Exploring Functional Affordances and Sensemaking in Human Resource Analytics

Interest and investment in Business Analytics (BA) have grown substantially the last decade and applying BA in the domain of Human Resource Management (HRM) has received much attention from practitioners. An increased understanding of what potential HR Analytics has and how this potential is recognized and used is of practical and academic importance. Using the lenses of Functional Affordances and Sensemaking from Information Systems (IS) literature, an explorative case survey is performed. Cases from conferences and publications are qualitatively analyzed. Twelve Functional Affordances of HR Analytics were identified along with exemplary Sensemaking Frames and Sensemaking Patterns. This explorative study provides insights into the Functional Affordances and Sensemaking mechanisms of HR Analytics on an organizational level. It contributes to prior IS research in terms of the potential use of HR Analytics and the Sensemaking mechanisms used to identify and develop these affordances

Psychology of Business Intelligence Tools: Needs-Affordances-Features Perspective

This study applied the Needs-Affordances-Features (NAF) framework to study psychological motivations behind the use of Business Intelligence (BI) tools especially when the use of such tools is voluntary. Our findings suggest that psychological needs motivate the use of BI tools that provide 13 affordances to fulfill five psychological needs, namely autonomy, competence, relatedness, having a place and self-realization. These affordances were identified through a review of six publicly available BI tools. This study posits that three groups of affordances––creation, collaboration, and communication––explain the relationship between psychological needs and applications of BI. This study generates important implications for BI research by providing an overarching framework for the affordances of BI tools as a whole and explaining the importance of psychological needs that motivate the use of BI tools. The results also provide a new lens and common vocabulary for future studies and design of BI tools

Affordance-Experimentation-Actualization Theory in Artificial Intelligence Research – A Predictive Maintenance Story

Artificial intelligence currently counts among the most prominent digital technologies and promises to generate significant business value in the future. Despite a growing body of knowledge, research could further benefit from incorporating technological features, human actors, and organizational goals into the examination of artificial intelligence-enabled systems. This integrative perspective is crucial for effective implementation. Our study intends to fill this gap by introducing affordance-experimentation-actualization theory to artificial intelligence research. In doing so, we conduct a case study on the implementation of predictive maintenance using affordance-experimentation-actualization theory as our theoretical lens. From our study, we find further evidence for the existence of the experimentation phase during which organizations make new technologies ready for effective use. We propose extending the experimentation phase with the activity of ‘conceptual exploration’ in order to make affordance-experimentation-actualization theory applicable to a broader range of technologies and the domain of AI-enabled systems in particular

Towards Design Principles for Data-Driven Decision Making: An Action Design Research Project in the Maritime Industry

Data-driven decision making (DDD) refers to organizational decision-making practices that emphasize the use of data and statistical analysis instead of relying on human judgment only. Various empirical studies provide evidence for the value of DDD, both on individual decision maker level and the organizational level. Yet, the path from data to value is not always an easy one and various organizational and psychological factors mediate and moderate the translation of data-driven insights into better decisions and, subsequently, effective business actions. The current body of academic literature on DDD lacks prescriptive knowledge on how to successfully employ DDD in complex organizational settings. Against this background, this paper reports on an action design research study aimed at designing and implementing IT artifacts for DDD at one of the largest ship engine manufacturers in the world. Our main contribution is a set of design principles highlighting, besides decision quality, the importance of model comprehensibility, domain knowledge, and actionability of results

Small fish in a big pond: an architectural approach to users privacy, rights and security in the age of big data

We focus on the challenges and issues associated with Big Data, and propose a novel architecture that uses the principles of Separation of Concerns and distributed computing to overcome many of the challenges associated with storage, analysis and integrity. We address the issue of asymmetrical distribution of power between the originators of data and the organizations and institutions that make use of that data by taking a systemic perspective to include both sides in our architectural design, shifting from a customer-provider relationship to a more symbiotic one in which control over access to customer data resides with the customer. We illustrate the affordances of the proposed architecture by describing its application in the domain of Social Networking Sites, where we furnish a mechanism to address problems of privacy and identity, and create the potential to open up online social networking to a richer set of possible applications