• A new rational IPA and application to cruise tourism

      Ramanathan, Ramakrishnan; Ramanathan, Usha; University of Bedfordshire; Nottingham Trent University (Elsevier Ltd, 2016-10-19)
      At least two versions of IPA, namely the simple IPA and the asymmetric IPA, are available in tourism literature (Albayrak and Caber, 2015; Pritchard and Havitz, 2006). The simple IPA involves asking customers their perceptions relating to importance of various performance criteria and how the firm has performed in terms of these criteria. The simple IPA assumes a symmetric relationship between performance in terms of various criteria and customer satisfaction. The asymmetric IPA or AIPA (Albayrak and Caber, 2013; Caber et al., 2013) recognizes that these relationships could be asymmetric and uses the three-factor theory of customer satisfaction (Matzler and Sauerwein, 2002) to argue that criteria could be basic, excitement or performance criteria and uses regression analysis. While AIPA is an improvement over IPA, AIPA calculations take into account only the magnitude of regression coefficients but not their level of significance. Further, figure 3 of Albayrak and Caber (2015) uses performance in Xaxis but impact asymmetry, not importance, in Y-axis. It is not clear why impact asymmetry should be considered synonymous to importance. In this research note, we propose a variation of AIPA and call it Rational IPA (RIPA). RIPA involves the following steps. Step 1. Collect relevant data. Step 2. Run two sets of regressions with overall customer satisfaction as the dependent variable, and performance in terms of various service criteria as dependent variables. The first set of regressions is called low performance regressions where only ratings below median levels for each criterion are considered. In contrast, the second set of regressions is called high performance regressions. As highlighted in previous studies (Hartline et al., 2003; Ramanathan and Ramanathan, 2011; Silverman and Grover, 1995), the criteria are classified based on the results of the two sets of regressions. 1 a. A critical criterion remains significant in all regressions (except for low performance in terms of the criterion). b. A desirable criterion is significant both for high performance and low performance in terms of the criterion. c. A satisfier criterion is significant for high performance regression in terms of the criterion but not significant for low performance. d. A dissatisfier criterion is not significant for high performance regression but significant for low performance in terms of the criterion. e. All other criteria are neutral criteria. Step 3. Prepare IPA matrix with the importance of criteria on the X-axis and performance (mean ratings) in the Y-axis. Step 4. Conduct IPA based on the criterion classification (importance) and achievement (performance). We demonstrate RIPA in the following steps using publicly available online data on customer ratings of cruise operations.
    • Nonintrusive methods for biomass estimation in aquaculture with emphasis on fish: a review

      Li, Daoliang; Hao, Yinfeng; Duan, Yanqing (Wiley, 2019-09-30)
      Fish biomass estimation is one of the most common and important practices in aquaculture. The regular acquisition of fish biomass information has been identified as an urgent need for managers to optimize daily feeding, control stocking densities and ultimately determine the optimal time for harvesting. However, it is difficult to estimate fish biomass without human intervention because fishes are sensitive and move freely in an environment where visibility, lighting and stability are uncontrollable. Until now, fish biomass estimation has been mostly based on manual sampling, which is usually invasive, time‐consuming and laborious. Therefore, it is imperative and highly desirable to develop a noninvasive, rapid and cost‐effective means. Machine vision, acoustics, environmental DNA and resistivity counter provide the possibility of developing nonintrusive, faster and cheaper methods for in situ estimation of fish biomass. This article summarizes the development of these nonintrusive methods for fish biomass estimation over the past three decades and presents their basic concepts and principles. The strengths and weaknesses of each method are analysed and future research directions are also presented. Studies show that the applications of information technology such as advanced sensors and communication technologies have great significance to accelerate the development of new means and techniques for more effective biomass estimation. However, the accuracy and intelligence still need to be improved to meet intensive aquaculture requirements. Through close cooperation between fisheries experts and engineers, the precision and the level of intelligence for fish biomass estimation will be further improved based on the above methods.
    • Opening the black box: the impacts of environmental regulations on technological innovation

