An Empirical Investigation on Business Analytics in Software and Systems Development Projects

2023 | Information Systems Frontiers | Citations: 0

Authors: Ahmad, Muhammad Ovais; Ahmad, Iftikhar; Rana, Nripendra P.; Khan, Iqra Sadaf

Abstract: To create competitive advantages, companies are leaning towards business analyti ... Expand

Abstract: To create competitive advantages, companies are leaning towards business analytics (BA) to make data-driven decisions. Nevertheless, users acceptance and effective usage of BA is a key element for its success. Around the globe, organizations are increasingly adopting BA, however, a paucity of research on examining the drivers of BA adoption and its continuance is noticeable in the literature. This is evident in developing countries where a higher number of systems and software development projects are outsourced. This is the first study to examine BA continuance in the context of software and systems development projects from the perspective of Pakistani software professionals. The data was collected from 186 Pakistani software professionals working in software and systems development projects. The data were analyzed using partial least squares - structural equation modelling techniques. Our structural model explains 45% variance on BA continuance intention, 69% variance on technological compatibility, and 59% variance on perceived usefulness. Our results show that confirmation has a direct impact on BA continuance intention in software and systems projects. The study has both theoretical and practical implications for professionals in the field of business analytics. Collapse

Topics: perceived usefulness IT development project IT project systems development IT workforce

Methods: survey partial least squares path modeling machine learning conceptual modelling data transformation

Theories: expectation confirmation theory

Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities

2023 | Information Systems Frontiers | Citations: 0

Authors: Alahakoon, Damminda; Nawaratne, Rashmika; Xu, Yan; De Silva, Daswin; Sivarajah, Uthayasankar; Gupta, Bhumika

Abstract: The emerging information revolution makes it necessary to manage vast amounts of ... Expand

Abstract: The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the self-building AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications. Collapse

Topics: artificial intelligence smart city big data cloud computing distributed system

Methods: computational algorithm experiment unsupervised learning machine learning longitudinal research

Towards More Convenient Services: A Text Analytics Approach to Understanding Service Inconveniences in Digital Platforms

2023 | HICSS | Citations: 0

Authors: Amat-Lefort, N.; Barnes, S.

Abstract: In today's fast-paced world, where time is our most valuable asset, convenience ... Expand

Abstract: In today's fast-paced world, where time is our most valuable asset, convenience is on the rise. This trend has led to a huge growth in digital on-demand services, which target convenience-oriented consumers. Using big data and text analytics, we examine the impact of service inconveniences on customer satisfaction in the context of on-demand food delivery. Building on the Model of Service Convenience and Attribution Theory, we analyze 235,147 user-generated reviews via a combination of keyword-assisted topic modelling and cumulative link model analysis. We introduce the concept of Remote support inconvenience and identify the key topics related to each inconvenience. We find that all service inconveniences negatively influence satisfaction (especially cancelled orders and remote support incidences), and the effects are exacerbated when moderated by stability or controllability attributions. These insights contribute to our theoretical understanding of service inconvenience and can help platforms identify and improve critical areas of their services. Collapse

Topics: user satisfaction customer satisfaction mobile application user-generated content crowdsourcing

Methods: natural language processing topic model exploratory data analysis ANOVA parametric test

Theories: attribution theory

Digitalization and Early Internationalization- Systematic Literature Review Analyses

2023 | HICSS | Citations: 0

Authors: Appiah, Emmanuel; Gabrielsson, Peter

Abstract: Substantial anecdotal evidence has been garnered to make it uncontentious to con ... Expand

Abstract: Substantial anecdotal evidence has been garnered to make it uncontentious to concede to studies that stress on the influence of digital technology on early internationalization of firms. Having an interest to study how studies on this phenomenon has progressed overtime. The present paper systematically evaluates internationalization literature in which issues of digitalization feature as a component underlying the causes, processes, and outcomes of early internationalization. This enables us to outline the development of major research areas in terms of themes, theoretical and methodological approaches. We contribute by proposing several future research directions. Collapse

Topics: information technology capability internet technology electronic business mobile system marketing management

Methods: literature sample structured literature research theory development cross sectional research literature study

Introduction to the Minitrack on Business Intelligence, Business Analytics, and Big Data: Innovation, Deployment, and Management

