• Editorial: Special issue on "Bright ICT: security, privacy and risk issues"

      Lawrence, Victor B.; Ayaburi, Emmanuel W.; Andoh-Baidoo, Francis Kofi; Dwivedi, Yogesh Kumar; Lal, Banita (Springer, 2022-04-02)
      Bright ICT, a 2015 initiative of the Association of Information Systems introduced by Prof J.K. Lee, refers to the grand vision of a bright society enabled by ICT. Bright ICT research involves taking a holistic view at the design of ICT enabled future society (Lee, 2016; Lee et al., 2018). This concept entails the development of relevant technologies, business models, public policies, social norms, international agreements, metrics for measuring national progress and preventing undesirable activities on the Internet. It is also at the center of discussions on adoption or modification of technologies, policies, and organizations from which new business models—that create a bright safe internet—can evolve. As a double edge sword, technology creates huge benefits such as the use of mobile phones for healthcare access but create challenges such as delayed access to healthcare providers (Haenssgen & Ariana, 2017). Legal frameworks such as the General Data Protection Regulation (GDPR) and opt-in/out rules that are promulgated to protect individuals’ private data have dual effect of reducing users’ information sharing intentions and giving power to a few Tech market players (Johnson et al., 2020).
    • Mechanistic model based optimization of feeding practices in aquaculture

      Li, Hui; Chatzifotis, Stavros; Lian, Guoping; Duan, Yanqing; Li, Daoliang; Chen, Tao; ; University of Surrey; Hellenic Centre for Marine Research; University of Bedfordshire; et al. (Elsevier, 2022-03-22)
      Fish feed accounts for more than 50% of total production cost in intensive aquaculture. Feeding fish with low quality feed or adopting inappropriate feeding strategies causes not only food waste and consequent loss of income but also lead to water pollution. The aim of this study was to develop a mechanistic model based optimization method to determine aquaculture feeding programs. In particular, we integrate a fish weight prediction model and a requirement analysis model to establish an optimization method for designing balanced and sustainable feed formulations and effective feeding programs. The optimization strategy is necessary to maximise the fish weight at harvest, while constraints include specific feed requirements and fish growth characteristics. The optimization strategy is re-solved with new available fish weight measurement by using the error between measurement and model prediction to adjust the requirement analysis model and update feeding amount decision. The mechanistic models are parameterised using the existing nutritional data on gilthead seabream (Sparus aurata) to demonstrate the usefulness of proposed method. The simulation results show that the proposed approach can significantly improve aquaculture production. This particular simulation study reveals that when “Only prediction” method is considered as benchmark, the average improvement in fish weight of proposed method would be 13.25% when fish weight is measured once per four weeks (mimicking manual sampling practice), and 38.43% when daily measurement of fish weight is possible (e.g. through automatic image-based methods). Furthermore, if feed composition (460 g protein.kg feed−1 ; 18.9 MJ kg feed−1 ) is adjusted, the average improvement of proposed method could reach 46.85%. Compared with traditional feeding methods, the improvement of proposed method could reach 36.36% of the final fish weight at harvest. Further studies will consider improving the quality of feed plus executing more appropriate mathematical prediction models to optimize production performance.
    • An investigation of the policies and crucial sectors of Smart Cities based on IoT application

      Razmjoo, Armin; Gandomi, Amirhossein; Mahlooji, Maral; Astiaso Garcia, Davide; Mirjalili, Seyedali; Rezvani, Alireza; Ahmadzadeh, Sahar; Memon, Saim; Universitat Politécnica de Catalunya; University of Technology Sydney; et al. (MDPI, 2022-03-04)
      As smart cities (SCs) emerge, the Internet of Things (IoT) is able to simplify more sophisti-cated and ubiquitous applications employed within these cities. In this regard, we investigate seven predominant sectors including the environment, public transport, utilities, street lighting, waste management, public safety, and smart parking that have a great effect on SC development. Our findings show that for the environment sector, cleaner air and water systems connected to IoT-driven sensors are used to detect the amount of CO2, sulfur oxides, and nitrogen to monitor air quality and to detect water leakage and pH levels. For public transport, IoT systems help traffic management and prevent train delays, for the utilities sector IoT systems are used for reducing overall bills and related costs as well as electricity consumption management. For the street-lighting sector, IoT systems are used for better control of streetlamps and saving energy associated with urban street lighting. For waste management, IoT systems for waste collection and gathering of data regarding the level of waste in the container are effective. In addition, for public safety these systems are important in order to prevent vehicle theft and smartphone loss and to enhance public safety. Finally, IoT systems are effective in reducing congestion in cities and helping drivers to find vacant parking spots using intelligent smart parking.
    • Critical analysis of the impact of Big Data analytics on supply chain operations

