A literature review of artificial intelligence research in business and management using machine learning and ChatGPT

This paper investigates applying AI models and topic modelling techniques to enhance computational literature reviews in business, management, and information systems. The study highlights the significance of impactful journals and emphasises the need for interdisciplinary and transdisciplinary research, especially in addressing AI's ethical and regulatory challenges. We demonstrate the effectiveness of combining machine learning and ChatGPT in the literature review process. Machine learning is used to identify research topics, and ChatGPT assists researchers in labelling the topics, generating content, and improving the efficiency of academic writing. By leveraging topic modelling techniques and ChatGPT, we uncover and label topics within the literature, shedding light on the thematic structure and content of the research field, allowing researchers to uncover meaningful insights, identify research gaps, and highlight rapidly expanding research areas. Additionally, we contribute to the literature review process by introducing a methodology that identifies impactful papers, helping to bridge the gap between computational literature reviews and traditional literature reviews

A multiple perspective approach to information system quality

The motivation for this research is a concern with the high rate of informationsystem failures reported in the academic literature and in practitioner publications. Itis proposed that the adoption of the customer-centred ideals and methods of qualitymanagement in information system development will increase the likelihood of thedelivery of successful information systems. The approach taken in the research is towork with the ideas of multiple perspectives - organizational effectiveness, work-lifequality, and technical artefact quality - and multiple stakeholders.The research approach is to use action research. The fieldwork comprisesthree phases. The first phase involved interviewing system developers and thesecond phase consisted of two case studies of implemented information systems.This preliminary analysis, together with a theoretical investigation of the foundationsof quality, was used to inform the development of a quality approach to informationsystem development. The information system development methodology (ISDM) isbased upon Multiview, a multiple perspective approach to information systemdevelopment, and the total quality management method used is quality functiondeployment. The resultant hybrid methodology is known as ISDM/Q.The ISDM/Q is tested using action research on a live system developmentproject concerned with the development of a wind tunnel control and data collectionsystem. Extensive organizational analysis was conducted to place this softwaredevelopment within a wider organizational context, involving quality requirementsworkshops and quality planning. The outcomes of the research are assessed in termsof the learning recorded with respect to the framework of ideas, the methodology(ISDM/Q) and the domain in which the action research took place. The field workshowed that there were benefits to using a quality metaphor in information systemdevelopment but that this would require a significant change in the culture and styleof information system development organizations. A practical contribution of theresearch is the development of quality function deployment for information systemdevelopment

A Quality Framework For Web Site Quality: User Satisfaction And Quality Assurance

Web site developers should make use of a range of standards and best practices to ensure their Web sites are functional, widely accessible and interoperable. However in practice many Web sites fail to achieve such goals. This short paper describes how a Web site quality assessment method (E-Qual) might be used in conjunction with a lightweight quality assurance framework (QA Focus) to provide a rounded view of Web site quality that takes account of user and supplier perspectives

Algorithmic Pollution - Making the Invisible Visible

In this paper, we focus on the growing evidence of unintended harmful societal effects of automated algorithmic decision-making (AADM) in transformative services (e.g., social welfare, healthcare, education, policing and criminal justice), for individuals, communities and society at large. Drawing from the long-established research on social pollution, in particular its contemporary ‘pollution-as-harm’ notion, we put forward a claim - and provide evidence - that these harmful effects constitute a new type of digital social pollution, which we name ‘algorithmic pollution’. Words do matter, and by using the term ‘pollution’, not as a metaphor or an analogy, but as a transformative redefinition of the digital harm performed by AADM, we seek to make it visible and recognized. By adopting a critical performative perspective, we explain how the execution of AADM produces harm and thus performs algorithmic pollution. Recognition of the potential for unintended harmful effects of algorithmic pollution, and their examination as such, leads us to articulate the need for transformative actions to prevent, detect, redress, mitigate, and educate about algorithmic harm. These actions, in turn, open up new research challenges for the information systems community

