Pages: 1303 - 1319 Abstract: Industrial Management & Data Systems, Volume 118, Issue 6, Page 1303-1319, July 2018. Purpose Due to stringent regulations on carbon emissions, green manufacturing has become a critical issue in manufacturers’ strategic planning. Manufacturers are greening production through carbon abatement activities. The purpose of this paper is to investigate the factors that influence the effects of carbon abatement on environmental productivity growth. Design/methodology/approach Using data envelopment analysis with directional distance function, this study examines productivity growth associated with carbon abatement under regulated and unregulated production technologies. A pollution abatement index (PAI) is constructed for determining the effects of carbon abatement on environmental productivity growth. Panel data of 18 European countries in paper and pulp and coke sectors are collected for the analysis. Findings The empirical findings reveal that carbon abatement may positively or negatively affect environmental productivity growth which is dependent on the nature of technology in a sector, the innovation capabilities of a country and environmental regulations. Originality/value Conventional approaches in measuring productivity changes do not normally take undesired outputs (e.g. carbon emissions) into consideration. This study contributes to literature by constructing a PAI that considers productivity changes under a joint production technology (where both desired and undesired outputs are considered). The findings enhance current understandings on the effectiveness of carbon abatement activities and help managers establish corporate environmental strategies to adopt green manufacturing. Citation: Industrial Management & Data Systems PubDate: 2018-08-08T01:08:40Z DOI: 10.1108/IMDS-03-2018-0102
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose Managing risks is becoming a highly focused activity in the health service sector. In particular, due to the complex nature of processes in the pharmaceutical industry, several risks have been associated to its supply chains. The purpose of this paper is to identify and analyze the risks occurring in the supply chains of the pharmaceutical industry and propose a decision model, based on the Analytical Hierarchy Process (AHP) method, for evaluating risks in pharmaceutical supply chains (PSCs). Design/methodology/approach The proposed model was developed based on the Delphi method and AHP techniques. The Delphi method helped to select the relevant risks associated to PSCs. A total of 16 sub risks within four main risks were identified through an extensive review of the literature and by conducting a further investigation with experts from five pharmaceutical companies in Bangladesh. AHP contributed to the analysis of the risks and determination of their priorities. Findings The results of the study indicated that supply-related risks such as fluctuation in imports arrival, lack of information sharing, key supplier failure and non-availability of materials should be prioritized over operational, financial and demand-related risks. Originality/value This work is one of the initial contributions in the literature that focused on identifying and evaluating PSC risks in the context of Bangladesh. This research work can assist practitioners and industrial managers in the pharmaceutical industry in taking proactive action to minimize its supply chain risks. To the end, the authors performed a sensitivity analysis test, which gives an understanding of the stability of ranking of risks. Citation: Industrial Management & Data Systems PubDate: 2018-09-17T12:30:46Z DOI: 10.1108/IMDS-10-2017-0465
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is twofold, first providing researchers with an overview about the uncertainties occurred in procurement including applicable approaches for analyzing different uncertain scenarios, and second proposing directions to inspire future research by identifying research gaps. Design/methodology/approach Papers related to supply chain risk management and procurement risk management (PRM) from 1995–2017 in several major databases are extracted by keywords and then further filtered based on the relevance to the topic, number of citations and publication year. A total of over 156 papers are selected. Definitions and current approaches related to procurement risks management are reviewed. Findings Five main risks in procurement process are identified. Apart from summarizing current strategies, suggestions are provided to facilitate strategy selection to handle procurement risks. Seven major future challenges and implications related PRM and different uncertainties are also indicated in this paper. Research limitations/implications Procurement decisions making under uncertainty has attracted considerable attention from researchers and practitioners. Despite the increasing awareness for risk management for supply chain, no detail and holistic review paper studied on procurement uncertainty. Managing procurement risk not only need to mitigate the risk of price and lead time, but also need to have sophisticated analysis techniques in supply and demand uncertainty. Originality/value The contribution of this review paper is to discuss the implications of the research findings and provides insight about future research. A novel research framework is introduced as reference guide for researchers to apply innovative approach of operations research to resolve the procurements uncertainty problems. Citation: Industrial Management & Data Systems PubDate: 2018-09-06T12:40:08Z DOI: 10.1108/IMDS-10-2017-0469
