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The purpose of this paper is to understand the nomological network of associations between collective mindfulness and big data analytics in fostering resilient humanitarian relief supply chains.

The authors conceptualize a research model grounded in literature and test the hypotheses using survey data collected from informants at humanitarian aid organizations in Africa and Europe.

The findings demonstrate that organizational mindfulness is key to enabling resilient humanitarian relief supply chains, as opposed to just big data analytics.

This is the first study to examine organizational mindfulness and big data analytics in the context of humanitarian relief supply chains.

Artificial intelligence (AI) has received much attention due to its promethean-like powers to transform the management and delivery of public sector services. Due to the proliferation of research articles in this context, research to date is fragmented into research streams based on different types of AI technologies or a specific government function of the public sector (e.g. health, education). The purpose of this study is to synthesize this literature, identify challenges and opportunities, and offer a research agenda that guides future inquiry.

This paper aggregates this fragmented body of knowledge by conducting a systematic literature review of AI research in public sector organisations in the Chartered Association of Business Schools (CABS)-ranked journals between 2012 and 2023.

The search strategy resulted in the retrieval of 2,870 papers, of which 61 were identified as primary papers relevant to this research. These primary papers are mapped to the ten classifications of the functions of government as classified by the Organisation for Economic Co-operation and Development (OECD), and the reported challenges and benefits aggregated.

This study advances knowledge by providing a state-of-the-art of AI research based the OECD classifications of government functions, reporting of claimed benefits and challenges and providing a research agenda for future research.

Social media has played a pivotal role in polarizing views on Russia–Ukraine conflict. The effects of polarization in online interactions have been extensively studied in many contexts. This research aims to examine how multiple social media sources may act as an integrator of information and act as a platform for depolarizing behaviors.

This study analyzes the communications of 6,662 tweets related to the sanctions imposed on Russia by using textual analytics and predictive modeling.

The research findings reveal that the tweeting behavior of netizens was depolarized because of information from multiple social media sources. However, the influx of information from non-organizational sources such as trending topics and discussions has a depolarizing impact on the user’s pre-established attitude.

For policymakers, conflict mediators and observers, and members of society in general, there is a need for (1) continuous and consistent communication throughout the crisis, (2) transparency in the information being communicated and (3) public awareness of the polarized and conflicting information being provided from multiple actors that may be biased in the claims being made about the conflict crisis.

While previous research has examined Russia–Ukraine conflict from a variety of perspectives, this is the first study to examine how social media might be used to reduce attitude polarization during times of conflict.

The study aims to map the links between Industry 4.0 (I-4.0) technologies and circular economy (CE) for sustainable operations and their role to achieving the selected number of sustainable development goals (SDGs).

The study adopts a systematic literature review method to identify 76 primary studies that were published between January 2010 and December 2020. The authors synthesized the existing literature using Scopus database to investigate I-4.0 technologies and CE to select SDGs.

The findings of the study bridge the gap in the literature at the intersection between I-4.0 and sustainable operations in line with the regenerate, share, optimize, loop, virtualize and exchange (ReSOLVE) framework leading to CE practices. Further, the study also depicts the CE practices leading to the select SDGs (“SDG 6: Clean Water and Sanitation,” “SDG 7: Affordable and Clean Energy,” “SDG 9: Industry, Innovation and Infrastructure,” “SDG 12: Responsible Consumption and Production” and “SDG 13: Climate Action”). The study proposes a conceptual framework based on the linkages above, which can help organizations to realign their management practices, thereby achieving specific SDGs.

The originality of the study is substantiated by a unique I-4.0-sustainable operations-CE-SDGs (ISOCES) framework that integrates I-4.0 and CE for sustainable development. The framework is unique, as it is based on an in-depth and systematic review of the literature that maps the links between I-4.0, CE and sustainability.

This paper aims to identify, prioritise and explore the relationships between the various barriers that are hindering the machine learning (ML) adaptation for analysing accident data information in the Indian petroleum industry.

The preferred reporting items for systematic reviews and meta-analysis (PRISMA) is initially used to identify key barriers as reported in extant literature. The decision-making trial and evaluation laboratory (DEMATEL) technique is then used to discover the interrelationships between the barriers, which are then prioritised, based on three criteria (time, cost and relative importance) using complex proportional assessment (COPRAS) and multi-objective optimisation method by ratio analysis (MOORA). The Delphi method is used to obtain and analyse data from 10 petroleum experts who work at various petroleum facilities in India.

The findings provide practical insights for management and accident data analysts to use ML techniques when analysing large amounts of data. The analysis of barriers will help organisations focus resources on the most significant obstacles to overcome barriers to adopt ML as the primary tool for accident data analysis, which can save time, money and enable the exploration of valuable insights from the data.

This is the first study to use a hybrid three-phase methodology and consult with domain experts in the petroleum industry to rank and analyse the relationship between these barriers.