      Hasan, R.; Kamal, M.M.; Daowd, Ahmad; Eldabi, T.; Koliousis, I.; Papadopoulos, T. (Taylor and Francis, 2022-02-21)
      Undoubtedly, due to the increasingly competitive pressures and the stride of varying demands, volatility and disturbance have become the standard in today’s global markets. The spread of Covid-19 is a prime example for that. Supply chain managers are urged to rethink their competitive strategies to make use of Big Data Analytics (BDA), due to the increasing uncertainty in both demand and supply side, the competition among the supply chain partners and the need to identify ways to offer personalised products and services. With many supply chain executives recognising the need of “improving with data”, supply chain businesses need to equip themselves with sophisticated BDA methods/techniques to create valuable insights from big data, thus, enhancing the decision-making process and optimising the efficiency of Supply Chain Operations (SCO). This paper proposes the building blocks of a theoretical framework for understanding the impact of BDA on SCO. The framework is based on a Systematic Literature Review (SLR) on BDA and SCO, underpinned by Task-Technology-Fit theory and Institutional Theory. The paper contributes to the literature by building a platform for future work on investigating factors driving and inhibiting BDA impact on SCO.
    • Developing training materials for entrepreneurial skills: identifying processes, principles and core skills through case studies

      Duan, Yanqing; Bentley, Yongmei; Wilson, Patricia; Iarmosh, Olena (2021-12-31)
      The study reported in this paper aims to address the challenge of entrepreneurial skills shortage by sharing the experience and findings of developing entrepreneurial skills for women and young graduates in the agri-food and creative sectors through effective online training material development and implementation. To achieve this aim, this paper analyses four projects, and identifies common themes in terms of projects, processes, principles, and core skills for developing online training materials. All four projects provide online training materials combined with multiple complimentary support schemes. Using the projects as case studies, this paper examines in particular the projects' aim and training objectives, processes and the core skills covered in the training modules. The findings of this paper are used to propose a framework for projects, processes and design principles, with the aim of enabling the development of entrepreneurial skills through effective online training design and implementation.
    • An adaptive method for fish growth prediction with empirical knowledge extraction

      Li, Hui; Chen, Yingyi; Li, Wensheng; Wang, Qingbin; Duan, Yanqing; Chen, Tao; ; University of Surrey; China Agricultural University; Laizhou Mingbo Aquatic Products Co., Ltd; et al. (Elsevier, 2021-11-25)
      Fish growth prediction provides important information for optimising production in aquaculture. Fish usually exhibit different growth characteristics due to the variations in the environment, the equipment used in different fish workshops and inconsistent application by operators of empirical rules varying from one pond to another. To address this challenge, the aim of this study is to develop an adaptive fish growth prediction method in response to feeding decision. Firstly, the practical operational experience in historical feeding decisions for different fish weights is extracted to establish the feeding decision model. Then, a fish weight prediction model is established by regression analysis methods based on historical fish production data analysis. The feeding decision model is integrated as the input information of the fish weight prediction model to obtain fish weight prediction. Furthermore, an adaptive fish growth prediction strategy is proposed by continuously updating model parameters using new measurements to adapt to specific characteristics. The proposed adaptive fish growth prediction method with empirical knowledge extraction is evaluated by the collected production data of spotted knifejaw (Oplegnathus punctatus). The results show that established models can achieve a good balance between goodness-of-fit and model complexity, and the adaptive prediction method can adapt to specific fish pond’s characteristics and provide a more effective way to increase fish weight prediction accuracy. The proposed method provides an important contribution to achieving adaptive fish growth prediction in a real time from the view of aquaculture practice for spotted knifejaw.
    • Solidarity with Soufra: dividuality and joint action with Palestinian women refugees

