• 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.
    • Applying blockchain technology to improve agri-food traceability: a review of development methods, benefits and challenges

      Feng, Huanhuan; Wang, Xiang; Duan, Yanqing; Zhang, Jian; Zhang, Xiaoshuan; University of Bedfordshire; China Agricultural University; Beijing Information Science and Technology University (Elsevier, 2020-03-11)
      Traceability plays a vital role in food quality and safety management. Traditional Internet of Things (IoT) traceability systems provide the feasible solutions for the quality monitoring and traceability of food supply chains. However, most of the IoT solutions rely on the centralized server-client paradigm that makes it difficult for consumers to acquire all transaction information and to track the origins of products. Blockchain is a cutting-edge technology that has great potential for improving traceability performance by providing security and full transparency. However, the benefits, challenges and development methods of blockchain-based food traceability systems are not yet fully explored in the current literature. Therefore, the main aim of this paper is to review the blockchain technology characteristics and functionalities, identify blockchain-based solutions for addressing food traceability concerns, highlight the benefits and challenges of blockchain-based traceability systems implementation, and help researchers and practitioners to apply blockchain technology based food traceability systems by proposing an architecture design framework and suitability application analysis flowchart of blockchain based food traceability systems. The results of this study contribute to better understanding and knowledge on how to improve the food traceability by developing and implementing blockchain-based traceability systems. The paper provides valuable information for researchers and practitioners on the use of blockchain-based food traceability management and has a positive effect on the improvement of food sustainability.
    • Artificial Intelligence (AI): multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

      Dwivedi, Yogesh K.; Hughesa, Laurie; Ismagilova, Elvira; Aarts, Gert; Coombs, Crispin; Crick, Tom; Duan, Yanqing; Dwivedi, Rohita; Edwards, John; Eirug, Aled; et al. (Elsevier, 2019-08-27)
      As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial,intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.
    • Artificial intelligence for decision making in the era of Big Data – evolution, challenges and research agenda

      Duan, Yanqing; Edwards, John S.; Dwivedi, Yogesh Kumar; University of Bedfordshire; Aston University; Swansea University (Elsevier, 2019-02-07)
      Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.
    • Automatic recognition methods of fish feeding behavior in aquaculture: a review

      Li, Daoliang; Wang, Zhenhu; Wu, Suyuan; Miao, Zheng; Du, Ling; Duan, Yanqing; ; China Agricultural University; Renmin University of China; University of Bedfordshire (Elsevier, 2020-05-23)
      Feeding is a major factor that determines the production costs and water quality of aquaculture. Analysis of fish feeding behavior forms an important part of the feeding optimization. Fish feeding has generally been performed with automatic feeding machines which can lead to excessive or insufficient feeding. Recognition of fish feeding behavior can provide valuable input for optimizing feeding quantity. Due to the complexity of the environment and the uncertainty of fish behavior, the correlation and accuracy of behavior recognition are generally low. The accurate identification of fish feeding behavior till faces substantial challenges. This paper reviews the technical methods that have been used to identify fish feeding behavior in aquaculture over the past 30 years. The advantages and disadvantages of each method under different experimental conditions and applications are analyzed. Many methods are effective at evaluating and quantifying fish feeding intensity, but the recognition accuracy still needs further improvement. It is proposed by this paper that technologies such as data fusion and deep learning has great potential for improving the recognition of fish feeding behavior.
    • An autonomous system for maintenance scheduling data-rich complex infrastructure: fusing the railways’ condition, planning and cost

      Durazo-Cardenas, Isidro; Starr, Andrew; Turner, Christopher J.; Tiwari, Ashutosh; Kirkwood, Leigh; Bevilacqua, Maurizio; Tsourdos, Antonios; Shehab, Essam; Baguley, Paul; Xu, Yuchun; et al. (Elsevier, 2018-02-22)
      National railways are typically large and complex systems. Their network infrastructure usually includes extended track sections, bridges, stations and other supporting assets. In recent years, railways have also become a data-rich environment. Railway infrastructure assets have a very long life, but inherently degrade. Interventions are necessary but they can cause lateness, damage and hazards. Every day, thousands of discrete maintenance jobs are scheduled according to time and urgency. Service disruption has a direct economic impact. Planning for maintenance can be complex, expensive and uncertain. Autonomous scheduling of maintenance jobs is essential. The design strategy of a novel integrated system for automatic job scheduling is presented; from concept formulation to the examination of the data to information transitional level interface, and at the decision making level. The underlying architecture configures high-level fusion of technical and business drivers; scheduling optimized intervention plans that factor-in cost impact and added value. A proof of concept demonstrator was developed to validate the system principle and to test algorithm functionality. It employs a dashboard for visualization of the system response and to present key information. Real track incident and inspection datasets were analyzed to raise degradation alarms that initiate the automatic scheduling of maintenance tasks. Optimum scheduling was realized through data analytics and job sequencing heuristic and genetic algorithms, taking into account specific cost & value inputs from comprehensive task cost modelling. Formal face validation was conducted with railway infrastructure specialists and stakeholders. The demonstrator structure was found fit for purpose with logical component relationships, offering further scope for research and commercial exploitation. ​​​​​​​
    • Challenges in cost analysis of innovative maintenance of distributed high value assets

