Purpose Technological advancements have been central to decarbonization efforts; however, this has led to an increasing, hidden digital carbon footprint. This study aims to examine how organizations can self-assess their digital decarbonization readiness with the aim of reducing their digital carbon footprint. Design/methodology/approach Using an integrative review approach, a canvas of the extant literature pertinent to digital decarbonization and the related fields of Green IT, digital, ICT and technology literatures, draws together insights that could be used by organizations to self-assess their digital decarbonization readiness. Findings The conceptual tool comprises ten dimensions against which organizations self-assess to generate an overview of the organization’s performance in each dimension, highlighting areas of strength (high scores) and areas that may need improvement (low scores), and an aggregate score representing the organization’s overall digital decarbonization readiness. Research limitations/implications In the context of global net zero targets, digital decarbonization represents an opportunity to mitigate the negative environmental impact of the new data-scape. To aid organizations in reducing their digital carbon footprint, the conceptual tool allows organizations to self-assess their digital decarbonization readiness. Practical implications The authors introduce a conceptual planning tool for Chief Information Officers (CIOs), Chief Technology Officers (CTOs), Chief Strategy Officers (CSOs) and Sustainability Leads to assess their digital decarbonization profile. Originality/value The study outlines a series of organizational skills and capabilities to successfully manage the digital ecosystem and contribute to organizations' digital decarbonization initiatives that may help to mitigate digital carbon footprints.

In the last 25 years, work-email activity has been studied across domains and disciplines. Yet, despite the abundance of research available, a comprehensive, unifying framework of how work-email activity positively impacts both well-being and work-performance outcomes has yet to emerge. This is a timely and significant concern; work-email is the most prominent and popular form of work communication but it is still unclear what people need to do to be effective emailers at work. To address this, we undertook a rigorous cross-disciplinary systematic literature review of 62 empirical papers. Using action regulation theory, we developed a multi-action, multi-goal framework and found four 'super' actions that consistently predict effectiveness (positive well-being and work-performance outcomes). These actions involve: (i) communicating and adhering to work-email access boundaries; (ii) regularly triaging emails (iii) sending work-relevant email and (iv) being civil and considerate in work-email exchanges. We found that super actions are engaged when workers have the resources to appropriately regulate their activity, and can attend to their self, task and social needs. Our framework synthesizes a broad and disparate research field, providing valuable insights and guiding future research directions. It also offers practical recommendations to organizations and individuals; by understanding and encouraging the adoption of work-email super actions, effective work-email practices can be enhanced.

The main purpose of this paper is to model the new ways of knowledge creation and their relationship with both exploratory and exploitative innovation. Based on Dubin’s quantitative method of theory building, a conceptualization of a model concerning transformations in the creation of knowledge in an organization embedded in technology is presented. The authors construct and analyze a novel model called Persistent Leveraging of Artificial Intelligence (AI) Systems Tapers Innovation Capability ‘PLASTIC’, which models on-going transitions in knowledge management practices. The theoretical model aids in the understanding of the specific knowledge processes within an organization and the main prospective challenges, obstacles and difficulties for knowledge management over the next decade. It discusses the role of a changeable environment and the interactions between technology-driven transformations and human-oriented practices, and it enables the evaluation of the future adaptation in knowledge management processes. This research is the first to challenge the impact of AI aided searching on the workforce and provides the catalyst for discussion of long-term innovation implications.

PURPOSE: This paper reports the challenges encountered and successfully overcome in designing and implementing a knowledge management (KM) system in a third sector organisation. In particular, it highlights the academic contribution to the implementation of Electronic Content Management (ECM) systems and exposes the gap between academic theory and practice. DESIGN/METHODOLOGY: The research adopts a case study and mixed methods approach following an exploratory sequential design in a third sector national sports organisation. FINDINGS: The findings highlight that a holistic academic approach is required for successful system implementation. The result is a co-created framework from both academics and practitioners that will facilitate the successful implementation of an ECM system in a third sector organisation. RESEARCH LIMITATIONS: The research was carried out in a single case study organisation and therefore caution should be taken in generalising the conclusions across multiple different contexts. PRACTICAL IMPLICATIONS: The outcome of the research offers a practical tool (the EKESNA system) and framework that will be of potential assistance in successful implementation of an ECM system. ORGINALITY/VALUE: The research builds upon academic theory that is applied to the design and implementation of KM systems. It extends current academic thinking through exploration of the approach taken to develop a more holistic implementation framework for ECM; detailing the relevance of social network analysis for stakeholder analysis, combined with the use of expertise profiling to aid the development of a corporate taxonomy for information structuring.