Algorithmic Pollution: Making the Invisible Visible

In this paper, we focus on the growing evidence of unintended harmful societal effects of automated algorithmic decision-making (AADM) in transformative services (e.g., social welfare, healthcare, education, policing and criminal justice), for individuals, communities and society at large. Drawing from the long-established research on social pollution, in particular its contemporary ‘pollution-as-harm’ notion, we put forward a claim, and provide evidence, that these harmful effects constitute a new type of digital social pollution, which we name ‘algorithmic pollution’. Words do matter, and by using the term ‘pollution’, not as a metaphor, but as a transformative redefinition of the digital harm performed by AADM, we seek to make it visible and recognized. By adopting a critical performative perspective, we explain how the execution of AADM produces harm and thus performs algorithmic pollution. Recognition of the potential for unintended harmful effects of algorithmic pollution, and their examination as such, leads us to articulate the need for transformative actions to prevent, detect, redress, mitigate, and educate about algorithmic harm. These actions, in turn, open up new research challenges for the information systems community. </jats:p

Social disparities in food preparation behaviours: a DEDIPAC study

BACKGROUND: The specific role of major socio-economic indicators in influencing food preparation behaviours could reveal distinct socio-economic patterns, thus enabling mechanisms to be understood that contribute to social inequalities in health. This study investigated whether there was an independent association of each socio-economic indicator (education, occupation, income) with food preparation behaviours. METHODS: A total of 62,373 adults participating in the web-based NutriNet-Santé cohort study were included in our cross-sectional analyses. Cooking skills, preparation from scratch and kitchen equipment were assessed using a 0-10-point score; frequency of meal preparation, enjoyment of cooking and willingness to cook better/more frequently were categorical variables. Independent associations between socio-economic factors (education, income and occupation) and food preparation behaviours were assessed using analysis of covariance and logistic regression models stratified by sex. The models simultaneously included the three socio-economic indicators, adjusting for age, household composition and whether or not they were the main cook in the household. RESULTS: Participants with the lowest education, the lowest income group and female manual and office workers spent more time preparing food daily than participants with the highest education, those with the highest income and managerial staff (P < 0.0001). The lowest educated individuals were more likely to be non-cooks than those with the highest education level (Women: OR = 3.36 (1.69;6.69); Men: OR = 1.83 (1.07;3.16)) while female manual and office workers and the never-employed were less likely to be non-cooks (OR = 0.52 (0.28;0.97); OR = 0.30 (0.11;0.77)). Female manual and office workers had lower scores of preparation from scratch and were less likely to want to cook more frequently than managerial staff (P < 0.001 and P < 0.001). Women belonging to the lowest income group had a lower score of kitchen equipment (P < 0.0001) and were less likely to enjoy cooking meal daily (OR = 0.68 (0.45;0.86)) than those with the highest income. CONCLUSION: Lowest socio-economic groups, particularly women, spend more time preparing food than high socioeconomic groups. However, female manual and office workers used less raw or fresh ingredients to prepare meals than managerial staff. In the unfavourable context in France with reduced time spent preparing meals over last decades, our findings showed socioeconomic disparities in food preparation behaviours in women, whereas few differences were observed in men

Understanding the adoption of business analytics and intelligence

Cruz-Jesus, F., Oliveira, T., & Naranjo, M. (2018). Understanding the adoption of business analytics and intelligence. In Á. Rocha, H. Adeli, L. P. Reis, & S. Costanzo (Eds.), Trends and Advances in Information Systems and Technologies, pp. 1094-1103. (Advances in Intelligent Systems and Computing; Vol. 745). Springer Verlag. DOI: 10.1007/978-3-319-77703-0_106Our work addresses the factors that influence the adoption of business analytics and intelligence (BAI) among firms. Grounded on some of the most prominent adoption models for technological innovations, we developed a conceptual model especially suited for BAI. Based on this we propose an instrument in which relevant hypotheses will be derived and tested by means of statistical analysis. We hope that the findings derived from our analysis may offer important insights for practitioners and researchers regarding the drivers that lead to BAI adoption in firms. Although other studies have already focused on the adoption of technological innovations by firms, research on BAI is scarce, hence the relevancy of our research.authorsversionpublishe

Doing more with less: productivity or starvation? The intellectual asset health check

The recent wave of savings in public service expenditure comes at the risk of creating starved workplaces, depleted of intellectual assets. This paper examines the perils of starved workplaces and how to avoid them. Organizations that nurture their intellectual assets were found to outperform their peers with 13.3% higher productivity. These organizations created a ‘win–win situation’, achieving both productivity targets while sustaining high stocks of emotional and human capital