      Kirkwood, Leigh; Shehab, Essam; Baguley, Paul; Amorim-Meloa, P.; Durazo-Cardenas, Isidro; Cranfield University (Elsevier, 2014-10-31)
      Condition monitoring is an increasingly important activity, but there is often little thought given to how a condition monitoring approach is going to impact the cost of operating a system. This paper seeks to detail the challenges facing such an analysis and outline the likely steps such an analysis will have to take to more completely understand the problem and provide suitable cost analysis. Adding sensors might be a relatively simple task, but those sensors come with associated cost; not only of the sensor, but of the utilities required to power them, the data gathering and processing and the eventual storage of that data for regulatory or other reasons. By adding condition monitoring sensors as a subsystem to the general system an organisation is required to perform maintenance to the new sensors sub-system. Despite these difficulties it is anticipated that for many high value assets applying condition monitoring will enable significant cost savings through elimination of maintenance activities on assets that do not need such cost and effort expended on them. Further savings should be possible through optimisation of maintenance schedules to have essential work completed at more cost efficient times.
    • A combined tactical and operational deterministic food grain transportation model: particle swarm based optimization approach

      Maiyar, Lohithaksha M.; Thakkar, Jitesh J.; Indian Institute of Technology Kharagpur (Elsevier, 2017-05-22)
      This paper proposes a combined tactical and operational two stage food grain transportation model with linear formulation in the first stage and a mixed-integer non-linear problem (MINLP) in the second stage taking the case of India. Transportation cost is minimized in both stages to fulfil a deterministic demand. First and the second stages correspond to the movement of food grains in between state and central level warehouses respectively. A novel k-parameter based method of constraint handling has been proposed. Further, the two stage MINLP formulation newly incorporates vehicle capacity constraints and proposes a generic metric for measuring vehicle utilization. First stage is solved by CPLEX and for the second stage, two population based random search techniques: Particle swarm optimization-composite particle (PSOCP) and PSO, have been employed. Experimentations on 10 different problem sets reveal that PSOCP performs marginally better than PSO with lesser standard deviation of global fitness and better solution quality with slightly higher CPU time. Later, sensitivity analysis is conducted on all ten problem sets and a decision support framework is proposed to assist potential stakeholders.
    • Cost drivers of integrated maintenance in high-value systems

      Shehab, Essam; Kirkwood, Leigh; Amorim-Melo, P.; Baguley, Paul; Cranfield University (Elsevier, 2014-10-31)
      High value systems are determined by a wide structure, where operations are considered to be one structural component. Nowadays “downtime” as a major impact in the operation costs of any system. To avoid or minimize “down-time” it is essential to match the appropriate maintenance to each failure. Therefore, it is relevant to determine the cost drivers of integrated maintenance in any system, in order to minimize the overall cost. It is common to use Value Driven Maintenance (VDM) to capture the cost drivers in maintenance. VDM is a methodology which relies in four distinct areas: Asset Utilization; Resource Allocation; Control Cost and Health and Safety and Environment. Within each category it is possible to allocate different cost drivers, building a framework for each system studied. The aim of this paper is to categorize the cost drivers of rail infrastructure networks, associating them with the maintenance preformed for each case. Furthermore, analysis of which part of the track falls under each VDM category as well as the general failure causes and effects will be included in the framework presented. Finally relating the maintenance type for each effect will provide the necessary inputs towards a cost model structure. The benefit of achieving a successful model will be the optimization of the cost in integrated maintenance of the rail infrastructure.
    • Data of the impact of aligning business, IT, and marketing strategies on firm performance