From a societal perspective, the huge growth in data being generated by organisations is clearly correlated to technological advancements enabling far greater capacity for data acquisition and storage (e.g., data centres) than has ever been previously available. Data centres alone account for 3% of the global electricity supply and consume more power than the entire United Kingdom (UK), contributing 2% of the total global greenhouse gas emissions (Bawden, 2016). The “store it all” approach adopted by many organisations as evidenced in the migration to the cloud, for instance, is a significant threat to the pursuit of netzero, given that the energy sector already accounts for 35% of the total global emissions (UN, 2022). The exponential growth in digital data generation, which according to Statista (2022) will be as high as 79.4 zettabytes worldwide by 2025, thus poses a huge potential threat to global net-zero efforts. To illustrate, early estimates have suggested that 4% of global greenhouse gas emissions can be attributed to digitalisation (Teuful & Sprus, 2020). The digital data carbon footprint should, therefore, be of critical concern to organisations and public administrations alike. With the increasing need for organisations to report the greenhouse gas emission associated with their direct, indirect, and supplychain activities as well as policy targets to reduce greenhouse gas emissions across developed economies, it is surprising to note that the digital data carbon footprint is not considered. As Jackson and Hodgkinson (2022) highlight, while decarbonisation is clearly a policy priority for developed governments, there remains no mention of the role of digital data in recent policy documents. It is important to be clear, as others have (e.g., Teuful & Sprus, 2020), that digital data and indeed digitalisation is not inherently “bad” for the environment, but rather, it is what we as individuals, organisations, and society make of it that dictates the impact on the environment. This is central to the digital decarbonisation movement, which concerns how knowledge and data are used, and reused, by organisations and the promotion of digital best-practices in sustainability strategies to reduce data CO2 (Jackson & Hodgkinson, 2022). Research on responsible management practices remains largely detached from the abundant work on organisational learning and the knowledge management (KM) field more broadly (Dzhengiz & Niesten, 2020). This is despite there being a clear relationship with how organisations draw on new and existing knowledge, and the health of the environment. Technological progress has changed how knowledge is managed in organisations and particularly in the way in which new knowledge is acquired, assimilated, transformed and exploited through organisations’ absorptive capacity, an established learning capability of the organisation (e.g., Dzhengiz & Niesten, 2020; Fosfuri & Tribó, 2008; Yuan et al., 2022). Several recent studies illustrate how emerging technologies have shaped knowledge processes in organisations (e.g., Stachová et al., 2020) and the relationship between modern technology and knowledge management processes in organisations (e.g., Almeida et al., 2019; Archer-Brown & Kietzmann, 2018; Benitez et al., 2018; O’connor & Kelly, 2017; Sher & Lee, 2004; Skok & Kalmanovitch, 2005; Wild & Griggs, 2008). Yet, the consequences of using modern technological solutions within the absorptive capacity process for individuals, organisations and society is not clear. At the individual and organisational levels, technological advancements have changed the cognitive patterns and knowledge-related behaviours of employees (Ward, 2013). For instance, employees spend less time on direct interactions with colleagues and more time on individual computer work (Kleszewski & Otto, 2020), resulting in reduced direct information exchange. Such behavioural changes are deemed to impede socialisation and group processes, which are known to be integral features of traditional knowledge processes (Nonaka & Takeuchi, 1995). Moreover, as knowledge workers have become more technologyreliant, they have become more efficient in using justin-time knowledge (Jackson & Hodgkinson, 2022). Consequently, they are more prone to surface learning (Gursoy et al., 2008) instead of gaining a deeper understanding of a subject or topic (Dennett & KNOWLEDGE MANAGEMENT RESEARCH & PRACTICE 2023, VOL. 21, NO. 3, 427–435 https://doi.org/10.1080/14778238.2023.2192580