      Schwabenland, Christina; Hirst, Alison; University of Bedfordshire; Anglia Ruskin University (Sage, 2021-10-08)
      Based on an exploratory study of Soufra, a women’s catering social enterprise in the Bourj al Barajneh Palestinian refugee camp in Beirut, we analyse how solidarity across difference can be organized. We conceptualize ‘difference’ not in terms of ‘whole’ individuals, but in terms of dividuals, the multiple roles and social positions that individuals occupy; this enables similarities between individuals of different ethnicities, nationalities and statuses to become apparent. We find that, despite their extreme and protracted marginalization, Soufra does not seek to organize solidarity relationships with co-resisters joining their struggle against oppressors. Rather, they initiate exchange relationships with different others via carefully managed impressions of similar dividualities (e.g. professional cooks and businesswomen) and different dividualities (e.g. having refugee status and lacking any citizenship). These encounters provide opportunities for solidarity relationships to be created and underlying cultural predispositions to be transformed. Whether these opportunities are taken up or rejected is dependent, at least to some extent, on the willingness of participants to allow such transformations to occur.
    • Editorial: How to develop a quality research article and avoid a journal desk rejection

      Dwivedi, Yogesh Kumar; Hughes, Laurie; Cheung, Christy M.K.; Conboy, Kieran; Duan, Yanqing; Dubey, Rameshwar; Janssen, Marijn; Jones, Paul; Sigala, Marianna; Viglia, Giampaolo; et al. (Elsevier, 2021-09-21)
      The desk rejection of submitted articles can be a hugely frustrating and demotivating process from the perspective of the researcher, but equally, a time-consuming and vital step in the process for the Editor, tasked with selecting appropriate articles that meet the required criteria for further review and scrutiny. The feedback from journal Editors within this editorial, highlights the significant gaps in understanding from many academics of the journal assessment process and acceptance criteria for progression to the review stage. This editorial offers a valuable “lived-in” perspective on the desk rejection process through the lens of the Editor, via the differing views of nine leading journal Editors. Each Editor articulates their own perspectives on the many reasons for desk rejection, offering key insight to researchers on how to align their submissions to the specific journal requirements and required quality criteria, whilst demonstrating relevance and contribution to theory and practice. This editorial develops a succinct summary of the key findings from the differing Editor perspectives, offering a timely contribution of significant value and benefit to academics and industry researchers alike.
    • Working from home during Covid-19: doing and managing technology-enabled social interaction with colleagues at a distance

      Lal, Banita; Dwivedi, Yogesh Kumar; Haag, Markus; ; University of Bradford; Swansea University; University of Bedfordshire (Springer, 2021-08-27)
      With the overnight growth in Working from Home (WFH) owing to the pandemic, organisations and their employees have had to adapt work-related processes and practices quickly with a huge reliance upon technology. Everyday activities such as social interactions with colleagues must therefore be reconsidered. Existing literature emphasises that social interactions, typically conducted in the traditional workplace, are a fundamental feature of social life and shape employees' experience of work. This experience is completely removed for many employees due to the pandemic and, presently, there is a lack of knowledge on how individuals maintain social interactions with colleagues via technology when working from home. Given that a lack of social interaction can lead to social isolation and other negative repercussions, this study aims to contribute to the existing body of literature on remote working by highlighting employees' experiences and practices around social interaction with colleagues. This study takes an interpretivist and qualitative approach utilising the diary-keeping technique to collect data from twenty-nine individuals who had started to work from home on a full-time basis as a result of the pandemic. The study explores how participants conduct social interactions using different technology platforms and how such interactions are embedded in their working lives. The findings highlight the difficulty in maintaining social interactions via technology such as the absence of cues and emotional intelligence, as well as highlighting numerous other factors such as job uncertainty, increased workloads and heavy usage of technology that affect their work lives. The study also highlights that despite the negative experiences relating to working from home, some participants are apprehensive about returning to work in the traditional office place where social interactions may actually be perceived as a distraction. The main contribution of our study is to highlight that a variety of perceptions and feelings of how work has changed via an increased use of digital media while working from home exists and that organisations need to be aware of these differences so that they can be managed in a contextualised manner, thus increasing both the efficiency and effectiveness of working from home.
    • Does the use of a web-based collaborative platform reduce cognitive load and influence project-based student engagement?