      Al-Surmi, Abdulrahman Mohamed; Cao, Guangming; Duan, Yanqing; University of Bedfordshire (Elsevier, 2019-10-15)
      The data presented in this article are related to the research article entitled “The Impact of Aligning Business, IT, and Marketing Strategies on Firm Performance” [https://www.sciencedirect.com/science/article/pii/S0019850118304449]. In order to succeed in today's competitive business environment, a firm should have a clear business strategy that is supported by other organizational strategies. While prior studies argue that strategic alignment enhances firm performance, either strategic alignment including multiple factors or strategic orientation of firms has received little attention. This study, drawing on contingency theory and configuration theory, investigates the performance impact of triadic strategic alignment among business, IT, and marketing strategies while simultaneously considers strategic orientation of firms. A research model is tested through SEM and MANOVA using data collected in a questionnaire survey of 242 Yemen managers. The findings indicate that (1) triadic strategic alignment has a positive impact on firm performance and (2) there is an ideal triadic strategic alignment for prospectors and defenders. This research contributes to strategic alignment literature and managers' understanding of how to align business, IT and marketing strategies to improve firm performance.
    • Development of an effective cost minimization model for food grain shipments

      Maiyar, Lohithaksha M.; Thakkar, Jitesh J.; Awasthi, Anjali; Tiwari, Manoj Kumar; Indian Institute of Technology Kharagpur; Concordia University (Elsevier, 2015-08-31)
      This paper makes an attempt to address the issue of food grain shipments taking a case of Indian food grain supply chain. The existing process of transportation and distribution has been represented as a four stage process. In this work only the first stage has been formulated as a bi-level nodal capacity network flow problem with linear model in the first level and a mixed integer non-linear model in the second level considering minimization of transportation costs. The model captures rail-road flexibility. Finally two variants of particle swarm optimization algorithms are used to solve the model and results are compared.
    • A dynamic capability view of marketing analytics use: evidence from UK firms

      Cao, Guangming; Duan, Yanqing; El-Banna, Alia; University of Bedfordshire (Elsevier, 2018-08-10)
      While marketing analytics plays an important role in generating insights from big data to improve marketing decision-making and firm competitiveness, few academic studies have investigated the mechanisms through which it can be used to achieve sustained competitive advantage. To close this gap, this study draws on the dynamic capability view to posit that a firm can attain sustained competitive advantage from its sensing, seizing and reconfiguring capabilities, which are manifested by the use of marketing analytics, marketing decision-making, and product development management. This study also examines the impact of the antecedents of marketing analytics use on marketing related processes. The analysis of a survey of 221 UK firm managers demonstrates: (a) the positive impact of marketing analytics use on both marketing decision-making and product development management; (b) the effect of the latter two on sustained competitive advantage; (c) the indirect effect of data availability on both marketing decision-making and production development management; and (d) the indirect effect of managerial support on marketing decision-making. The research model proposed in this study provides insights into how marketing analytics can be used to achieve sustained competitive advantage.
    • 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.
    • An empirical validation of a unified model of electronic government adoption (UMEGA)

      Dwivedi, Yogesh Kumar; Rana, Nripendra P.; Janssen, Marijn; Lal, Banita; Williams, Michael D.; Clement, Marc; Swansea University; Delft University of Technology; Nottingham Trent University (Elsevier, 2017-03-31)
      In electronic government (hereafter e-government), a large variety of technology adoption models are employed, which make researchers and policymakers puzzled about which one to use. In this research, nine well-known theoretical models of information technology adoption are evaluated and 29 different constructs are identified. A unified model of e-government adoption (UMEGA) is developed and validated using data gathered from 377 respondents from seven selected cities in India. The results indicate that the proposed unified model outperforms all other theoretical models, explaining the highest variance on behavioral intention, acceptable levels of fit indices, and significant relationships for each of the seven hypotheses. The UMEGA is a parsimonious model based on the e-government-specific context, whereas the constructs from the original technology adoption models were found to be inappropriate for the e-government context. By using the UMEGA, relevant e-government constructs were included. For further research, we recommend the development of e-government-specific scales.
    • Enhancing student learning experience with technology-mediated gamification: an empirical study

      Tsay, Crystal Han-Huei; Kofinas, Alexander K.; Luo, Jing; University of Greenwich; University of Bedfordshire (Elsevier, 2018-01-31)
      We evaluated the use of gamification to facilitate a student- centered learning environment within an undergraduate Year 2 Personal and Professional Development (PPD) course. In addition to face-to-face classroom practices, an information technology-based gamified system with a range of online learning activities was presented to students as support material. The implementation of the gamified course lasted two academic terms. The subsequent evaluation from a cohort of 136 students indicated that student performance was significantly higher among those who participated in the gamified system than in those who engaged with the nongamified, traditional delivery, while behavioral engagement in online learning activities was positively related to course performance, after controlling for gender, attendance, and Year 1 PPD performance. Two interesting phenomena appeared when we examined the influence of student background: female students participated significantly more in online learning activities than male students, and students with jobs engaged significantly more in online learning activities than students without jobs. The gamified course design advocated in this work may have significant implications for educators who wish to develop engaging technology-mediated learning environments that enhance students' learning, or for a broader base of professionals who wish to engage a population of potential users, such as managers engaging employees or marketers engaging customers.
    • Environmental pressures and performance: an analysis of the roles of environmental innovation strategy and marketing capability