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to identify electronic word-of-mouth (eWOM) customers from customer reviews. Thus, firms can precisely leverage eWOM customers to increase their product sales. Design/methodology/approach This research proposed a framework to analyze the content of consumer-generated product reviews. Specific algorithms were used to identify potential eWOM reviewers, and then an evaluation method was used to validate the relationship between product sales and the eWOM reviewers identified by the authors’ proposed method. Findings The results corroborate that online product reviews that are made by the eWOM customers identified by the authors’ proposed method are more related to product sales than customer reviews that are made by non-eWOM customers and that the predictive power of the reviews generated by eWOM customers are significantly higher than the reviews generated by non-eWOM customers. Research limitations/implications The proposed method is useful in the data set, which is based on one type of products. However, for other products, the validity must be tested. Previous eWOM customers may have no significant influence on product sales in the future. Therefore, the proposed method should be tested in the new market environment. Practical implications By combining the method with the previous customer segmentation method, a new framework of customer segmentation is proposed to help firms understand customers’ value specifically. Originality/value This study is the first to identify eWOM customers from online reviews and to evaluate the relationship between reviewers and product sales. Citation: Industrial Management & Data Systems PubDate: 2018-09-06T12:39:11Z DOI: 10.1108/IMDS-09-2017-0418
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to investigate how different logistics resources and resource patterns, such as logistics infrastructure, logistics location, logistics knowledge and logistics information, affect logistics capabilities and operational performance. Design/methodology/approach Based on data collected from 273 companies in China, this study examines impacts of individual logistics resource dimensions on resource integration capability, customer service capability and operational performance through contingency approach. Furthermore, three logistics resource patterns are identified and linked with resource integration capability and operational performance through configuration approach. Findings Contingency results show that different logistics resources have different impacts on resource integration capability and operational performance. Configuration results reveal that companies’ capabilities and operational performance vary for different logistics resource patterns: the high-uniform pattern has a better resource integration capability and operational performance than other patterns, while all logistics resource patterns have similar customer service capability levels. Research limitations/implications Future studies should examine other resource capabilities and performance indicators of companies and extend this study to other countries and regions. Originality/value This study contributes to the logistics resource literature through empirically investigating relationships among logistics resources, resource integration capability and operational performance using contingency approach, and through identifying different logistics resource patterns based on configuration approach. The findings extend the logistics resource literature, particularly on research of logistics parks in China. Citation: Industrial Management & Data Systems PubDate: 2018-09-05T10:47:30Z DOI: 10.1108/IMDS-01-2018-0024
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose Recently, various Twitter Sentiment Analysis (TSA) techniques have been developed, but little has paid attention to the microblogging feature – emojis, and few works have been conducted on the multi-class sentiment analysis of tweets. The purpose of this paper is to consider the popularity of emojis on Twitter and investigate the feasibility of an emoji training heuristic for multi-class sentiment classification of tweets. Tweets from the “2016 Orlando nightclub shooting” were used as a source of study. Besides, this study also aims to demonstrate how mapping can contribute to interpreting sentiments. Design/methodology/approach The authors presented a methodological framework to collect, pre-process, analyse and map public Twitter postings related to the shooting. The authors designed and implemented an emoji training heuristic, which automatically prepares the training data set, a feature needed in Big Data research. The authors improved upon the previous framework by advancing the pre-processing techniques, enhancing feature engineering and optimising the classification models. The authors constructed the sentiment model with a logistic regression classifier and selected features. Finally, the authors presented how to visualise citizen sentiments on maps dynamically