      Oluwajana, Dokun; Adeshola, Ibrahim; Clement, Seyefar (Springer, 2021-08-06)
      The web-based supported collaborative learning is increasingly used to support student social activities in higher institutions. However, little is known about the factors of collaborative learning in a web-based supported learning environment. Therefore, this study examines the use of a web-based supported collaborative platform to enhance project-based student engagement. This research aims to determine the factors that determine collaborative learning and subsequent student satisfaction. Moreover, this research determines students’ cognitive load understanding, social influence, and learner’s motivation towards collaborative learning and the resultant impact of the web-based supported collaborative platform on student satisfaction. The data was collected from university post-graduate students who used the TRELLO platform. A total of 115 post-graduate students participated in this study, and the resulting data were analyzed based on partial least squares structural equation modelling statistical approach. The study results suggest that students’ social influence and motivation positively influence collaborative learning; directly and indirectly, students are satisfied using a web-based supported collaborative learning platform to support project-based student engagement.
    • Stakeholders shaping experiences of self-funded international PhD students in UK business schools

      Mogaji, Emmanuel; Adamu, Nenadi; Nguyen, Nguyen Phong; University of Greenwich; University of Bedfordshire; University of Economics Ho Chi Minh City (Elsevier Ltd, 2021-07-22)
      Self-funded international PhD students bring substantial financial returns to universities, but they are often placed in a precarious position, caught between different identities and experiencing struggles that are peculiar to international students – liability for fees, which sometimes have to be raised during their study; visa restrictions that affect employability; and the solitary journey of their doctoral study. It is, therefore, important to recognise their unique positioning and understand how their experiences are being shaped and can be improved. Using qualitative data obtained through semi-structured interviews with 26 self-funded international PhD students in UK business schools, the analysis identified variations in experiences based on gender, marital status, and university group as significant to self-funded PhD students' experiences. The study also adopted the theory of student persistence and the multidimensional value-based approach to identify the role of university administrative systems, supervisors, fellow PhD students, social networks, families, and self-funded PhD students as key stakeholders shaping students’ learning experiences and maintaining their engagement, influencing completion rates, and affecting post-graduation outcomes. This study extended the existing knowledge on international student experiences and doctoral education, presenting vital implications for a range of stakeholders, including universities, post-graduate and business schools, academic and professional bodies, supervisors, and policymakers.
    • A dual attention network based on efficientNet-B2 for short-term fish school feeding behavior analysis in aquaculture

      Chen, Yingyi; Yang, Ling; Yu, Huihui; Cheng, Yuelan; Mei, Siyuan; Duan, Yanqing; Li, Daoliang (2021-07-08)
      Fish school feeding behavior analysis based on images can provide important information for aquaculture managers to make effective feeding decision. However, it is a challenging task due to intra-class variation, cross-occlusion, and unbalanced image categories in real high-density industrial farming. At present, most of the existing works on fish school feeding behavior are limited because they seem to ignored the spatial relationship between the region of interest in fish feeding images. To address this research gap, we propose a dual attention network with efficientnet-b2 for fine-grained short-term feeding behavior analysis of fish school. The algorithm includes EfficientNet-B2 network and two parallel attention modules, which focus on the feature extraction of the feeding region. In addition, several training strategies, such as mish activation function, ranger optimizer, label smoothing, and cosine annealing, are employed to improve the algorithm performance. Especially, label smoothing technique is used to address the problem of image class imbalance. To evaluate the effectiveness of our method, performance of proposed algorithm is analyzed on fish school feeding behavior dataset and it is also compared with benchmark Convolutional Neural Networks (CNNs) including AlexNet, VGG, Inception, ResNet, Densenet, SENet, and MobileNet. Comprehensive experimental results show that proposed algorithm achieves very good results in terms of the accuracy (the test accuracy is 89.56% on datasets), precision, parameters and floating point operations per second (FLOPS), compared with the benchmark classification algorithm. Therefore, we proposed method can be integrated into aquacultual vision system to guide farmers to plan their feeding strategy.
    • Meal for two: a typology of co-performed practices