The purpose of this paper is to examine contradictions (specifically tertiary and quaternary contradictions) that can disrupt the flow of work in contemporary systems development methods.

This study uses Activity theory (AT) as a theoretical lens to: examine ISD flow as an interrelated activity system; and identify contradictions. AT is pertinent in the context of this study as rather than view contradictions as a threat to prematurely abandon the use of flow tools and metrics, it shows how contradictions can act as a motor for change and continuity. This study adopts a longitudinal single case study approach including face-to-face interviews with management and software development project teams, as well as direct observations and document analysis.

This study identifies tertiary and quaternary contradictions, and highlights the influence of contradictions on flow-based systems development.

This study provides a set of contradictions for researchers and practitioners. It shows that contradictions can be culturally or politically challenging to confront, and even when resolved, can have intended or unintended consequences.

This paper fulfils an identified need to study ISD flow from the perspective of interrelated activity systems and beyond its initial implementation phase.

The purpose of this paper is to identify, classify and analyse temporality in information systems development (ISD) literature.

The authors address the temporality and ISD research gap by using a framework – which classifies time into three categories: conceptions of time, mapping activities to time and actors relating to time. The authors conduct a systematic literature review which investigates time in ISD within the Senior Scholars' Basket, Information Technology & People (IT&P), and top two information systems conferences over the past 20 years. The search strategy resulted in 9,850 studies of which 47 were identified as primary papers.

The results reveal that ISD research is ill equipped for contemporary thinking around time. This systematic literature review (SLR) contributes to ISD by finding the following gaps in the literature: (1) clock time is dominant and all other types of time are under-researched; (2) contributions to mapping activities to time is lacking and existing studies focus on single ISD projects rather multiple complex ISD projects; (3) research on actors relating to time is lacking; (4) existing ISD studies which contribute to temporal characteristics are fragmented and lack integration with other categories of time and (5) ISD methodology papers lack contributions to temporal characteristics and fail to acknowledge and contribute to time as a multifaceted interrelated concept.

This work has developed the first SLR on temporality in ISD. This study provides a starting point for ISD researchers and ISD practitioners to test commonly held temporal assumptions of ISD researchers and practitioners.

The purpose of this essay is to illustrate how project management “pull” and AI or analytics technology “push” are likely to result in incremental and disruptive evolution of project management capabilities and practices.

This paper is written as a critical essay reflecting the experience and reflections of the author with many ideas drawn from and extending selected items from project management, artificial intelligence (AI) and analytics literatures.

Neither AI nor sophisticated analytics is likely to elicit hands on attention from project managers, other than those producing AI or analytics-based artifacts or using these tools to create their products and services. However, through the conduit of packaged software support for project management, new tools and approaches can be expected to more effectively support current activities, to streamline or eliminate activities that can be automated, to extend current capabilities with the availability of increased data, computing capacity and mathematically based algorithms and to suggest ways to reconceive how projects are done and whether they are needed.

This essay includes projections of possible, some likely and some unlikely, events and states that have not yet occurred. Although the hope and purpose are to alert readers to the possibilities of what may occur as logical extensions of current states, it is improbable that all such projections will come to pass at all or in the way described. Nonetheless, consideration of the future ranging from current trends, the interplay among intersecting trends and scenarios of future states can sharpen awareness of the effects of current choices regarding actions, decisions and plans improving the probability that the authors can move toward desired rather than undesired future states.

Project managers not involved personally with creating AI or analytics products can avoid mastering detailed skill sets in AI and analytics, but should scan for new software features and affordances that they can use enable new levels of productivity, net benefit creation and ability to sleep well at night.

This essay brings together AI, analytics and project management to imagine and anticipate possible directions for the evolution of the project management domain.

Business analytics (BA) has been a breakthrough technological development in recent years. Although scholars have suggested several solutions in using these technologies to facilitate decision-making, there are as of yet limited studies on how analysts, in practice, improve decision makers' understanding of business environments. This study uses sensemaking theory and proposes a model of how data analysts generate analytical outcomes to improve decision makers' understanding of the business environment.

This study employs an interpretive field study with thematic analysis. The authors conducted 32 interviews with data analysts and consultants in Australia and New Zealand. The authors then applied thematic analysis to the collected data.

The thematic analysis discovered four main sensegiving activities, including data integration, trustworthiness analysis, appropriateness analysis and alternative selection. The proposed model demonstrates how these activities support the properties of sensemaking and result in improved decision-making.

This study provides strong empirical evidence for the theory development and practice of sensemaking. It brings together two distinct fields – sensemaking and business analytics – and demonstrates how the approaches advocated by these two fields could improve analytics applications. The findings also propose theoretical implications for information system development (ISD).

This study demonstrates how data analysts could use analytical tools and social mechanisms to improve decision makers' understanding of the business environment.

This study is the first known empirical study to conceptualize the theory of sensemaking in the context of BA and propose a model for analytical sensegiving in organizations.