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This paper aims to expand knowledge on strategy and business model transformation by exploring how the increased data availability can threaten the competitive positioning of data-based firms.

The study uses two longitudinal cases design. The data include a review of 270 business acquisitions performed by IBM and Yahoo! between 1995 and 2018 relying on publicly available documentation, corporate annual reports, shareholders presentations and press releases.

The study provides insights into how the availability of data can affect business models and the competitive advantage of data-based firms. Successful business model transformation in data-based firms appear to be contingent on dual-purpose mergers and acquisitions (M&As), oriented toward data and data-processing activities.

Inductive case studies yield results that require quantitative validation. The insights on business model transformation and M&As from this study were obtained within the context of data-intensive firms.

When formulating a growth strategy through M&As, strategists need to consider whether the current state of their competitive positioning requires single purpose acquisitions (e.g. data or data-processing capabilities) or dual-purpose acquisitions.

As data becomes a commoditized asset, further research and guidance are needed to assess the impact of this phenomenon on data-based business models. This study fulfills an identified need to gain insights into the relationship between business model transformation and M&A activity.

Access to personal data is key to many of the most successful recent business models. These models rely on individuals outside of traditional organizational boundaries as their product, content providers, and customers. The topic of organizational boundaries is central to organizational research, and these models raise questions about the permeability of these new forms’ boundaries. Herein I elaborate on data-based business models, the organizational field that has emerged around data governance issues, and the institutions that have formed around it at different stages, by various actors. I also explore the interplay of institutional field and organizational boundaries, to identify how field-level issues influence the permeability of organizational boundaries.

In light of the data economy, data protection law is a key legal element for being able to leverage data-driven innovation and is often regarded as a limitation for businesses and service design. Contrasting this traditional view, this chapter argues why designing with privacy in mind is a win-win situation, not only, but especially in the context of data-based services. On the backdrop of new regulations around the globe setting incentives, we show how research in the domain of usable privacy can be leveraged to embed innovative privacy features for customers into digital services as competitive advantage. Building upon these insights, we argue that a well-designed privacy and/or data protection process should be a key element for customer experience management.

Focusing on ethical issues when making organizational decisions should encourage a variety of positive outcomes for companies and their employees. The purpose of this paper is to determine the degree to which data-based ethical decision making, lateral relations and organizational commitment are interrelated in organizations.

Data were collected from business professionals employed at multiple locations of a financial services firm operating in the USA. Mediation analysis (based on structural equation modeling) was used to test the proposed relationships.

Results indicated that employees’ perceptions of data-based ethical decision making were positively related to perceived lateral relations, and that perceived lateral relations were positively related to organizational commitment.

Given that information was collected using only a self-report questionnaire, common method bias could be an issue. In addition, the study’s cross-sectional design limits conclusions about causality. Another limitation involves the study’s homogenous sample, which decreases the generalizability of the findings. Finally, variable responses could have been impacted by individual frames of reference and other perceptual differences.

Results suggest that information flow enhancements should support or be consistent with horizontal information flow enhancements, and that together these factors should increase employee commitment.

Given the dearth of existing research, this interdisciplinary investigation is important because it fills gaps in the management literature. This study is also important because the results could inform decisions regarding the use of data analysis in ethical decisions and lateral forms of organizational structuring to improve work attitudes.

Panel data-based demand forecasting models have been widely adopted in various industrial settings over the past few decades. Despite being a highly versatile and intuitive method, in the literature, there is a lack of comprehensive review examining the strengths, the weaknesses, and the industrial applications of panel data-based demand forecasting models. The purpose of this paper is to fill this gap by reviewing and exploring the features of various main stream panel data-based demand forecasting models. A novel process, in the form of a flowchart, which helps practitioners to select the right panel data models for real world industrial applications, is developed. Future research directions are proposed and discussed.

It is a review paper. A systematically searched and carefully selected number of panel data-based forecasting models are examined analytically. Their features are also explored and revealed.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. A novel model selection process is developed to assist decision makers to select the right panel data models for their specific demand forecasting tasks. The strengths, weaknesses, and industrial applications of different panel data-based demand forecasting models are found. Future research agenda is proposed.

This review covers most commonly used and important panel data-based models for demand forecasting. However, some hybrid models, which combine the panel data-based models with other models, are not covered.

The reviewed panel data-based demand forecasting models are applicable in the real world. The proposed model selection flowchart is implementable in practice and it helps practitioners to select the right panel data-based models for the respective industrial applications.

This paper is the first one which reviews the analytical panel data models specifically for demand forecasting applications. It is original.

Progress monitoring and data-based intervention are unique special education developments stemming from efforts to find an effective alternative to diagnostic/prescriptive instruction. Springing from research on Curriculum-based Measurement (CBM) in the late 1970s and early 1980s at the Minnesota Institute for Research on Learning Disabilities, the approach has generated a large body of empirical research and development. While the original work demonstrated that teachers could be more effective using progress monitoring in data-based intervention, most research and development activity has focused on development and extensions of the CBM model with less attention to data-based intervention. While research on progress monitoring has occurred at a high rate, widespread implementation of progress monitoring has been spurred by both federal funding and commercial development. As might be expected, all of this activity has resulted in a large set of successes and disappointments that are described here. For better or worse, as progress monitoring and data-based intervention have been incorporated into Response to Intervention (RTI) models it seems likely that the future of progress monitoring and data-based intervention is tied to the future of RTI. The question is whether this linking will result in adding to the set of successes or to that of disappointments for this unique special education innovation.

Two major trends – demographic shifts in the working-age population, and the proliferation of web technologies – are having a profound and generally unrecognized effect on the nature and characteristics of work, and on opportunities for the mature workforce. Key features of the workplace point to seven broad work trends. These trends have significant implications for organizations and for older workers. Six interdependent organizational changes are central to the far-reaching effects on enterprises and operating approaches. These changing work characteristics require certain essential behaviors for mature workers to be successful in the contemporary work environment. Such a dynamic workplace provides opportunity to introduce new thinking and propose new models. Realigning organizational and workforce interests calls for developing solutions beyond the individual level, reorienting enterprise capabilities, and reframing of the organization development practitioner role as work ecosystem advisor. High-leverage strategies and systemic interventions, such as multiconstituent initiatives and action research, can be used to influence constructively the multifaceted world of work.