      Yu, Wantao; Ramanathan, Ramakrishnan; Nath, Prithwiraj; Kent Business School; University of Bedfordshire; Leeds Becket University (Elsevier, 2016-12-16)
      The purpose of this study is to explore the relationship between environmental pressures (i.e. environmental regulation and stakeholder pressures) and performance considering the mediating role of environmental innovation strategy and the moderating role of marketing capability. Both primary data collected from 121 UK-based manufacturing firms and secondary data on financial performance of the firms is used to test the proposed relationships. The results show that environmental innovation strategy fully/partially mediates the relationship between environmental regulation/stakeholder pressures and environmental performance, and partially mediates the effect of environmental regulation on financial performance. The results also indicate that marketing capability significantly moderates the relationship between environmental regulation and environmental innovation strategy. Drawing upon contingency theory and dynamic capability view, by testing the mediation and moderation effects, the results of this study provide managers with valuable guidance for developing environmental innovation strategy.
    • Exploring the relationships between different types of environmental regulations and environmental performance : evidence from China

      Li, Ruiqian; Ramanathan, Ramakrishnan; Harbin University of Commerce; University of Bedfordshire (Elsevier, 2018-06-20)
      The literature on the relationship between environmental regulations (ERs) and environmental performance (EP) of firms has largely ignored consideration of different kinds of ERs and the potential non-linear relationship between ERs and EP. This study uses the literature to differentiate three types of ERs (command-and-control regulations, market-based regulations and informal regulations) and further investigates (i) the linear links between different types of ERs and EP, and (ii) the potential non-linear relationships. The results provide support that the links between ERs and EP are not linear for command-and-control regulations and market-based regulations but non-linear and positive. For informal regulations, both the linear and non-linear relationships are not significant. We further test the impacts of time lag effects. Command-and-control regulations have impacts on EP both in current and the preceding years, whereas market-based regulations only affect EP in current year rather than in the preceding years. It takes 2 years to see the effects of informal regulations on EP.
    • Factors affecting active participation in B2B online communities: an empirical investigation

      Gharib, Rebwar Kamal; Philpott, Elly; Duan, Yanqing; University of Bedfordshire (Elsevier, 2016-11-22)
      There is a lack of understanding on the factors affecting active participation in Business-to-Business (B2B) Online Communities (OC). To address this gap, we developed a model based on two theories: Social Exchange Theory and the Information Systems Success Model. The model was validated by using survey data collected from 40 B2B discussion forums on LinkedIn (n = 521). Our work made a number of significant contributions including an integrated model of factors affecting active participation in B2B OCs and a new validated measure for active participation. Further, we proposed several guidelines which assist B2B OC providers in building and maintaining successful communitities.
    • A generalised adoption model for services: a cross-country comparison of mobile health (m-health)

      Dwivedi, Yogesh Kumar; Shareef, Mahmud Akhter; Simintiras, Antonis C.; Lal, Banita; Weerakkody, Vishanth; Swansea University; North South University, Dhaka; McMaster University; Nottingham Trent University; Brunel University (Elsevier, 2015-07-17)
      Which antecedents affect the adoption by users is still often a puzzle for policy-makers. Antecedents examined in this research include technological artefacts from the Unified Theory of Acceptance and Use of Technology (UTAUT), consumer context from UTAUT2 and psychological behaviour concepts such as citizens' channel preference and product selection criteria. This research also investigated cultural domination on citizens' behavioural perception. The data for this study was collected among citizens from three countries: USA, Canada, and Bangladesh. The findings suggest that the UTAUT model could partially shape technology artefact behaviour and the extended UTAUT must consider specific determinants relevant to cognitive, affective, and conative or behavioural aspects of citizens. The model helps policy-makers to develop mobile healthcare service system that will be better accepted. The finding also suggests that this mobile service system should reflect a country's cultural traits. These findings basically extend the theoretical concept of UTAUT model to articulate adoption behaviour of any complex and sensitive ICT related issues like mobile healthcare system.