• aaa Clarivate analytics (formerly produced by Thomson Reuters) journal metrics and AJPH

      Shelepak A. (American Public Health Association Inc., 2018-12-12)
    • Adoption of business analytics and impact on performance: a qualitative study in retail

      Ramanathan, Ramakrishnan; Philpott, Elly; Duan, Yanqing; Cao, Guangming; University of Bedfordshire (Taylor & Francis, 2017-07-11)
      This paper describes a qualitative study aimed at understanding issues faced by retail firms when they start a project of implementing Business Analytics (BA) and understanding the impact of BA implementation on business performance. Our study is informed by prior literature and the theoretical perspectives of the Technology-Organisation-Environment (TOE) framework but is not constrained by this theory. Using case studies of nine retailers in the UK, we have found support for the link between TOE elements and adoption. In addition, we have identified more interesting involvement of additional factors in ensuring how firms could maximise benefit derived from BA and traditional TOE factors that potentially could have additional impacts different from the ones. For example, there appears a link between adoption of BA and business performance (including performance in terms of environmental sustainability), and this link is moderated by the level of BA adoption, IT integration and trust.
    • Analysis of factors affecting UK small and medium enterprises' corporate sustainability behaviour

      Oyedepo, Gbemisola Aramide; Duan, Yanqing; Bentley, Yongmei; He, Qile (2017-08-01)
    • Application of graphene-based materials for detection of nitrate and nitrite in water—a review

      Li, Daoliang; Wang, Tan; Li, Zhen; Xu, Xianbao; Wang, Cong; Duan, Yanqing; China Agricultural University; University of Bedfordshire (MDPI AG, 2019-12-20)
      Nitrite and nitrate are widely found in various water environments but the potential toxicity of nitrite and nitrate poses a great threat to human health. Recently, many methods have been developed to detect nitrate and nitrite in water. One of them is to use graphene-based materials. Graphene is a two-dimensional carbon nano-material with sp2 hybrid orbital, which has a large surface area and excellent conductivity and electron transfer ability. It is widely used for modifying electrodes for electrochemical sensors. Graphene based electrochemical sensors have the advantages of being low cost, effective and efficient for nitrite and nitrate detection. This paper reviews the application of graphene-based nanomaterials for electrochemical detection of nitrate and nitrite in water. The properties and advantages of the electrodes were modified by graphene, graphene oxide and reduced graphene oxide nanocomposite in the development of nitrite sensors are discussed in detail. Based on the review, the paper summarizes the working conditions and performance of different sensors, including working potential, pH, detection range, detection limit, sensitivity, reproducibility, repeatability and long-term stability. Furthermore, the challenges and suggestions for future research on the application of graphene-based nanocomposite electrochemical sensors for nitrite detection are also highlighted.
    • 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.
    • Assessing the value of hotel online reviews to consumers

      Reino, Sofia; Massaro, Maria Rita; University of Bedfordshire (Springer, 2016-03-03)
      Previous research studied the impact of travel online reviews. However, this is quantitative and lacks of conceptual frameworks to ensure consistency. Only a few of these have considered influencing variables (i.e. characteristics of the review and the reader, and surrounding circumstances). Some of their findings are conflicting, which could relate to the lacking of a consistent approach. This study will only focus on online reviews about accommodation establishments. Its aim is to gain an understanding of the value of accommodation online reviews, through a qualitative study. A conceptual framework, based on consumer-perceived value theory, has been developed and face-to-face interviews with accommodation online review readers have been undertaken. The results suggest that the value of reviews is primary epistemic and partially functional, but limited emotional and social value has been reported. Furthermore, the elements eliciting the different value dimensions and additional variables influencing on their value (such as information search patterns) are identified.
    • 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. ​​​​​​​
    • The availability of critical minerals for China’s renewable energy development: an analysis of physical supply