Customer experience management (CEM) in the social media age finds itself needing to adapt to a rapidly changing digital environment and hence there is a need for innovative digital data analytical solutions. Drawing on an action case study of a large global automotive manufacturer, this study presents a digital innovation for enhanced emotion analytics on user generated content (UGC) and behaviour (UGB), to improve consumer insights for CEM. The digital innovation captures customer experience in real time, enabling measurement of a wide range of discrete emotions on the studied social media platform, which goes beyond traditional tools that capture positive or negative sentiment only. During the digital intervention, a substantial number of inauthentic and bot like behaviours was revealed, unbeknown to the case organisation. These accounts were found to be posting and amplifying highly emotional and potentially damaging content surrounding the case brand and its products. The study illustrates how emotion in the context of customer experience should go beyond typical categorisations, given the complexity of human emotion, while a distinction between bot and authentic users is imperative for CEM.

Purpose In the pursuit of net-zero, the decarbonization activities of organizations are a critical feature of any sustainability strategy. However, government policy and recent technological innovations do not address the digital carbon footprint of organizations. The paper aims to present the concept of single-use dark data and how knowledge reuse by organizations is a means to digital decarbonization. Design/methodology/approach Businesses in all sectors must contribute to reducing digital carbon emissions globally, and to the best of the authors’ knowledge, this paper is the first to examine “how” from a knowledge (re)use perspective. Drawing on insights from the knowledge creation process, the paper presents a set of pathways to greater knowledge reuse for the reduction of organizations’ digital carbon footprint. Findings Businesses continually collect, process and store knowledge but generally fail to reuse these knowledge assets – referred to as dark data. Consequently, this dark data has a huge impact on energy use and global emissions. This model is the first to show explicit pathways that businesses can follow to sustainable knowledge practices. Practical implications If businesses are to be proactive in their collective pursuit of net-zero, then it becomes paramount that reducing the digital carbon footprint becomes a key sustainability target. The paper presents how this might be accomplished, offering practical and actionable guidance to businesses for digital decarbonization. Originality/value Two critical questions are facing businesses: how can decarbonization be achieved? And can it be achieved at a low-cost? Awareness of the damaging impact digitalization may be having on the environment is in its infancy, yet knowledge reuse is a proactive and cost-effective route to reduce carbon emissions, which is explored in the paper.

The role of humorous content on social media has rarely been taken into account in prior work. Understanding its dynamics on social media provides insight that could benefit a range of applications in sentiment analysis. This paper introduces literature on humour theory, related human behaviour and a discussion of existing automated approaches to humour detection. We present and review current research on humorous language use on social media and its significance. In particular, example humorous expressions from Twitter are used to illustrate the heterogeneous types of humour on social media. Since most prior work focused on English language contexts, the analysed example uses of humour are set in the Arabic cultural context, providing a novel view. The primary contribution of this paper is the position that similar to sentiment analysis, automated humour detection in its own right has potential in understanding public reactions and should be explored in future studies.

Purpose Customer experience is more critical than ever to firms’ successes and future growth opportunities. Typically measured through aggregate satisfaction scores, businesses have been criticized for oversimplifying what experience means. The purpose of this study is to provide a new perspective on experience management and offers a novel way forward for customer-centric strategizing. Design/methodology/approach Mapping the current digital technologies being used across businesses in all sectors to engage and connect with customers more effectively, this paper outlines some of the fundamental challenges of experience management and future opportunities to enhance business practice. Findings Businesses are capturing what they know about customers, rather than what a customer thinks and feels about the firm. Many experience management initiatives create customer pains (not gains), while for businesses, decision-making can be jeopardized by fake customer data. A framework based upon the five experience dimensions is presented for optimal customer-driven decision-making. Practical implications Going beyond aggregate satisfaction scores that serve as an output rather than an input into businesses strategizing, the paper presents an actionable framework for targeted investments and enhanced experience management practices. Originality/value Businesses are seeking to grow intelligent customer experience analysis capabilities to disrupt traditional business models toward greater customer-centricity and to track the digital spread of positive and negative experiences. Examining how this is being done and where the weaknesses lie by bridging management practice and the scientific literature, this paper provides new knowledge to advance customer-centric strategies for growth and profitability.

