Guidance in Business Intelligence & Analytics Systems: A Review and Research Agenda

While the data amount grows exponentially, the number of people with analytical and technical skills is only slowly increasing. This skill gap is putting pressure on the labor market and increasing the need for personnel with these skills. At the same time, companies are forced to think of alternative ways to empower their less-skilled workforce to take on Business Intelligence and Analytics (BI&A) tasks. One promising attempt to address these challenges may turn to the concept of guidance. However, the current body of research on guidance in BI&A systems is scattered and lacks a structured investigation from which future research avenues can be derived. To address this gap, this article analyzes five categories, namely BI&A phases, guidance degree, guidance generation, user roles, and interactivity form. Reviewing 82 articles, our contribution is to synopsize articles on guidance in BI&A systems and to suggest five research avenues

Towards a situation-awareness-driven design of operational business intelligence & analytics systems

With the swamping and timeliness of data in the organizational con-text, the decision maker’s choice of an appropriate decision alternative in a given situation is defied. In particular, operational actors are facing the challenge to meet business-critical decisions in a short time and at high frequency. The con-struct of Situation Awareness (SA) has been established in cognitive psychology as a valid basis for understanding the behavior and decision making of human beings in complex and dynamic systems. SA gives decision makers the possibil-ity to make informed, time-critical decisions and thereby improve the perfor-mance of the respective business process. This research paper leverages SA as starting point for a design science project for Operational Business Intelligence and Analytics systems and suggests a first version of design principles

The Effect of Recommender Systems on Users’ Situation Awareness and Actions

Many organizations are implementing recommender systems with the expectation to influence users’ actions. However, research has shown that poorly designed recommender systems may be counterproductive. For instance, if a recommender system provides too many recommendations, users cannot focus on relevant recommendations anymore. To tackle this challenge, recommender systems need to be balanced and adjusted to the processes in which they shall support users. Only designed correctly, recommender systems may influence users’ situation awareness and, ultimately, enable them to perform informed actions. Research has shown that users’ situation awareness depends on users’ elaboration. Therefore, we draw on the Elaboration Likelihood Model to conceptualize recommendation velocity and recommendation faithfulness as two variables that influence users’ situation awareness. Furthermore, since research identified process automation as a major antecedent of situation awareness, we conceptualize process automation as a third influencing variable. Finally, we develop a conceptual research model and outline our next steps

Flow in Information Systems Research: Review, Integrative Theoretical Framework, and Future Directions

As information systems (IS) are increasingly able to create highly engaging and interactive experiences, the phenomenon of flow is considered a promising vehicle to understand pre-adoptive and post-adoptive IS user behavior. However, despite a strong interest of researchers and practitioners in flow, the reliability, validity, hypothesized relationships, and measurement of flow constructs in current IS literature remain challenging. By reviewing extant literature in top IS outlets, this paper develops an integrative theoretical framework of flow antecedents, flow constructs, and flow consequences within IS research. In doing so, we identify and discuss four major flow streams in IS research and indicate future research directions

The Smart Mobile Application Framework (SMAF) - Exploratory Evaluation in the Smart City Contex

What makes mobile apps "smart"? This paper challenges this question by seeking to identify the inherent characteristics of smartness. Starting with the etymological foundations of the term, elements of smart behavior in software applications are extracted from the literature, elaborated and contrasted. Based on these findings we propose a Smart Mobile Application Framework incorporating a set of activities and qualities associated with smart mobile software. The framework is applied to analyze a specific mobile application in the context of Smart Cities and proves its applicability for uncovering the implementation of smart concepts in real-world settings. Hence, this work contributes to research by conceptualizing a new type of application and provides useful insights to practitioners who want to design, implement or evaluate smart mobile applications

Tools of Trade of the Next Blue-Collar Job? Antecedents, Design Features, and Outcomes of Interactive Labeling Systems

