HOW BUSINESS INTELLIGENCE CREATES VALUE

Assessing IT business value has long been recognized as a major challenge, stemming largely from the considerable variability in the role and contribution of IT. This study examines the business value associated with business intelligence (BI) systems, suggesting that business value assessment is largely contingent on system type and should consider its unique contribution. The study adopts a process-oriented approach to evaluating the value contribution of BI, arguing that it stems from the improvement of business processes. The study develops and tests a research model that explains the unique mechanisms through which BI creates business value. The model draws on the resource-based view to identify key resources and capabilities that determine the impact of BI on business processes and, consequently, on organizational performance. Furthermore, the research model seeks to analyse the manner in which the organizational approach to innovation moderates the business value of BI. Analysis of data collected from 159 managers and IT/BI experts, using Structural Equations Modelling (SEM) techniques, shows that BI largely contributes to business value by improving both operational and strategic business processes. Further, it highlights the effect of the organizational approach toward exploration on transforming BI resources into capabilities and further into business value

Drivers of Business Intelligence-based Value Creation: The Experts’ View

The field of business intelligence (BI) has become increasingly important in both research and practice in recent years. However, research on the business value of BI is still scarce. This study investigates the factors influencing how BI creates business value. Through an exploratory study, we con-ducted interviews with 16 BI experts from different industries. The experts highlighted four significant drivers of BI-based business value creation: (1) building a business case, (2) formulating a BI strate-gy, (3) data governance, and (4) organizational adaptability. In addition, this study outlines how BI creates business value. Research gaps and suggestions for future research are also presente

Value Drivers of Artificial Intelligence

Artificial intelligence (AI) holds great potential for firms to create new business models and gain competitive advantages. While some pioneers are effectively leveraging AI, most firms are struggling to capitalize on the opportunities for value creation. Previous research has highlighted the performance benefits, success factors, and challenges of adopting AI. However, the value drivers of AI, specifically regarding how AI creates value, remain unclear and need exploration so that firms can adapt their value creation to leverage the potential. To clarify how AI creates value, we conduct a case survey of 61 firms to identify six value drivers: efficiency, novelty, knowledge from data, ecosystem, personalization, and human resemblance. We discuss how these value drivers differ from other digital technologies. For practitioners, we provide valuable insights into the business value of AI and business model (BM) design opportunities to build on

Understanding Kindness – A Moral Duty of Human Resource Leaders

The role of leaders in the modern organization has evolved as scholars and practitioners have recognized that a key element to long-term profitability is the creation of high trust and high commitment work systems that treat employees as valued partners (Kim & Wright, 2011; Block, 2013; Beer, 2009; Caldwell & Floyd, 2014). Effective leaders create aligned organizational cultures with systems, processes, practices, and programs reinforcing the organization’s espoused values in achieving its mission (Schein, 2010). Human resource professionals (HRPs) play a critical leadership role in ensuring that human resource management (HRM) cultural elements are properly integrated, communicated effectively to employees, and followed in a manner that builds trust and increases commitment (Lengnick-Hall, 2009; McEvoy, et al., 2005). The purpose of this paper is to identify the importance of kindness as a moral duty of HRPs in serving their organizations and the employees within them. As HRPs perform their strategic and operational roles in the modern organization, properly understanding the nature of kindness is an important factor in carrying out HRM roles. This paper begins by defining kindness and its specific application to HRPs — equating the definition of kindness as a leadership trait with six elements of kindness and seven kindness-related ethical perspectives. The paper concludes with a summary of its contribution for HRP practitioners and scholars in understanding the nuances of kindness as a morally-and ethically-related HRM leadership virtue

Big Data in the Oil and Gas Industry: A Promising Courtship

The energy industry remains one of the highest money-producing and investment industries in the world. The United States’ own economic stability depends greatly on the stability of oil and gas prices. Various factors affect the amount of money that will continue to be invested in producing oil. A main disadvantage to the oil and gas industry is its lack of technological adaptation. This weakens the industry because the surest measures are not currently being taken to produce oil in optimally efficient, safe, and cost-effective ways. Big data has gained global recognition as an opportunity to gather large volumes of information in real-time and translate data sets into actionable insights. In a low commodity price environment, saving time, reducing costs, and improving safety are crucial outcomes that can be realized using machine learning in oil and gas operations. Big data provides the opportunity to use unsupervised learning. For example, with this approach, engineers can predict oil wells’ optimal barrels of production given the completion data in a specific area. However, a caveat to utilizing big data in the oil and gas industry is that there simply is neither enough physical data nor data velocity in the industry to be properly referred to as “big data.” Big data, as it develops, will nonetheless significantly change the energy business in the future, as it already has in various other industries.Petroleum and Geosystems Engineerin

Mapping domain characteristics influencing Analytics initiatives: The example of Supply Chain Analytics

Purpose: Analytics research is increasingly divided by the domains Analytics is applied to. Literature offers little understanding whether aspects such as success factors, barriers and management of Analytics must be investigated domain-specific, while the execution of Analytics initiatives is similar across domains and similar issues occur. This article investigates characteristics of the execution of Analytics initiatives that are distinct in domains and can guide future research collaboration and focus. The research was conducted on the example of Logistics and Supply Chain Management and the respective domain-specific Analytics subfield of Supply Chain Analytics. The field of Logistics and Supply Chain Management has been recognized as early adopter of Analytics but has retracted to a midfield position comparing different domains. Design/methodology/approach: This research uses Grounded Theory based on 12 semi-structured Interviews creating a map of domain characteristics based of the paradigm scheme of Strauss and Corbin. Findings: A total of 34 characteristics of Analytics initiatives that distinguish domains in the execution of initiatives were identified, which are mapped and explained. As a blueprint for further research, the domain-specifics of Logistics and Supply Chain Management are presented and discussed. Originality/value: The results of this research stimulates cross domain research on Analytics issues and prompt research on the identified characteristics with broader understanding of the impact on Analytics initiatives. The also describe the status-quo of Analytics. Further, results help managers control the environment of initiatives and design more successful initiatives.DFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli