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      Shelepak A. (American Public Health Association Inc., 2018-12-12)
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    • 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.
    • The affordances of business analytics for strategic decision-making and their impact on organisational performance

      Cao, Guangming; Duan, Yanqing (Pacific Asia Conference on Information Systems, 2015-12-31)
      Increasingly, business analytics is seen to provide the possibilities for businesses to effectively support strategic decision-making, thereby to become a source of strategic business value. However, little research exists regarding the mechanism through which business analytics supports strategic decisionmaking and ultimately organisational performance. This paper draws upon literature on IT affordances and strategic decision-making to (1) understand the decision-making affordances provided by business analytics, and (2) develop a research model linking business analytics, data-driven culture, decision-making affordances, strategic decision-making, and organisational performance. The model is empirically tested using structural equation modelling based on 296 survey responses collected from UK businesses. The study produces four main findings: (1) business analytics has a positive effect on decision-making affordances both directly and indirectly through the mediation of a data-driven culture; (2) decision-making affordances significantly influence strategic decision comprehensiveness positively and intuitive decision-making negatively; (3) data-driven culture has a significant and positive effect on strategic decision comprehensiveness; and (4) strategic decision comprehensiveness has a positive effect on organisational performance but a negative effect on intuitive decision-making.
    • Analysis of factors affecting UK small and medium enterprises' corporate sustainability behaviour

      Oyedepo, Gbemisola Aramide; Duan, Yanqing; Bentley, Yongmei; He, Qile (2017-08-01)
    • An analysis of the impact of business analytics on innovation

      Duan, Yanqing; Cao, Guangming (Association for Information Systems, 2015-12-31)
      The advances in Big Data and Business Analytics (BA) have provided unprecedented opportunities for organizations to innovate. With new and unique insights gained from BA, companies are able to develop new or improve existing products/services. However, few studies have investigated the mechanism through which BA contributes to a firm's innovation success. This research aims to address this gap. From an information processing and use perspective, a research model is proposed and empirically validated with data collected from a survey with UK businesses. The evidence from the survey of 296 respondents supports the research model that provides a focused and validated view on BA's contribution to innovation. The key findings suggest that BA directly improves environmental scanning which in turn helps to enhance a company's innovation in terms of new product novelty and meaningfulness. However, the effect of BA's contribution would be increased through the mediation role of data-driven culture in the organization. Data-driven culture directly impacts on new product novelty, but indirectly on product meaningfulness through environmental scanning. The findings also confirm that environmental scanning directly contributes to new product novelty and meaningfulness which in turn enhance competitive advantage. The model testing results also reveal that innovation success can be influenced by many other factors which should be addressed alongside the BA applications.
    • The applicability of best value in the Nigerian public sector

      Bukoye, Oyegoke Teslim; Norrington, Peter; University of Bedfordshire (Taylor and Francis Inc., 2014-08-14)
      We examine the applicability of Best Value practices in the Nigerian public sector and present a Best Value Model for Nigeria. We find the literature does not extend to the Nigerian context. We make contributions towards understanding stakeholder perceptions of public service delivery best practice. We show Best Value as a significant initiative for improving public service delivery. The mixed methods survey reveals Nigerian Best Value initiatives do not exist significantly, but are applicable. Outcomes are exploration of a new area for Best Value application, incorporation of implementation issues into the model and the seven-stage process for its implementation. © Taylor & Francis Group, LLC.
    • 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 appropriate grassroots technological innovation for sustainable development

      Singh, Sonal H.; Maiyar, Lohithaksha M.; Bhowmick, Bhaskar (Routledge, 2019-07-28)
      Grassroots technological innovation (GRTI) is perceived as a source of sustainable development while addressing local problems and needs of people belonging to the bottom of the economic pyramid. The fostering of sustainable development develops a need for scientific evaluation and subsequent diffusion of GRTI to ameliorate the livelihood of grassroots communities. It is, hence, the purpose of this research to assess the relative performance of different GRTIs with respect to economic, social, and environmental benefits. The empirical data for this study comprised of 32 GRTIs from the three different rural non-farm sectors in the Indian context. Analytical hierarchy process is used for deducing the relative assessment of the selected GRTI against the aforementioned performance criteria. The findings of this study offer imperative insights into the field of technology diffusion and development at the grassroots level and suggest recommendations for sustainable policy formulation.
    • 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.
    • Block teaching as the basis for an innovative redesign of the PG suite of programmes in University of Bedfordshire Business School

      Kofinas, Alexander K.; Bentley, Yongmei; Minett-Smith, Cathy; University of Bedfordshire (Editorial Universitat Politècnica de València, 2017-12-31)
      This paper aims to provide a first evaluation of the University of Bedfordshire Business School’s innovative attempt to develop a new suite of Masters Programmes that delivers in terms of academic rigor and employability requirements while providing a rich student learning experience. The new delivery is based on a block delivery model that rationalises the previous offerings by providing a smaller range of standardized large units which are more tightly integrated to each other and are part of courses with particular characteristics such as a four-tier induction system (with inductions being progressively more employabilityfocused as students’ progress from one unit to the next) and the final capstone unit where students have a choice between a traditional dissertation and an experiential final project. That common architecture is coupled with a flipped classroom delivery style, utilization of blended learning and rich peer-to-peer learning opportunities with multiple entry points providing additional students into the cohorts for each unit. Preliminary data is provided here as an early evaluation of the approach’s effectiveness and efficiency in terms of the delivery experience, the assessment strategies, the levels of student engagement and performance, as well as the experience of staff and students.