We present a Work‐habit Intervention Model (WhIM) to explain and predict how to change work‐habits to be more effective. Habit change has primarily been researched within the health domain. The WhIM contributes a unique theoretical perspective by: (i) suggesting that work‐habit change requires a two‐stage process of exposure to regular rationalized plans and a stated intention to use these plans; and, (ii) defining effective work‐habit change in terms of improvements to both goal attainment and well‐being over time. Self‐regulatory resources are included as potential moderators of habit change. This approach implies that work‐habits (unlike health‐habits) are seldom constitutionally ‘good’ or ‘bad’, which means that change requires a clear rationale in terms of improving goal attainment and well‐being. The WhIM was evaluated in a 12‐month wait‐list intervention study designed to improve work‐email habits for workers in a UK organization (N = 127 T1; N = 58 T3; N = 46 all data). Findings were that the two‐stage process changed work‐email habits for those with higher levels of self‐efficacy, which predicted well‐being in terms of reduced negative affect (via perceived goal attainment). We outline theoretical and practical implications and encourage future research to refine the WhIM across a range of other work contexts.Practitioner points Workers need to regularly engage with rationalized plans of action and state their intention to use these, in order to change work‐email habits. Organizations should consider training workers to enhance their self‐efficacy prior to implementing a work‐email habit change intervention. Providing regular feedback about the impact of work‐email habit change on well‐being and goal attainment is likely to make the change sustainable in the long‐term.

The aim of the research outlined in this paper is to demonstrate the implementation of a Cyber-Physical System (CPS) within the Automotive Industry for the monitoring and control of Returnable Transit Items (RTIs) toward improved quality assurance and process compliance. The socio-technical issues encountered during the real-world implementation are discussed to inform future design Automotive RTI’s are utilised in the transportation of both components and subsequently assembled products at the beginning and end of life stages. The implemented system utilises passive Ultra-High Frequency (UHF) Radio Frequency IDentification (RFID) tags for the identification of metal RTIs via associated plastic separators, whilst a distributed network of RFID portals was integrated within the RTI working environment to capture and characterise their movements. The requirements, design process and resulting architecture are presented alongside the results and lessons learnt from an implementation within the automotive industry. Through the integration of business processes, analytics and tacit domain knowledge, a real-time model of the state of RTIs was developed to support decision making by a range of stakeholders. This research contributes to the knowledge of CPSs requirements identification, design, deployment and the challenges faced within real world asset monitoring and traceability within the automotive industry. Areas for future research to support the next generation of RTI traceability, monitoring and control systems are presented.

Computer-mediated communication (CMC) interruptions are a common feature of people's work activity. In studying interruptions, researchers can understand how people manage and co-ordinate their work when faced with multiple, often competing, demands. However, CMC interruptions are characteristically different from each other and impact people's work performance in different ways. In this theoretical paper, we define and differentiate between computer-mediated communication (CMC) interruptions, according to the level of control people are able to exert over an interruption at different points in the delivery timeline. Informed by the extant interruptions literature and Action Regulation Theory, a classification framework is provided, to help researchers and work designers distinguish which types of real-world CMC interruption are more or less disruptive, based on levels of control. Using the developed framework, two key research propositions are made, which we encourage future research to attend to. Unique contributions and implications of this paper are discussed.