      Li, Muyao; Zhang, Jinsong; Ramanathan, Ramakrishnan; Li, Ruiqian; ; Harbin University of Commerce; University of Bedfordshire; Heilongjiang University (MDPI, 2020-06-16)
      Whether environmental regulations (ERs) can stimulate technological innovation (TI) is the key for realizing the win-win strategy between economic development and environmental protection. This study seeks to analyze the impacts of ERs on TI. Though previous literature has highlighted that the black box of TI can be decomposed into technology investment and technology transformation, further empirical studies on such a decomposition has largely been ignored. Moreover, a detailed discussion of the links between ER and the decomposed components of TI has not been conducted in developing countries such as China. Our study attempts to address these research gaps by (i) decomposing TI using a novel DEA procedure and to further analyze the impacts of ERs on the decomposed components of TI, and (ii) apply this novel methodology to Chinese context. Accordingly, this study is conducted in two stages. First, a novel application of the slack-based Network DEA model is developed to uncover the black box of TI using Chinese data; to estimate the overall efficiency of technological innovation (TIE) and decompose it into the efficiency of technology investment (TVE) and the efficiency of technology transformation (TTE). Second, a random effect Tobit model is applied to (i) investigate both the linear and non-linear impacts of ERs on TIE in all sectors, and (ii) examine whether the impacts of ERs on TVE and TTE in different sub-processes are heterogeneous or not. Our results have brought out the benefits of decomposing TI; while technology transformation in China closely follows the trend of TI, the trend of technology investment is somewhat different. The estimation results further indicate that the impacts of ERs on TIE are non-linear. Besides, ERs have heterogeneous impacts on the decomposed components of TI. The impacts of ERs on TVE are non-linear, whereas the impacts of ERs on TTE become insignificant.
    • Operations capability, productivity and business performance: the moderating effect of environmental dynamism

      Yu, Wantao; Ramanathan, Ramakrishnan; Wang, Xingyu; Yang, Jeihui (Emerald, 2017-06-27)
      Purpose – The purpose of this study is to investigate the relationships between operations capability, productivity and business performance in the context of environmental dynamism. Design/methodology/approach – A proposed conceptual framework grounded in the resourcebased view (RBV) and dynamic capability view (DCV) is analysed using archival data from 193 automakers in the UK. Findings – The results show that operations capability, as an important dynamic capability, has a significant positive effect on productivity, which in turn leads to improved business performance. The results also suggest that productivity fully mediates the relationship between operations capability and business performance, and that environmental dynamism significantly moderates the relationship between operations capability and productivity. Practical implications – The research findings provide practical insights that will help managers develop operations capability to gain greater productivity and business performance in a dynamic environment.
    • Optimising online review inspired product attribute classification using the self-learning particle swarm-based Bayesian learning approach

      Maiyar, Lohithaksha M.; Cho, SangJe; Tiwari, Manoj Kumar; Thoben, Klaus-Dieter; Kiritsis, Dimitris (Taylor and Francis Ltd., 2018-10-24)
      Bowing to the burgeoning needs of online consumers, exploitation of social media content for extrapolating buyer-centric information is gaining increasing attention of researchers and practitioners from service science, data analytics, machine learning and associated domains. The current paper aims to identify the structural relationship between product attributes and subsequently prioritise customer preferences with respect to these attributes while exploiting textual social media data derived from fashion blogs in Germany. A Bayesian Network Structure Learning model with the K2score maximisation objective is formulated and solved. A self-tailored metaheuristic approach that combines self-learning particle swarm optimisation (SLPSO) with the K2 algorithm (SLPSOK2) is employed to decipher the highest scored structures. The proposed approach is implemented on small, medium and large size instances consisting of 9 fashion attributes and 18 problem sets. The results obtained by SLPSOK2 are compared with the particle swarm optimisation/K2score, Genetic Algorithm/K2 score and ant colony optimisation/K2 score. Results verify that SLPSOK2 outperforms its hybrid counterparts for the tested cases in terms of computational time and solution quality. Furthermore, the study reveals that psychological satisfaction, historical revival, seasonal information and facts and figure-based reviews are major components of information in fashion blogs that influence the customers.
    • Optimization of machining parameters for end milling of Inconel 718 super alloy using Taguchi based grey relational analysis

      Maiyar, Lohithaksha M.; Ramanujam, R.; Venkatesan, K.; Jerald, J.; VIT University; National Institute of Technology, India (Elsevier Ltd, 2013-11-13)
      This study investigated the parameter optimization of end milling operation for Inconel 718 super alloy with multi-response criteria based on the taguchi orthogonal array with the grey relational analysis. Nine experimental runs based on an L9 orthogonal array of Taguchi method were performed. Cutting speed, feed rate and depth of cut are optimized with considerations of multiple performance characteristics namely surface roughness and material removal rate. A grey relational grade obtained from the grey relational analysis is used to solve the end milling process with the multiple performance characteristics. Additionally, the analysis of variance (ANOVA) is also applied to identify the most significant factor. Finally, confirmation tests were performed to make a comparison between the experimental results and developed model. Experimental results have shown that machining performance in the end milling process can be improved effectively through this approach.
    • Organising for emancipation/emancipating organisations?