using Mapbox. Findings The sentiment model constructed with the automatically annotated training sets using an emoji approach and selected features performs well in classifying tweets into five different sentiment classes, with a macro-averaged F-measure of 0.635, a macro-averaged accuracy of 0.689 and the MAEM of 0.530. Compared to those experimental results in related works, the results are satisfactory, indicating the model is effective and the proposed emoji training heuristic is useful and feasible in multi-class TSA. The maps authors created, provide a much easier-to-understand visual representation of the data, and make it more efficient to monitor citizen sentiments and distributions. Originality/value This work appears to be the first to conduct multi-class sentiment classification on Twitter with automatic annotation of training sets using emojis. Little attention has been paid to applying TSA to monitor the public’s attitudes towards terror attacks and country’s gun policies, the authors consider this work to be a pioneering work. Besides, the authors have introduced a new data set of 2016 Orlando Shooting tweets, which will be made available for other researchers to mine the public’s political opinions about gun policies. Citation: Industrial Management & Data Systems PubDate: 2018-09-05T10:45:51Z DOI: 10.1108/IMDS-12-2017-0582
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose There are particularly high fresh agricultural product (FAP) loss rates in actual supply chain operation and the development of FAPs e-commerce is hindered to some extent. The purpose of this paper is to achieve the coordination of three-layer FAP supply chain and maximize profit through the contracts among the supply chain members. Design/methodology/approach A three-layer FAP supply chain that consists of a fresh produce e-commerce enterprise, third-party logistics service provider (TPLSP) and community convenience store under e-commerce environment is considered. New game models are developed and optimal decisions in centralized and decentralized channel are characterized. Different contract coordination mechanisms are designed to improve the supply chain performance. Finally, computational studies are conducted. Findings The decentralized supply chain cannot be coordinated by a freshness-keeping cost-sharing contract, and it can be coordinated by a freshness-keeping cost-sharing and revenue-sharing contract. The optimal unit online selling price, unit logistics distribution price, fresh-keeping effort and unit self-collection service price can all be achieved. Practical implications The paper provides a practical guideline to managers in fresh produce industry in terms of how to cooperate with other supply chain members so as to maximize total profit and achieve Pareto improvement while also supply the freshest and safest produce to the target market under e-commerce environment. Originality/value Few studies have explored the coordination of three-layer FAP supply chain under e-commerce environment with TPLSP and community convenience store’s participation in decisions, especially considering that the market demand for FAPs is affected by freshness and unit online selling price. In this paper, all these scenarios are taken into account and corresponding mathematical models are developed. In particular, different contract coordination mechanisms are designed and examined simultaneously. Citation: Industrial Management & Data Systems PubDate: 2018-08-28T01:46:34Z DOI: 10.1108/IMDS-12-2017-0559
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose Delivering messages and information to potentially interested users is one of the distinguishing applications of online enterprise social network (ESN). The purpose of this paper is to provide insights to better understand the repost preferences of users and provide personalized information service in enterprise social media marketing. Design/methodology/approach It is accomplished by constructing a target audience identification framework. Repost preference latent Dirichlet allocation (RPLDA) topic model topic model is proposed to understand the mass user online repost preferences toward different contents. A topic-oriented preference metric is proposed to measure the preference degree of individual users. And the function of reposting forecasting is formulated to identify target audience. Findings The empirical research shows the following: a total of 20 percent of the repost users in ESN represent the key active users who are particularly interested in the latent topic of messages in ESN and fits Pareto distribution; and the target audience identification framework can successfully identify different target key users for messages with different latent topics. Practical implications The findings should motivate marketing managers to improve enterprise brand by identifying key target audience in ESN and marketing in a way that truthfully reflects personalized preferences. Originality/value This study runs counter to most current business practices, which tend to use simple popularity to seek important users. Adaptively and dynamically identifying target audience appears to have considerable potential, especially in the rapidly growing area of enterprise social media information service. Citation: Industrial Management & Data Systems PubDate: 2018-08-23T12:20:05Z DOI: 10.1108/IMDS-01-2018-0007