Supervised machine learning is becoming increasingly popular - and so is the need for annotated training data. Such data often needs to be manually labeled by human workers, not unlikely to negatively impact the involved workforce. To alleviate this issue, a new information systems class has emerged - interactive labeling systems. However, this young, but rapidly growing field lacks guidance and structure regarding the design of such systems. Against this backdrop, this paper describes antecedents, design features, and outcomes of interactive labeling systems. We perform a systematic literature review, identifying 188 relevant articles. Our results are presented as a morphological box with 14 dimensions, which we evaluate using card sorting. By additionally offering this box as a web-based artifact, we provide actionable guidance for interactive labeling system development for scholars and practitioners. Lastly, we discuss imbalances in the article distribution of our morphological box and suggest future work directions

TOWARDS AN INTEGRATIVE THEORETICAL FRAMEWORK OF INTERACTIVE MACHINE LEARNING SYSTEMS

Interactive machine learning (IML) is a learning process in which a user interacts with a system to iteratively define and optimise a model. Although recent years have illustrated the proliferation of IML systems in the fields of Human-Computer Interaction (HCI), Information Systems (IS), and Computer Science (CS), current research results are scattered leading to a lack of integration of existing work on IML. Furthermore, due to diverging functionalities and purposes IML systems can refer to, an uncertainty exists regarding the underlying distinct capabilities that constitute this class of systems. By reviewing extensive IML literature, this paper suggests an integrative theoretical framework for IML systems to address these current impediments. Reviewing 2,879 studies in leading journals and conferences during the years 1966-2018, we found an extensive range of applications areas that have implemented IML systems and the necessity to standardise the evaluation of those systems. Our framework offers an essential step to provide a theoretical foundation to integrate concepts and findings across different fields of research. The main contribution of this paper is organising and structuring the body of knowledge in IML for the advancement of the field. Furthermore, we suggest three opportunities for future IML research. From a practical point of view, our integrative theoretical framework can serve as a reference guide to inform the design and implementation of IML systems

A SITUATION AWARENESS DRIVEN DESIGN FOR PREDICTIVE MAINTENANCE SYSTEMS: THE CASE OF OIL AND GAS PIPELINE OPERATIONS

The acquisition and processing of events from sensors or enterprise applications in real-time represent an essential part of many application domains such as the Internet of Things (IoT), offering benefits to predict the future condition of equipment to prevent the occurrence of failures. Many organisations already use some form of predictive maintenance to monitor performance or keep track of emerging business situations. However, the optimal design of applications to allow an effective Predictive Mainte-nance System (PMS) capable of analysing and processing large amounts of data is only scarcely exam-ined by Information Systems (IS) research. Due to the number, frequency, and the need for near-real-time evaluation systems must be capable of detecting complex event patterns based on spatial, temporal, or causal relationships on data streams (i.e. via Complex Event Processing). At the same time, however, due to the technical complexity, available systems today are static, since the creation and adaptation of recognisable situations results in slow development cycles. In addition, technical feasibility is only one prerequisite for predictive maintenance. Users must be capable of processing this vast amount of data presented without considerable cognitive effort. Precisely this challenge is even more daunting as op-erational maintenance personnel have to manage business-critical decisions with increasing frequency and short time. Research in Human Factors (HF) suggests Situation Awareness (SA) as a crucial sys-tem’s design paradigm allowing human beings to understand and anticipate the information available effectively. Building on this concept, this paper proposes a PMS for promoting operational decision makers’ Situation Awareness by three design principles (DP): Sensing, Acting, and Tracking. Based on these DPs, we implemented a PMS prototype for a scenario in Oil and Gas pipeline operations. Our finding suggest that the use of SA is of particular interest in realizing effective PMS

Towards a Physiological Computing Infrastructure for Researching Students’ Flow in Remote Learning – Preliminary Results from a Field Study

With the advent of physiological computing systems, new avenues are emerging for the field of learning analytics related to the potential integration of physiological data. To this end, we developed a physiological computing infrastructure to collect physiological data, surveys, and browsing behavior data to capture students’ learning journey in remote learning. Specifically, our solution is based on the Raspberry Pi minicomputer and Polar H10 chest belt. In this work-in-progress paper, we present preliminary results and experiences we collected from a field study with medical students using our developed infrastructure. Our results do not only provide a new direction for more effectively capturing different types of data in remote learning by addressing the underlying challenges of remote setups, but also serve as a foundation for future work on developing a less obtrusive, (near) real-time measurement method based on the classification of cognitive-affective states such as flow or other learning-relevant constructs with the captured data using supervised machine learning