      Khanijou, Ratna; Cappellini, Benedetta; Hosany, Sameer; University of Bedfordshire; Durham University; Royal Holloway University of London (Elsevier Inc., 2021-06-19)
      Drawing on practice theory, this ethnographic study investigates how meal practices are co-performed by 13 newly cohabiting couples. Findings reveal how practices previously performed by individual consumers become co-performed through a synergetic and chronologically multi-phased process. Disruption, the first phase, is characterised by misalignments of individually performed practices and their elements. The second phase, incorporation, is characterised by initial collective re-alignments of practices and their elements. The third phase, synergetic outcomes, shows three different ways in which alignments can shape a co-performed practice, namely blending, combining and domineering. Theoretically this paper offers two contributions to practice theory and domestic meal consumption. It reveals the synergetic process through which meal practices become co-performed over time and provides a typology of co-performed practices.
    • Understanding airline organizational attractiveness using interpretive structural modelling

      Vatankhah, Sanaz; Ilkhanizade, Shiva; University of Bedfordshire; Cyprus International University (Akdeniz University, 2021-06-18)
      This study investigates whether and how key components of organizational attractiveness are interrelating to impose the maximum positive impact on the air transportation job market. An expert panel was shaped to gauge judgments regarding the driving power of each criterion over the other. The results of Interpretive Structural Modelling (ISM) revealed that organizational and job characteristics are the main criteria with the most driving power in the model fostering perceived fit. In addition, corporate branding and corporate social responsibility (CSR) demonstrated the highest dependence on the other criteria. The results were further validated through Impact Matrix Cross-reference Multiplication to a classification (MICMAC). The hierarchical pattern of study findings offers theoretical contributions to the study of organizational attractiveness. Practical implications of the results and study limitations are also provided.
    • In favor of large classes: a social networks perspective on experiential learning

      Kofinas, Alexander K.; Tsay, Crystal Han-Huei; University of Bedfordshire; University of Greenwich (SAGE Publications Inc., 2021-06-15)
      Most of the literature has viewed large classes as a problem and a challenge. Furthermore, large classes are often presented to be an obstacle to students’ experiential learning and a multitude of solutions can be found in the literature to manage large classes; solutions that include innovative technologies, alternative assessment designs, or expanding the capacity of delivery. This conceptual paper advocates that large classes, when used intentionally as a pedagogical tool, can be a powerful means for socialized and experiential learning for our students. In this work we connect the phenomenon of large classes with social network theory and concepts to re-conceptualize large classes as a social micro-cosmos consisting of a multitude of interconnected student communities. On this conceptual basis we offer three positive features of large classes: (i) higher levels of freedom for students to learn in their own terms (ii) learning from a diverse body of students and (iii) the provision of meaningful experiences of learning. We conclude with suggestions that should enable educators in large classes shift from an individualistic psychology-based model of experiential learning to a sociological model of experiential learning.
    • Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making

      Cao, Guangming; Duan, Yanqing; Edwards, John S.; Dwivedi, Yogesh Kumar; Ajman University; University of Bedfordshire; Aston University; Swansea University (Elsevier, 2021-06-08)
      While using artificial intelligence (AI) could improve organizational decision-making, it also creates challenges associated with the “dark side” of AI. However, there is a lack of research on managers’ attitudes and intentions to use AI for decision making. To address this gap, we develop an integrated AI acceptance-avoidance model (IAAAM) to consider both the positive and negative factors that collectively influence managers’ attitudes and behavioral intentions towards using AI. The research model is tested through a large-scale questionnaire survey of 269 UK business managers. Our findings suggest that IAAAM provides a more comprehensive model for explaining and predicting managers’ attitudes and behavioral intentions towards using AI. Our research contributes conceptually and empirically to the emerging literature on using AI for organizational decision-making. Further, regarding the practical implications of using AI for organizational decision-making, we highlight the importance of developing favorable facilitating conditions, having an effective mechanism to alleviate managers’ personal concerns, and having a balanced consideration of both the benefits and the dark side associated with using AI.
    • Information sharing and business analytics in global supply chains