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose It is necessary to determine the processes affecting cloud computing service applications in supply chain management (SCM) systems in order to facilitate cloud computing service exchanges and transmission of data among supply chain members. The paper aims to discuss these issues. Design/methodology/approach Drawing on the elaboration likelihood model (ELM) and integrating the commitment trust theory, this paper develops a theoretical model using argument advantage and source credibility constructs to examine the relationships among perceived usefulness, attitude, trust and usage intention. Findings The results indicate that both the central route and the peripheral route of the ELM have a positive influence on perceived usefulness. The argument advantage has a strong influence on perceived usefulness as compared to source credibility while source credibility has a strong impact on trust. Furthermore, the perceived usefulness of cloud computing services plays a pivotal role in attitude and intention, whereas trust has a weak effect on usage intention. Originality/value The proposed model not only explores the argument that potential user evaluations of both the advantages of cloud computing services and source credibility influence their affective states, which in turn affect their usage intention, but it also examines the mediating factors that influence processes related to cloud SCM acceptance. Citation: Industrial Management & Data Systems PubDate: 2018-08-23T12:19:12Z DOI: 10.1108/IMDS-12-2017-0589
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to formulate a framework to construct a patient-specific risk score and therefore to classify these patients into various risk groups that can be used as a decision support mechanism by the medical decision makers to augment their decision-making process, allowing them to optimally use the limited resources available. Design/methodology/approach A conventional statistical model (logistic regression) and two machine learning-based (i.e. artificial neural networks (ANNs) and support vector machines) data mining models were employed by also using five-fold cross-validation in the classification phase. In order to overcome the data imbalance problem, random undersampling technique was utilized. After constructing the patient-specific risk score, k-means clustering algorithm was employed to group these patients into risk groups. Findings Results showed that the ANN model achieved the best results with an area under the curve score of 0.867, while the sensitivity and specificity were 0.715 and 0.892, respectively. Also, the construction of patient-specific risk scores offer useful insights to the medical experts, by helping them find a trade-off between risks, costs and resources. Originality/value The study contributes to the existing body of knowledge by constructing a framework that can be utilized to determine the risk level of the targeted patient, by employing data mining-based predictive approach. Citation: Industrial Management & Data Systems PubDate: 2018-08-23T12:16:32Z DOI: 10.1108/IMDS-12-2017-0579
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to investigate the effect of including letter repetition commonly found within social media text and its impact in determining the sentiment scores for two major airlines in Malaysia. Design/methodology/approach A Sentiment Intensity Calculator (SentI-Cal) was developed by assigning individual weights to each letter repetition, and tested it using data collected from official Facebook pages of the airlines. Findings Evaluation metrics indicate that SentI-Cal outperforms the baseline tool Semantic Orientation Calculator (SO-CAL), with an accuracy of 90.7 percent compared to 58.33 percent for SO-CAL. Practical implications A more accurate sentiment score allows airline services to easily obtain a better understanding of the sentiments of their customers, hence providing opportunities in improving their airline services. Originality/value Proposed mechanism calculates sentiment intensity of social media text by assigning individual weightage to each repeated letter and exclamation mark thus producing a more accurate sentiment score. Citation: Industrial Management & Data Systems PubDate: 2018-08-23T12:14:32Z DOI: 10.1108/IMDS-07-2017-0300
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to examine the multiple paths of absorptive capacity’s (AC) effect on business performance (BP): direct effect and indirect effects through innovation and mass customization (MC). Design/methodology/approach The authors examined the role of innovation and MC capability on the relationship between AC and BP. Data from 278 Chinese firms in four manufacturing industries collected by questionnaire are used to empirically examine the proposed model by Structural equation modeling. Findings The results show that AC can directly enhance BP, and indirectly through innovation and MC capability. The study also finds that MC capability has a stronger mediating effect than innovation. Originality/value Prior studies have not yet built linkages between AC and BP with both innovation and MC’s effects. This study provides empirical evidence of the effects of AC on BP and three paths are discussed including AC’s direct effect and indirect effects through innovation and MC to firm’s BP. This study provides a new direction for executives to better understand the movement of knowledge along supply chain and in intra-organizational environment, and of the importance of innovation and MC capability since they are essential carriers for BP enhancement. Citation: Industrial Management & Data Systems PubDate: 2018-08-20T12:24:11Z DOI: 10.1108/IMDS-09-2017-0416