      Onyx, Jenny; Schwabenland, Christina; Lange, Chris; Nakagawa, Sachiko (Policy Press, 2017-10-04)
    • Overcoming the novelty effect in online gamified learning systems: an empirical evaluation of learner engagement and performance

      Tsay, Crystal Han-Huei; Kofinas, Alexander K.; Trivedi, S.K.; Yang, Yang (Wiley, 2019-10-11)
      Learners in the Higher Education context who engage with computer-based gamified learning systems often experience the novelty effect: a pattern of high activity during the gamified system's introduction followed by a drop in activity a few weeks later, once its novelty has worn off. We applied a two-tiered motivational, online gamified learning system over two years, and used three-years' worth of longitudinal data to assess students' engagement and performance in that period. Quantitative results suggest that students engaged and performed better in the gamified condition vis-à-vis the non-gamified. Likewise, they sustained engagement better in the second year compared to the first year of the gamified condition. Our qualitative data suggests that students in the second year of the gamified delivery exhibited sustained engagement, bypassing the novelty effect. Thus, we suggest that sustained engagement with computer-based gamified learning systems beyond the novelty effect relies in making the engagement meaningful and useful for the students.
    • Part segregation based on particle swarm optimisation for assembly design in additive manufacturing

      Maiyar, Lohithaksha M.; Singh, Sube; Prabhu, Vittal; Tiwari, Manoj Kumar (Taylor and Francis Ltd., 2019-05-05)
      Minimising total production time in the additive or layered manufacturing is a critical concern, and in this respect, the idea of balancing assembly time and build time is rapidly gaining research attention. The proposed work intends to provide benefit in terms of reduced lead time to customers in a collaborative environment with simultaneous part printing. This paper formulates a mixed-integer non-linear programming (MINLP) model to evaluate the near optimal threshold area and support material allocation while segregating parts for a single material additive manufacturing set-up. The resulting time minimisation model is finitely bounded with respect to support material volume, total production time and total assembly cost constraints. A novel swarm intelligence-based part segregation procedure is proposed to determine the number of part assemblies and part division scheme that adheres to cross-sectional shape, cross-sectional area, and height restrictions. The proposed approach is illustrated and evaluated for objects with regular as well as free-form surfaces using two different hypothetically simulated real size 3D models. Results indicate that the proposed approach is able to reduce the total amount of manufacturing time in comparison with single part build time for all the tested cases.
    • Performance of supply chain collaboration - a simulation study

      Ramanathan, Usha; University of Bedfordshire (Elsevier, 2013-07-16)
      In the past few decades several supply chain management initiatives such as Vendor Managed Inventory, Continuous Replenishment and Collaborative Planning Forecasting and Replenishment (CPFR) have been proposed in literature to improve the performance of supply chains. But, identifying the benefits of collaboration is still a big challenge for many supply chains. Confusion around the optimum number of partners, investment in collaboration and duration of partnership are some of the barriers of healthy collaborative arrangements. To evolve competitive supply chain collaboration (SCC), all SC processes need to be assessed from time to time for evaluating the performance. In a growing field, performance measurement is highly indispensable in order to make continuous improvement; in a new field, it is equally important to check the performance to test conduciveness of SCC. In this research, collaborative performance measurement will act as a testing tool to identify conducive environment to collaborate, by the way of pinpointing areas requiring improvements before initializing collaboration. We use actual industrial data and simulation to help managerial decision-making on the number of collaborating partners, the level of investments and the involvement in supply chain processes. This approach will help the supply chains to obtain maximum benefit of collaborative relationships. The use of simulation for understanding the performance of SCC is relatively a new approach and this can be used by companies that are interested in collaboration without having to invest a huge sum of money in establishing the actual collaboration. © 2013 Elsevier Ltd. All rights reserved.
    • Positive cultures and honest words

