Information Systems Frontiers最新文献

Traffic caused by drivers searching for a free parking space has numerous negative effects, such as increased emissions and noise pollution. Innovative solutions can reduce these negative effects by providing car drivers with better information via a smart parking app. However, smart parking apps currently do not offer overarching solutions which support the entire parking process. Utilizing a service-dominant logic perspective, we examine why such overarching solutions do not emerge, whereas specialized ecosystems flourish. We follow a multiple case study approach and conduct qualitative interviews with three app providers and fourteen associated parking operators in Germany. Our results show how conflicting institutional arrangements at the micro, meso, and macro context levels lead to specialization. Our study deepens the understanding of how conflicting institutional arrangements affect the emergence of service ecosystems, drawing practical recommendations to overcome specialized smart parking apps in favor of overarching solutions.

IT use is the fundamental construct of IS research. However, despite its centrality, the literature lacks agreement on the nature of the construct and its antecedents, which has led to a fragmented nomological network. We provide a comprehensive review of the existing empirical IT use constructs, analyze their overlaps, and distill them into three dominant constructs: adoption, static IT use, and innovative IT use. We then develop an overarching framework for extant IT use research by developing a holistic model. Each IT use construct has its own set of organizational and technological antecedents as well as psychological drivers. Our model synthesizes existing research and is empirically validated using a web-based survey of business technology users. These findings resolve issues in the conceptualization of IT use, integrate the fragmented IT literature, and help avoid the illusion of knowledge accumulation. The outcomes of the paper provide guidelines for researchers on the conceptualization of IT use as well as for practitioners regarding purposeful use-based design. Areas for future research in IT use are also recommended.

The aim of this study is to illustrate the significance of human resources and software development teams in the process of value co-creation, specifically in the provision of human capital within the framework of Industry 5.0. This investigation takes into account ethical considerations, machine ethics, and gender inequalities. In order to achieve this aim, we conduct semi-structured in-depth qualitative interviews with 12 Human Resources Specialists and 12 Computer Engineers in large scale organisations in Turkey. As a theoretical lens, we adopt modified grounded theory to explore the interaction of teams for demonstrating how they design and manage the digital process by considering the human–machine collaboration aspect of Industry 5.0. Based on the interviews, there are three main themes in the present research: digitalisation in tracking personnel data, ensuring ethical actions in digitalisation of organisational process, and reflections of digitalisation to gender inequality. Since studies on diversity and industry 5.0 are scarce, this research demonstrates the ethical and adverse aspects of industry 5.0, and how it reflects to gender inequality in organisations.

In response to the COVID-19 pandemic, organisations across the world have been adopting different strategies, including remote working (RW), to continue with their normal routines. However, little is known about the impact of RW strategy on task innovativeness, organisational performance, and employees’ continuance intention to remote work post-COVID-19. Using a variant of the resource-based view and the antecedent-behaviour-consequence models, data was collected from a total of 643 employees in the United Kingdom (UK) and Ghana; and analysed using structural equation modelling (Study 1). The results showed and confirmed a positive effect of RW strategy, information systems (IS) resource availability and capabilities on employees’ task innovativeness leading to improved organisational performance. Further, interviews were then conducted with 22 participants from Ghana and the UK (Study 2) to understand the reasons for the outcomes in Study 1. These provided a basis for employees’ continuance intention to remote work. The need to upgrade the current levels of IS resources to support job redesign and responsive workaround in times of uncertainty is highlighted for the consideration of businesses, organisations, and policymakers.

Businesses constantly strive to build organizational capacity to use data strategically. As a result, there is a growing demand for business analytics professionals. While higher education systems worldwide have been adapting to build competencies, they must meet employees' expectations. Curriculum design for delivering business analytics competencies remains a challenge due to the rapidly evolving nature of business analytics as a discipline. The paper aims to decode the industry expectations for the Business Analytics profile. This study investigates the skills employers value by analyzing job descriptions. We use a text-mining approach to understand the weightage of different skills and mine skill clusters within business analytics roles. The core skill clusters are hard skills related to Big data, Business Intelligence, and analytical techniques. Results also suggest that traditional machine learning (ML) skills, typically expected in a data science profile, are also being sought after in a business analytics role. Surprisingly soft communication and stakeholder management skills are also emerging as essential skills for business analytics roles. This study provides a better understanding by investigating the interplay between the demand for skills in the job market and curriculum development.