      Wang, Jianliang; Yang, Lifang; Bentley, Yongmei; Lin, Jingli (Springer, 2020-01-13)
      In the context of depletion of fossil energy and environmental impacts of its use, society has begun to develop vigorously renewable energy (RE). As a result, concerns about the availability of critical minerals used in RE systems have been raised. This paper uses a generalized Weng model to analyze the long-term production of critical minerals for China’s RE development. In our pessimistic case, the results show that the production of most of the minerals investigated for China will peak before 2030, with a relatively high decline rate thereafter. This is an unsustainable situation for China’s RE development unless large and growing quantities of these minerals can be imported. In our optimistic case, although this delays the peak date only slightly, it significantly increases the maximum production rate and lowers the subsequent decline rate. The impacts of many other factors on production, and the implications of China’s domestic minerals production on world’s minerals supply chain, are also analyzed. We conclude that both China and the world should pay close attention to the potential supply risks to critical minerals. Possible measures in response are suggested for both China and the world.
    • Basics of analytics and big data

      Dinesh Kumar, U.; Pradhan. M.; Ramanathan, Ramakrishnan (CRC Press, Taylor & Francis, 2017-07-17)
      In this book chapter, we introduce fundamental concepts of analytics and big data and role of analytic in multi-criteria decision making.  Three components of analytics, namely, descriptive, predictive and prescriptive analytics are explained using different applications of these three components.  The chapter also introduces big data challenges and technology used for handling big data problems.  The primary objective of the chapter is to introduce basic concepts in analytics and big data to the readers.
    • Best practices in the cost engineering of through-life engineering services in Life Cycle Costing (LCC) and Design To Cost (DTC)

      Baguley, Paul (Springer, 2020-04-30)
      This chapter defines a number of Cost Engineering challenges from industry and their potential best practice solutions as industry case studies and industry practices surveys completed during the previous 5 years. In particular Life Cycle Costing in the context of upgrade and revamp in the process industry and also an example of design for full life cycle target cost for the manufacturing industry. Life Cycle Costing of complex long life cycle facilities is exemplified by identification and development of a life cycle costing of oil refineries through a survey of 15 companies and full life cycle experts and a review of the literature. Life cycle costing practices and a standardised life cycle cost breakdown structure are identified. Design to full life cycle target cost practices have been identified in the development of a full life cycle cost estimating tool for marine radar systems. In particular a survey of 17 companies and a case study with a marine radar systems company has identified specific practices useful in developing products to full life cycle target cost. In planning for future Through Life Engineering Services it is proposed that the collection of cost data and the understanding of Cost Engineering practices is a potential competitive advantage.
    • Big data analytics using multiple criteria decision making models

      Ramanathan, Ramakrishnan; Mathirajan, Muthu; Ravindran, A. Ravi (CRC Press, Taylor & Francis, 2017-07-17)
      The field of multi-criteria decision-making (MCDM) assumes special importance in this era of Big Data and Business Analytics (BA). Big Data and BA are relatively recent phenomena, and studies on understanding the power of Big Data and BA are rare with a few studies being reported in the literature. While there are several textbooks and research materials in the field of multi-criteria decision-making (MCDM), there is no book that discusses MCDM in the context of emerging Big Data. Thus, the present volume addresses the knowledge gap on the paucity of MCDM models in the context of Big Data and BA. The book has 13 chapters. The first chapter is Festschrift in Honor of Professor Ravindran (which has been the primary purpose for developing this book) by Professor Adedeji B Badiru. The rest of the volume is broadly divided into three sections. The first section, consisting of chapters 2 and 3, is intended to provide the basics of MCDM and Big Data Analytics. The next section, comprising of Chapters 4-10, discusses applications of traditional MCDM methods. The last section, comprising of the final three chapters, discusses the application of more sophisticated MCDM methods, namely, Data Envelopment Analysis and the Analytics Hierarchy Process. The chapters are aimed to illustrate how MCDM methods can be fruitfully employed in exploiting Big Data, and it is hoped that this book will kindle further research avenues in this exciting new field.  This book will serve as a reference for MCDM methods, Big Data, and linked applications.
    • Board gender diversity and organizational determinants: empirical evidence from a major developing country

