied directly within the database, you eliminate data movement to and from other analytical servers, which accelerates information cycle times and reduces total cost of ownership.

• Hadoop is useful for pre-processing data to identity macro trends or find nuggets of information, such as out of-range values. It enables businesses to unlock potential value from new data using inexpensive commodity servers. Organizations primarily use Hadoop as a precursor to advanced forms of analytics.

• Decision Management includes predictive modeling, business rules, and self-learning to take informed action based on the current context. This type of analysis enables individual recommendations across multiple channels, maximizing the value of every customer interaction. Oracle Advanced Analytics scores can be integrated to operationalize complex predictive analytic models and create real-time decision processes.

1.11 Summary
This chapter presented the introduction and background of the study. The objectives of the study were laid out. The next chapter will present review of related literature.
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