      Schwabenland, Christina; University of Bedfordshire (2018-10-18)
    • Predicting monthly natural gas production in China using a novel grey seasonal model with particle swarm optimization

      Li, Nu; Wang, Jianliang; Wu, Lifeng; Bentley, Yongmei; China University of Petroleum; Hebei University of Engineering; University of Bedfordshire (Elsevier, 2020-10-22)
      Accurate prediction of short and medium-term monthly natural gas production in a country is the basis for understanding the supply capacity of natural gas in different months, and for the timely adjustment of natural gas production and import strategies. In China the monthly production of natural gas has obvious seasonal and cyclical variations, thus the use of a traditional grey prediction model is not very effective. As a result, a novel grey seasonal model is proposed in this paper. This is the Particle swarm optimized Fractional-order-accumulation non-homogenous discrete grey Seasonal Model (PFSM(1,1) model). This model enhances the adaptability to seasonal fluctuation data in two ways: the seasonal adjustment of the original data, and improvement of model self-adaptability. We use monthly natural gas production data of China for the period 2013-2018 as samples to predict those for the period 2019-2023. To demonstrate the PFSM(1,1) model does indeed exhibit better predictive capability, we also use the Holt–Winters model and a seasonal GM(1,1) model to predict monthly natural gas production, and compare the results with the model proposed here. The prediction results show that monthly natural gas production in China will continue to increase throughout the 2019-2023 period, that the peak-to-valley differences in monthly production values will also increase, and that the seasonal variations in production will become increasingly pronounced. Moreover, although Chinese production of natural gas is increasing, it will still be difficult to meet future demand, and hence the gap between supply and demand will increase year by year. We conclude that China needs to develop a more complete import plan for gas to meet expected natural gas consumption.
    • Privatisation, investments and human resources in foreign firms operating in the Middle East

      Al-Husan, Faten Z. Baddar; ALHussan, Fawaz (Edward Elgar Publishing Ltd., 2016-11-25)
    • The production of garments and textiles in Bangladesh: trade unions, international managers and the health and safety of workers

      Khan, Md Asaduzzaman; Brymer, Katharine; Koch, Karl; Buckinghamshire New University; University of Bedfordshire; London South Bank University (SAGE Publications Ltd, 2020-11-16)
      This paper offers a view of working practices within the garment and textile (G&T) industry in Bangladesh. The G&T industry accounts for over 84 per cent of Bangladesh exports and is therefore viewed as key to the country’s economic development. This importance is seen in the creation of Export Processing Zones (EPZs), which were created by that state to encourage foreign investment by offering a congenial climate free from cumbersome procedures. Trade unions are outlawed in these areas. Health and safety are poor within the G&T industry. However, the Rana Plaza disaster of 2013, which caused 1,132 deaths and over 2,500 injuries, placed the issue of workplace safety on the international agenda. Arguably, this prompted a change of attitude within Bangladesh and the G&T industry towards health and safety. The presence of international managers appears to have played a significant role in improving health and safety in the working environment, however these international managers do face a range of cultural barriers, which include both language and a different perception of the value of health and safety in the workplace. This paper has adopted a mixed method of both qualitative and quantitative data, collected through interviews and questionnaire surveys within the G&T industry in Bangladesh.
    • Real-time four-dimensional trajectory generation based on gain-scheduling control and a high-fidelity aircraft model

      Obajemu, Olusayo; Mahfouf, Mahdi; Maiyar, Lohithaksha M.; Al-Hindi, Abrar; Weiszer, Michal; Chen, Jun; University of Sheffield; University of Bedfordshire; Queen Mary University of London (Elsevier Ltd, 2021-03-19)
      Aircraft ground movement plays a key role in improving airport efficiency, as it acts as a link to all other ground operations. Finding novel approaches to coordinate the movements of a fleet of aircraft at an airport in order to improve system resilience to disruptions with increasing autonomy is at the center of many key studies for airport airside operations. Moreover, autonomous taxiing is envisioned as a key component in future digitalized airports. However, state-of-the-art routing and scheduling algorithms for airport ground movements do not consider high-fidelity aircraft models at both the proactive and reactive planning phases. The majority of such algorithms do not actively seek to optimize fuel efficiency and reduce harmful greenhouse gas emissions. This paper proposes a new approach for generating efficient four-dimensional trajectories (4DTs) on the basis of a high-fidelity aircraft model and gain-scheduling control strategy. Working in conjunction with a routing and scheduling algorithm that determines the taxi route, waypoints, and time deadlines, the proposed approach generates fuel-efficient 4DTs in real time, while respecting operational constraints. The proposed approach can be used in two contexts: ① as a reactive decision support tool to generate new trajectories that can resolve unprecedented events; and ② as an autopilot system for both partial and fully autonomous taxiing. The proposed methodology is realistic and simple to implement. Moreover, simulation studies show that the proposed approach is capable of providing an up to 11% reduction in the fuel consumed during the taxiing of a large Boeing 747-100 jumbo jet.
    • Recent advances in sensor fault diagnosis: a review

