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This chapter uses multiple research methods, including quantitative science mapping analysis (SciMat) and a qualitative literature review, to provide insight into the academic debate unfolding at the intersection of big data and business processes. SciMat analysis based on keyword co-occurrence enabled identifying 12 of the most productive research themes, as reflected in a poll of 301 articles about big data and business processes. The three most important themes are: firm performance, Industry 4.0, and innovation. The traditional literature review on firm performance indicated that big data analytics (BDA) positively influence business process performance and have a beneficial impact on a firm’s performance, that is, the role of big data is viewed as critical in the context of Industry 4.0 because it enhances productivity and improves business processes. The benefits of BDA can be achieved only if the organizational obstacles related to planning, workforce attitude, and alignment with strategy are overcome. Moreover, big data is perceived as a significant source of innovation in an organization and can be conceptualized with the use of a resource-based view (RBV) of the firm. BDA positively influence business processes, which is strengthened by adequate implementation and openness to innovation.

There is a relationship between the organizations’ strategic objectives and their corporate communications. The latter is an important feature of organizational performance. Organizational leaders are continuously facing the challenge of communicating their strategic goals to their stakeholders. Very often, they are adopting performance management tools to meet this challenge. Consequently, this chapter explains that the Balanced Scorecard (BSC) can be used to evaluate and measure the firms’ corporate communications and their organizational performance. This tool has been widely recognized by academics and managers as it is capable of aligning organizational strategies (including their missions and visions), strategic indicators (leading and lagging indicators) and stakeholder management. A review of the relevant literature review suggests that many practitioners are becoming strategic in their corporate communications. In this light, this chapter clarifies that the BSC approach can be used to support them in their stakeholder engagement. This contribution is useful for both academics and practitioners as it aligns the corporate communication practices with organizational strategy and performance management in the digital era.

In this chapter, we apply diffusion of innovation theory and the theory of management fashion to examine the diffusion trajectory of human resource (HR) analytics in a U.S. context. We focus on the role mass media plays in influencing the diffusion process and address two research questions. First, does the mass media on HR analytics make observable the positive outcomes of HR analytics and is this related to increasing HR analytics adoption over time? Second, does the mass media on HR analytics show evidence of management trendsetting rhetoric?

We analyze published popular trade, business press, and peer-reviewed academic articles over a decade using a big data discourse analytical technique, natural language processing.

We find preliminary evidence that suggests that although the media has broadcasted positive outcomes of HR analytics, adoption has tailed off. In concert with the tailing off of HR analytic adoptions, the media appears to be recasting HR analytics as solving newer problems such as managing talent. Whether this shift makes a difference has yet to be determined.

Business press appears to influence the adoption process, both by broadcasting positive outcomes and through creating management fashion trendsetting rhetoric.

To promote the use of HR analytics, academic institutions and the HR profession need to train HR professionals in the use and benefits of HR analytics.

We lay the groundwork to improve our understanding of the role media plays in influencing how new HRM practices spread across organizations. We introduce the application of an emerging big data analytic technique, natural language processing, to analyze published media on HR analytics.

Micro, small, and medium enterprises (MSMEs) can play a significant role in achieving sustainable development goals (SDGs) as they have the ability to reduce unemployment. Digitalization helps MSMEs in a number of ways, including lowering transaction costs, quickening access to information, and bettering communication with extended supply chain members. This chapter aims to understand the level of digitalization in MSMEs in an emerging economy such as Bangladesh. MSMEs in Bangladesh account for 25% of the gross domestic product and employ 87% of civilians. This chapter builds on qualitative data from 60 MSMEs from various manufacturing and service sectors such as textile, retail, food delivery, IT companies, etc. The interviews were semi-structured and followed an interview protocol. The length of interviews varied between 40 and 50 minutes. Content analysis was used to analyze the data. Findings suggest that counterintuitively the level of digitization in MSMEs is not low in Bangladesh. Many micro and small enterprises use MS Excel to help them manage customer and product data. Medium Enterprises use Enterprise Resource Planning (ERP) software for planning enterprise-wide resources. Some medium enterprises also use powerful data analytics software such as Oracle, Power BI, Google Analytics, Python, and SPSS. Results also reveal barriers to digitization in MSMEs, which include a lack of employee awareness, training, and motivation of top management. This chapter maps the digitalization levels in MSMEs in Bangladesh and provides implications for SGDs. The chapter also presents policy recommendations for improving the digitalization level in emerging economies.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

Background: Insurance was discovered many centuries before Christ (BC). In the second and third millennia BC, Chinese and Babylonian traders traded risks. Insurance is now the backbone of the economy, but penetration is low in developing countries. Big data, internet of things (IoT), and InsurTech have recently ushered in the fourth industrial revolution in insurance.

Objective: This study examines the Indian challenges and solutions of using Big Data Analytics (BDA).

methodology: A SLR was used to extract themes/variables related to challenges and solutions in adopting BDA in the Indian insurance sector. Google Scholar was searched for relevant literature using keywords. Inclusion and exclusion criteria were used to filter the studies.

Findings: This study identified several barriers to BDA adoption in the Indian insurance industry. Policymakers could use the suggestions to improve insurance service delivery.

Practical implication: Insurers can understand the challenges, and accordingly, they can adopt the proposed solution in this study to enhance the insurance penetration in India.

Internet of things (IoT) and Big Data, which are among the pioneers of Industry 4.0 technologies, have gained great importance in recent years. Within the scope of Industry 4.0, organizations are trying to undertake digital transformation by adapting these two important technologies to their business processes. Undoubtedly, while this transformation provides great advantages for organizations in terms of management, organization, and marketing, it also carries disadvantages such as difficulties and complexity regarding the privacy of the collected data and systems. However, IoT and Big Data Analytics play a role as restructuring factors for products, services, and especially business processes. This study discusses the impact of IoT and Big Data Analytics on the digital transformation of organizations from the perspective of corporate culture, marketing, and management. Simultaneously, the effects of the COVID-19 epidemic that the world has experienced recently, on the business of institutions, are also discussed. By adopting IoT and Big Data Analytics, the attitudes, benefits, and challenges of the institutions that are or are not willing to realize digital transformation during the epidemic process are examined, and a projection is tried to be made to the post-COVID-19 period. While the study specifically highlights the positive effects of IoT and Big Data Analytics on the business, it sheds light on available opportunities and provides useful implications for managers and marketers.

Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

Introduction: In recent years, fresh big data ideas and concepts have emerged to address the massive increase in data volumes in several commercial areas. Meanwhile, the phenomenal development of internet use and social media has not only added to the enormous volumes of data available but has also posed new hurdles to traditional data processing methods. For example, the insurance industry is known for being data-driven, as it generates massive volumes of accumulated material, both structured and unstructured, that typical data processing techniques can’t handle.

Purpose: In this study, the authors compare the benefits of big data technologies to the needs for insurance data processing and decision-making. There is also a case study evaluation concentrating on the primary use cases of big data in the insurance business.

Methodology: This chapter examines the essential big data technologies and tools from the insurance industry’s perspective. The study also included an analytical analysis that supported several gains made by insurance companies, such as more efficient processing of large, heterogeneous data sets or better decision-making support. In addition, the study examines in depth the top seven use cases of big data in insurance and justifying their use and adding value. Finally, it also reviewed contemporary big data technologies and tools, concentrating on their key concepts and recommended applications in the insurance business through examples.

Findings: The study has demonstrated the value of implementing big data technologies and tools, which enable the development of powerful new business models, allowing insurance to advance from ‘understand and protect’ to ‘predict and prevent’.