      Saeed, Abubakr; Sameer, Muhammad; Raziq, Muhammad Mustafa; Salman, Aneel; Hammoudeh, Shawkat (Taylor and Francis, 2018-09-21)
      This article seeks to identify and analyze the organizational determinants of women presence on Indian corporate boards. Using a sample set of 294 Indian firms between years 2004–2014, Tobit regression analysis indicates that firm size, family ownership and affiliation with the high-tech sector exhibit positive association with the number of female directors on corporate boards. Further, we do not find any significant impact of state-ownership on the number of women on those boards. Notably, the effects of the organizational variables are more pronounced for the proportion of female non-executive directors, as compared to female executive directors. We conclude that understanding the organizational characteristics in conjunction with business environment can provide useful insights into state of board gender diversity, particularly in developing countries.
    • Cabbalistic cases: demystifying generalizability

      Deigh, Linda; Farquhar, Jillian Dawes; London Metropolitan University; University of Bedfordshire (2015-07-01)
      Case study research is concerned with in-depth and within context knowledge which is generated empirically. As such it is well suited to address complex marketing problems thus advancing theory in the discipline. In spite of these benefits, case studies are rarely published in marketing journals thus depriving the discipline of rich insights and opportunities to build new theory. This relatively poor showing of case study research may be attributable to a perceived lack of rigour with one particular criticism being that case study findings are not generalizable. This paper sets out to investigate the generalizability ‘problem’ in case study research. It finds that strategic case selection and specificity in the bounding of cases enable the findings of a study to be extended to similar contexts and generalized to theory.
    • Can a values reframing of ISO14001:2015 finally give business an effective tool to tackle climate change

      Williams, Sarah (Emerald, 2018-09-05)
      Purpose: This chapter argues that the revised ISO 14001:2015 environmental standard for business constitutes a fundamental reframing of business engagement with environmental management.   Design: Drawing on the values framework of Shalom Schwartz, it is demonstrated how the revised standard represents a values shift away from self-limiting approaches based on power, control and conformity. Instead, the revised standard frames environmental management into the language of achievement and openness where managers are encouraged to work together, make a difference, lead, inspire, engage and find innovative and creative solutions.   Findings: Drawing on empirical research with SME managers, the significance of this values reframing is illustrated. Managers drawing on power and conformity to engage with environmental actions tended to focus on short-term actions that demonstrated quick financial pay back or reputations wins. This is contrasted with managers drawing on achievement and self-direction values who took a longer-term view to making a difference and working with others to find innovative solutions to complex problems. Originality and Value: It is posited that this reframing represents a significant opportunity for business generally and for the environmental profession specifically to develop the skills and approaches required to tackle climate change and other sustainability related concerns. 
    • Can environmental investments benefit environmental performance? the moderating roles of institutional environment and foreign direct investment

      Li, Ruiqian; Ramanathan, Ramakrishnan (John Wiley & Sons Ltd, 2020-06-18)
      Contribution of environmental investments (EI) to environmental performance (EP) is a lively topic for environmental researchers across the world. In spite of huge amount of research, there is still lack of clarity on the moderating factors that affect the role played by EI. In this study, we distinguish EI into pollution control investments (PCI) and pollution prevention investments (PPI). We further investigate whether institutional environment and foreign direct investment (FDI) can play their moderating effects both on the relationship between EI and EP and on the relationships between different types of investments and EP or not. The results indicate that EI has a positive effect on EP. More specifically, PPI plays a stronger positive role in EP, but PCI does not have a significant effect on EP. In addition, both institutional environment and FDI can strengthen the positive impact of EI on EP. The increase of EI in regions with better institutional environment or high FDI can lead to greater improvement in EP. These moderating effects of institutional environment and FDI are also confirmed on the link between PPI and EP. In summary, our results reinforce the existing views that EI, and specifically PPI, can improve EP, but further contribute to the understanding of the positive moderating roles played by the institutional environment and FDI on the link between EI and EP.
    • 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.