2023 | HICSS | Citations: 0

Authors: Ariyachandra, Thilini; Marjanovic, Olivera; Dinter, Barbara

Abstract: ... Expand

Abstract: Collapse

Topics: IT security organizational value big data

Methods: action design research personal interview structured literature research

Towards a domain-driven distributed reference architecture for big data systems

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Ataei, Pouya; Litchfield, Alan

Abstract: The proliferation of digital devices, rapid development of software and the inf ... Expand

Abstract: The proliferation of digital devices, rapid development of software and the infrastructure of today, have augmented user's capability to produce data at an unprecedented rate. The accelerated growth of data could be called the era of big data and forced a paradigm shift in data engineering because the variety, velocity and volume of data overwhelmed existing systems. While companies attempt to extract benefit from big data, success rates are still low. Challenges such as rapid changes in technology, organizational culture, complexity in data engineering, impediments to system development, and a lack of effective big data architectures mean that only an estimated 20% of companies achieved their goals. To this end, this study explores a domain-driven distributed big data reference architecture that addresses issues in data architecture, data engineering, and system development. This reference architecture is empirically grounded and evaluated through deployment in a real-world scenario as an instantiated prototype, solving a problem in practice. The results of the evaluation demonstrate utility and applicability but with architectural trade-offs and challenges. Collapse

Topics: software as a service cloud computing data lake systems development big data

Methods: design artifact simulation data modeling design science structured literature research

Human-machine collaboration for feature selection and integration to improve congestive Heart failure risk prediction

2023 | Decision Support Systems | Citations: 0

Authors: Ben-Assuli, Ofir; Heart, Tsipi; Klempfner, Robert; Padman, Rema

Abstract: The issue of harnessing machine learning (ML) algorithms for the prediction of a ... Expand

Abstract: The issue of harnessing machine learning (ML) algorithms for the prediction of adverse medical events is important considering the prevalence of vast repositories of patient-level medical data, and the quest to reduce healthcare costs while elevating the quality of care. Of particular interest is identifying a set of features that can better predict the risk of unplanned readmissions of chronically ill patients within 30 days post discharge, among them patients with Congestive Heart Failure (CHF). While numerous studies have employed ML algorithms to identify sets of significant features, only a handful have compared features extracted by various methods, and even fewer have compared the predictive power of feature sets selected by human experts to those selected by ML. Hence, this research aimed to elicit a comprehensive feature set selected by four ML algorithms and compare its predictive performance to that of a feature set selected by experts. We then evaluated a merged list of the two feature sets, constructing a human-machine collaborative set to predict the likelihood of 30-day unplanned readmission of CHF patients using data on 10,763 CHF patients hospitalized during the years 2010–2017. Our models achieved Area under the ROC Curve (AUC) above 0.8 and an Accuracy of ~0.89, comparable to the best models in the extant literature. Moreover, the Human-Machine Collaborative model significantly outperformed (best AUC 0.836) other human-only selected models as well as the machine-selected set. This study contributes to a better understanding of the power of using ML for patient risk stratification with special attention to the benefits of human-machine collaboration, even when these two entities work separately or in parallel, for improved clinical decision making. Collapse

Topics: healthcare data decision making big data missing data data warehouse

Methods: machine learning computational algorithm XGBoost descriptive statistic random forest classification

Attention to Digital Innovation: Exploring the Impact of a Chief Information Officer in the Top Management Team

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Bendig, David; Wagner, Robin; Piening, Erk; Foege, Johann

Abstract: We draw on the attention-based view of the firm to examine whether and when the ... Expand

Abstract: We draw on the attention-based view of the firm to examine whether and when the presence of a CIO in the TMT has a positive effect on both firms' ideated digital innovation (IDI) (i.e., the intensity of firms' digital patenting activity) and commercialized digital innovation (CDI) (i.e., the digital sophistication of firms' new products). Building on the idea that attention processes are context dependent, we also explore the moderating roles of CEO characteristics (IT background and role tenure) as well as environmental characteristics (the industry's IT attention). We analyze data from a cross-industry panel of U.S. S&P 500 firms over eight years that includes up to 2,852 firm-year observations. The results indicate that CIO presence in the TMT is positively related to a firm's IDI and CDI. Furthermore, they show that the organizational context related to CEO characteristics moderates the CIO-CDI relationship and that the environmental context related to the industry's IT attention moderates the CIO-IDI relationship. Our research contributes to the information systems literature by providing robust evidence that CIO presence in the TMT positively influences a firm's digital innovation outcomes, showing how internal and external boundary conditions affect the work of CIOs, and elaborating the role of managerial attention as an underlying mechanism explaining digital innovation. Collapse