      Ramanathan, Usha; Ramanathan, Ramakrishnan; Nottingham Trent University; University of Bedfordshire (Elsevier, 2021-05-17)
      The importance of collaboration in business and transparent information exchange among supply chain partners, have been topics of discussion for several years. Global businesses such as Walmart and P&G have focused on collaborative relationships with downstream partners (buyers) and upstream partners (suppliers) to meet customers’ demands. While supply chain collaborations support transparent information exchange, business analytics is proving to be the source of generating business intelligence to all supply chain partners for production planning, logistics, and distribution in this digital era. This chapter highlights the fact that businesses with transparent inventory and demand information could realize cost reductions and improve service levels using sensible business analytics. However, creating a platform to share the information and developing trustworthy partnerships are paramount. Our study discusses a few examples from current businesses to bring out the importance of collaboration by (1) sharing information with all supply chains partners and (2) moving from traditional ordering to an automated system with the support of business analytics and supply chain collaboration.
    • Knowledge sharing for enhanced performance in the HEIs using a conceptual framework

      Khilji, Nasrallah; Duan, Yanqing; Tehrani, Jasmine; ; University of Bedfordshire (North American Business Press, 2021-04-30)
      Knowledge sharing is an essential management practice that provides a sustainable competitive advantage in a vibrant and dynamic economy (Kaur, 2019). To achieve an enhanced performance in the Higher Education Institutions (HEIs), it is essential to make sure that the teaching and learning system is determined by knowledge sharing approach (Nair and Munusami, 2019). The Higher Education Institutions are required to consider how they could better share knowledge from experts who have it to learners who need to get the best of such expertise (Darling-Hammond et al., 2019). This study examines the knowledge sharing behaviour among academics and leaners in the HEIs by providing a better understanding for their enhanced performance. This is aimed to comprehend the individual acts of knowledge creation and the collective efforts of knowledge sharing adapted in the HEIs towards continuous improvement. A literature review is carried out to propose a conceptual framework of knowledge sharing for enhanced performance in the HEIs.
    • Retail analytics: store segmentation using rule-based purchasing behaviors analysis

      Bilgic, Emrah; Cakir, Ozgur; Kantardzic, Mehmed; Duan, Yanqing; Cao, Guangming; Iskenderun Technical University; Marmara University; University of Louisville; University of Bedfordshire; Ajman University (Taylor and Francis, 2021-04-29)
      Retailers are facing challenges in making sense of the significant amount of data for better understanding of their customers. While retail analytics plays an increasingly important role in successful retailing management, comprehensive store segmentation based on a Data Mining-based Retail Analytics is still an under-researched area. This study seeks to address this gap by developing a novel approach to segment the stores of retail chains based on “purchasing behavior of customers” and applying it in a case study. The applicability and benefits of using Data Mining techniques to examine purchasing behavior and identify store segments are demonstrated in a case study of a global retail chain in Istanbul, Turkey. Over 600K transaction data of a global grocery retailer are analyzed and 175 stores in İstanbul are successfully segmented into five segments. The results suggest that the proposed new retail analytics approach enables the retail chain to identify clusters of stores in different regions using all transaction data and advances our understanding of store segmentation at the store level. The proposed approach will provide the retail chain the opportunity to manage store clusters by making data-driven decisions in marketing, customer relationship management, supply chain management, inventory management and demand forecasting.
    • Identifying the configurational conditions for marketing analytics use in UK SME

      Cao, Guangming; Duan, Yanqing; Tian, Na (Emerald, 2021-04-09)
      While marketing analytics can be used to improve organizational decision-making and performance significantly, little research exists to examine how the configurations of multiple conditions affect marketing analytics use. This study draws on configuration theory to investigate marketing analytics use in small and medium-sized enterprises (SMEs). This research employs fuzzy set qualitative comparative analysis using data collected from a survey of 187 managers in UK SMEs. The key findings show that (1) configurations of multiple conditions provide alternative pathways to marketing analytics use; and (2) the configurations for small firms are different from those for medium-sized firms. The research results are based on several key configurational factors and a single key-informant method to collect subjective data from UK SME managers. The study helps SMEs to understand that marketing analytics use is influenced by the interaction of multiple conditions, that there are alternative pathways to marketing analytics use, and that SMEs should choose the configuration that fits best with their organizational contexts.