Abstract Radio-Frequency Identification (RFID) system technology is a key element for the realization of the Industry 4.0 vision, as it is vital for tasks such as entity tracking, identification and asset management. However, the plethora of RFID systems’ elements in combination with the wide range of factors that need to be taken under consideration along with the interrelations amongst them, make the problem of identification and design of the right RFID system, based on users’ needs particularly complex. The research outlined in this paper seeks to optimize this process by developing an integrating schema that will encapsulate this information in a form that is both human and machine processible. Human readability will allow a shared understanding of the RFID technology domain; machine readability, automated reasoning engines to perform logical deduction techniques returning implicit information. For this purpose, the novel RFID System Configuration Ontology (RFID SCO) is developed. Hence, non-RFID experts are enabled to identify the most suitable RFID system according to their needs and RFID experts to retrieve all the relevant information required for the efficient design of the corresponding RFID system. The RFID SCO is validated and tested successfully against real-world scenarios provided by domain experts.

There is increasing interest in systems that aid employees to find those with the expertise they require. This paper discusses the evolution of expert finding tools, with particular reference to solutions that exploit email sources and identifies related gaps. The authors then propose Email Knowledge Extraction (EKE), a system for expertise discovery which addresses the issues highlighted by gap analysis.

As the use of automated social media analysis tools surges, concerns over accuracy of analytics have increased. Some tentative evidence suggests that sarcasm alone could account for as much as a 50% drop in accuracy when automatically detecting sentiment. This paper assesses and outlines the prevalence of sarcastic and ironic language within social media posts. Several past studies proposed models for automatic sarcasm and irony detection for sentiment analysis; however, these approaches result in models trained on training data of highly questionable quality, with little qualitative appreciation of the underlying data. To understand the issues and scale of the problem, we are the first to conduct and present results of a focused manual semantic annotation analysis of two datasets of Twitter messages (in total 4334 tweets), associated with; (i) hashtags commonly employed in automated sarcasm and irony detection approaches, and (ii) tweets relating to 25 distinct events, including, scandals, product releases, cultural events, accidents, terror incidents, etc. We also highlight the contextualised use of multi-word hashtags in the communication of humour, sarcasm and irony, pointing out that many sentiment analysis tools simply fail to recognise such hashtag-based expressions. Our findings also offer indicative evidence regarding the quality of training data used for automated machine learning models in sarcasm, irony and sentiment detection. Worryingly only 15% of tweets labelled as sarcastic were truly sarcastic. We highlight the need for future research studies to rethink their approach to data preparation and a more careful interpretation of sentiment analysis.

Currently the majority of employee email interaction is inefficient as many employees are interrupted by email as frequently as every five minutes (Jackson et al 2003a). A detailed assessment of email interaction has been carried out over the last five years and a system has been developed that will attempt to increase the efficiency of an employee’s interaction with an email system through changing the way employees are interrupted. This paper details the research components of the email system and the results of a very small preliminary study.

Operational Research (OR) techniques in sport have primarily focused on fixture timetabling, officials scheduling, and result forecasting. Emerging novel approaches to micro-level analysis of sport participation data is vital in providing sport management and the public health sector with evidence to make informed decisions about sport participation strategies and policy. The paper aims to identify factors facilitating effective decision making on increasing participation using logistic regression analysis. 24,678 records from the Active People Survey were used to determine factors that could be used to predict swimming participation for different demographic groups in England. Results show that the current country level approach covering all sports masks too much detail and the micro level approach to understanding relationships between the regressed variable (swimming participation) and the explanatory variables adopted in this paper is required to enable more effective policy recommendations to be made on increasing swimming, or any sport, participation.

In this research, we utilize semantic technology for robust early diagnosis and decision support. We present a light-weight platform that provides the end-user with direct access to the data through an ontology, and enables detection of any forthcoming faults by considering the data only from the reliable sensors. Concurrently, it indicates the actual sources of the detected faults, enabling mitigation action to be taken. Our work is focused on systems that require only real-time data and a restricted part of the historic data, such as fuel cell stack systems. First, we present an upper-level ontology that captures the semantics of such monitored systems and then we present the structure of the platform. Next, we specialize on the fuel cell paradigm and we provide a detailed description of our platform's functionality that can aid future servicing problem reporting applications.