      Li, Daoliang; Wang, Ying; Wang, Jinxing; Wang, Cong; Duan, Yanqing; China Agricultural University; Shandong Agricultural University; University of Bedfordshire (Elsevier, 2020-05-11)
      As an essential component of data acquisition systems, sensors have been widely used, especially in industrial and agricultural sectors. However, sensors are also prone to faults due to their harsh working environment. Therefore, the early identification of sensor faults is critical for making corrective actions to mitigate the impact. This paper provides a comprehensive review on the contemporary fault diagnosis techniques and helps researchers and practitioners to understand the current state of the art development in this emerging field. The paper introduces the common fault types and causes in sensors, and different types’ methods for fault diagnosis used in industry and agriculture sectors. It discusses the advantages and disadvantages of these methods, highlights the current challenges, and offers recommendations for future research directions.
    • Reducing edible food waste in the UK food manufacturing supply chain through collaboration

      Cao, Guangming; Shah, Pramitkumar; Ramanathan, Usha; Ajman University; University of Bedfordshire; Nottingham Trent University (Springer, 2020-07-16)
      While a third of food produced is wasted at the pre-consumer stage in the UK food manufacturing supply chain (FMSC) and has had significant negative economic and environmental impacts, many challenges remain in how to reduce edible food waste. This chapter addresses the problem of whether and to what extent FMSC collaboration could lead to the reduction of edible food waste. Evidence in the literature suggests that despite an increasing attention having been paid to reduce edible food waste, there is a scarcity of studies that focus on the relationship between FMSC collaboration and the reduction of edible food waste. Consequently, the aim of this chapter is to develop a research model that explains the relationships among FMSC collaboration, collaborative effectiveness and the reduction of edible food waste. The model is underpinned by the relation view and has been empirically tested with 122 survey responses from food manufacturing firms, using structural equation modelling. The findings indicated that FMSC collaboration has a positive effect on collaborative effectiveness, which in turn results in the reduction of edible food waste during production, processing and storage. Thus, an important implication of this chapter is that the UK FMSC members would benefit from closely collaborating with their supply chain partners to achieve greater collaborative effectiveness and thereby reducing edible food waste.
    • Relational resources for emerging markets’ non-technological innovation: insights from China and Taiwan

      Fletcher-Chen, Chavi C-Y.; Al-Husan, Faten Z. Baddar; ALHussan, Fawaz; Université Catholique de Lille; University of Bedfordshire (Emerald Group Publishing Ltd., 2017-07-03)
      Purpose: This paper aims to highlight the importance of relational resources (trust and relationship effectiveness). The authors investigate how the Chinese guanxi is utilized to create and develop service exploitation and exploration activities for adopting non-technological innovations. Design/methodology/approach: This study surveyed 252 Chinese and Taiwanese firms. The results were analyzed through structural equation model. Findings: Relational antecedents of collaborative communication and constructive conflict positively relate to trust, as well as to relationship effectiveness. Constructive conflict positively relates to exploration and exploitation. Relationship effectiveness and trust mediate two relational antecedents to exploitation. Relationship effectiveness crucially mediates two relational antecedents to exploration. Research limitations/implications: Dyadic data would be more desirable to study firm interactions. Practical implications: Chinese society perceives conflict as being detrimental to relationships. Constructive conflict enhances inter-firm trust and relationship effectiveness. Relationship effectiveness, which motivates suppliers to mobilize their guanxi network, mediates the supplier–customer interaction in broadening relationships to produce new services, as well as reinforcing networks to strengthen existing ventures. Originality/value: This study contributes to a relatively under-explored relationship effectiveness area. Chinese suppliers capitalize their guanxi networks to achieve competitive advantages in non-technological innovation.
    • 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.