Information systems are critical for companies since they offer quick and easy access to complex and significant data in a structured manner to make informed and effective business decisions. Hence, the objective of this study is to conceptualize and implement an innovative information system in the case study organization. The study identified the requirements for Organizing Vision Theory (OVT) and developed architecture based on Organizational Information Processing Theory (OIPT). This architecture is designed and developed using the Industrial Internet of Things (IIoT) to support a self-organizing vision and enhanced information processing. The study’s contribution lies in developing and executing an integrative architecture of IIoT-driven information systems from the lenses of OVT and OIPT. Further, this study contributes by mapping OVT elements (such as transparency, continuity, and coherence) and OIPT elements (information processing needs and capabilities) to drive value and knowledge through a robust architecture of IIoT-driven information systems. The study also highlights the contribution of IIoT-based information systems to a new knowledge system, facilitating better decision-making by professionals.

The paper studies the interactions between customers and robots within the framework of Industry 5.0-driven services. Prior studies have explored several factors contributing to the quality of these interactions, with perceived value being a crucial aspect. This study uses value recipes, which refer to specific configurations of how different benefits and costs are weighed up/evaluated, as a theoretical framework to investigate the quality of relationships between customers and service robots. The study aims to shed light on the complex interplay between different value dimensions that shape customers' relationships with robots. To achieve this goal, the authors analyze what value configurations facilitate or impede high-quality relationships between customers and service robots. Fuzzy set qualitative comparative analysis (fsQCA) was used to analyze data from 326 consumers. The data reveal that value recipes comprising positive values (such as relational benefit, novelty, control, personalization, excellence, and convenience) and negative values (about privacy and effort) prove highly effective in augmenting relationship quality. Results also underscore those negative values either in isolation or in conjunction with positive values, do not impede relationship quality. The theoretical contribution of this study lies in presenting new insights into relationship dynamics between customers and service robots in an Industry 5.0 value-driven context. From a practical standpoint, the findings suggest guidelines for successfully infusing the retail landscape with more intelligent service robots.

User-generated content (UGC) provides us with a rich channel to aggregate public information. The study examines whether UGC could predict the failure of P2P lending platforms, which also depends on crowds’ involvement and contribution. Specifically, we examine a web forum with user comments on the P2P platforms and extract consumer opinion valence and opinion concentration with natural language processing (NLP) techniques. Time-dependent Cox regression analysis shows that consumer opinion has a strong prediction power for platform survival. If consumer opinions on a platform are positive and concentrated, a platform is more likely to survive. Besides, the two factors show a substitution effect to each other. This study deepens our understanding of UGC, which has significant implications for research. It also forms an application of information technology on risk assessment, by expanding the risk indicators to UGC-related information sources.

Social media platforms have become an increasingly popular tool for individuals to share their thoughts and opinions with other people. However, very often people tend to misuse social media posting abusive comments. Abusive and harassing behaviours can have adverse effects on people's lives. This study takes a novel approach to combat harassment in online platforms by detecting the severity of abusive comments, that has not been investigated before. The study compares the performance of machine learning models such as Naïve Bayes, Random Forest, and Support Vector Machine, with deep learning models such as Convolutional Neural Network (CNN) and Bi-directional Long Short-Term Memory (Bi-LSTM). Moreover, in this work we investigate the effect of text pre-processing on the performance of the machine and deep learning models, the feature set for the abusive comments was made using unigrams and bigrams for the machine learning models and word embeddings for the deep learning models. The comparison of the models’ performances showed that the Random Forest with bigrams achieved the best overall performance with an accuracy of (0.94), a precision of (0.91), a recall of (0.94), and an F1 score of (0.92). The study develops an efficient model to detect severity of abusive language in online platforms, offering important implications both to theory and practice.

As countries emerged from the Covid-19 pandemic, management teams faced the challenges of supporting their employees to return to the traditional office work environment, adopting hybrid work modes to ensure business continuity, and creating work conditions conducive to personal well-being. Despite the critical role of digital platforms during the pandemic, there is limited understanding of the role of digital platform continuance to retain employees during the Great Resignation phenomenon. To address this gap, this study focuses on organisational support as there is growing recognition of its importance in digital platform continuance. Using partial least square algorithms, we used the organisational support theory and information systems (IS) continuance literature to derive a research model tested on data collected from 447 knowledge workers across central Europe and Africa. The results show that management-by-objective (MBO) and support from direct managers are essential determinants of knowledge workers’ post-crisis digital platform continuance intentions. The perceived usefulness of digital platforms mediates the effect of direct manager support on digital platform continuance. We discuss how the results could extend organisational level IS continuance research and help organisations develop strategies to retain employees in this post-crisis work-related phenomenon.