Topics: Chief Information Officer innovation management digital innovation research and development IT investment

Methods: regression analysis method theory development instrumental variables estimation Compustat descriptive statistic

Algorithmic Transparency: Concepts, Antecedents, and Consequences – A Review and Research Framework

2023 | Communications of the Association for Information Systems | Citations: 0

Authors: Bitzer, Tobias; Wiener, Martin; Cram, W. Alec

Abstract: The widespread and growing use of algorithm-enabled technologies across many as ... Expand

Abstract: The widespread and growing use of algorithm-enabled technologies across many aspects of public and private life is increasingly sparking concerns about the lack of transparency regarding the inner workings of algorithms. This has led to calls for (more) algorithmic transparency (AT), which refers to the disclosure of information about algorithms to enable understanding, critical review, and adjustment. To set the stage for future research on AT, our study draws on previous work to provide a more nuanced conceptualization of AT, including the explicit distinction between AT as action and AT as perception. On this conceptual basis, we set forth to conduct a comprehensive and systematic review of the literature on AT antecedents and consequences. Subsequently, we develop an integrative framework to organize the existing literature and guide future work. Our framework consists of seven central relationships: (1) AT as action versus AT as perception; factors (2) triggering and (3) shaping AT as action; (4) factors shaping AT as perception; as well as AT as perception leading to (5) rational-cognitive and (6) affective-emotional responses, and to (7) (un-)intended behavioral effects. Building on the review insights, we identify and discuss notable research gaps and inconsistencies, along with resulting opportunities for future research. review of the literature on AT. Not only has such an endeavor, to our knowledge, not yet been undertaken; but more importantly, we expect that such a review will reveal critical research gaps and inconsistencies and inform future research in the area. In particular, we aim to extend past work and contribute to the extant body of knowledge by (1) strengthening the conceptual foundations of AT; (2) reviewing the AT literature in a systematic manner; and (3) deriving a research framework. In doing so, our study offers a more nuanced conceptualization of AT, including the explicit distinction between AT as action and AT as perception, and delineates AT from related concepts. Moreover, following established guidelines (e.g., vom Brocke et al., 2015;Okoli & Schabram, 2010;Paré et al., 2015;Webster & Watson, 2002) and drawing on a review sample of 50 studies (published between 2002 and 2021), our study offers a comprehensive review and synthesis of prior research on the antecedents and consequences of AT. Finally, we propose an integrative framework to organize existing and guide future work. Collapse

Topics: database system algorithmic transparency artificial intelligence website digital service

Methods: computational algorithm literature study synthesis mixed method archival research

Analyzing Patients’ EHR: Predicting and Explaining Admission Consequences for COPD and Liver Disease Patients

2023 | International Conference on Information Systems | Citations: 0

Authors: Ben-Assuli, Ofir; Geva, Gaya

Abstract: This study analyzed the admission outcomes in chronic patients (with COPD, and ... Expand

Abstract: This study analyzed the admission outcomes in chronic patients (with COPD, and Liver disease) to demonstrate the feasibility of applying prediction methods on EHR records while incorporating an explainable AI technique. We predicted three target variables: 30-day readmission, Medium&Long Length of Stay and Single-day admission and analyzed the features using an explainable AI technique, the SHapley Additive exPlanations (SHAP). The results show that Readmission had higher prediction scores than all other dependent variables. Some features affected all target variables with either positive or negative influence including: Age, Charlson comorbidity index, Day-Shift, Gender, using EHR screens and Insurance cover level. These findings thus point to the value of using Machine-Learning combined with an explainable AI method to understand and assess the risks factors. The assessment of the potential factors leading to multiple complications can bolster prevention-oriented medical decisions to groups of patients but can also be tailored to the patient level. Collapse

Topics: shapley additive explanation explainable artificial intelligence Python IT work artificial intelligence

Methods: machine learning XGBoost logistic regression artificial neural network classification method