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose Three adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements. The purpose of this paper is to deal with the decision-making problem between these modes by proposing a novel multi-classification method. This method recommends appropriate adjustment modes for the assembly lines faced with different customer orders through machine learning from historical data. Design/methodology/approach The decision-making method uses the classification model composed of an input layer, two intermediate layers and an output layer. The input layer describes the assembly line in a knowledge-intensive manner by presenting the impact degrees of production parameters on line performances. The first intermediate layer provides the support vector data description (SVDD) of each adjustment mode through historical data training. The second intermediate layer employs the Dempster–Shafer (D–S) theory to combine the posterior classification possibilities generated from different SVDDs. The output layer gives the adjustment mode with the maximum posterior possibility as the classification result according to Bayesian decision theory. Findings The proposed method achieves higher classification accuracies than the support vector machine methods and the traditional SVDD method in the numerical test consisting of data sets from the machine-learning repository and the case study of a diesel engine assembly line. Practical implications This research recommends appropriate adjustment modes for MMALs in response to customer demand changes. According to the suggested adjustment mode, the managers can improve the line performance more effectively by using the well-designed optimization methods for a specific scope. Originality/value The adjustment mode decision belongs to the multi-classification problem featured with limited historical data. Although traditional SVDD methods can solve these problems by providing the posterior possibility of each classification result, they might have poor classification accuracies owing to the conflicts and uncertainties of these possibilities. This paper develops a novel classification model that integrates the SVDD method with the D–S theory. By handling the conflicts and uncertainties appropriately, this model achieves higher classification accuracies than traditional methods. Citation: Industrial Management & Data Systems PubDate: 2018-08-17T11:46:49Z DOI: 10.1108/IMDS-01-2017-0014
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to understand how knowledge spillover and access in a supply chain network enhance the credit quality in supply chain finance (SCF) of small and medium enterprises (SMEs). Design/methodology/approach Drawing on network theory and a knowledge-based view (KBV) of SCF, this paper proposes a theoretical model and tests it using survey data from a sample of 248 SMEs in China. Findings The main finding is that both strong ties and dense ties within a supply chain network have positive effects on SMEs’ credit quality, and these effects are mediated by knowledge spillover and knowledge access. Interestingly, knowledge spillover is found to have a positive effect on knowledge access. Originality/value This paper is the first to investigate the relationship between supply chain network and supply chain financing from a KBV. The proposed model captures the complexity in the interaction among different attributes of supply chain networks (i.e. strong ties and dense ties), different aspects of knowledge transfer (i.e. knowledge spillover and knowledge access) and SMEs’ credit quality in SCF. The results not only show the importance of SMEs’ supply chain networks to SMEs’ credit quality but also contribute to the understanding of the KBV in SCF. Citation: Industrial Management & Data Systems PubDate: 2018-08-17T09:46:49Z DOI: 10.1108/IMDS-01-2018-0049
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to illustrate an original decision-support tool (DST) that aids 3PL managers to decide on the proper warehouse management system (WMS) customization. The aim of this tool is to address to the three main issues affecting such decision: the cost of the information sharing, the scarce visibility of the client’s data and the uncertainty of quantifying the return from investing into a WMS feature. Design/methodology/approach The tool behaves as a digital twin of a WMS. In addition, it incorporates a set of WMS’s features based both on heuristics and optimization techniques and uses simulation to perform what-if multi-scenario analyses of alternative management scenarios. In order to validate the effectiveness of the tool, its application to a real-world 3PL warehouse operating in the sector of biomedical products is illustrated. Findings The results of a simulation campaign along an observation horizon of ten months demonstrate how the tool supports the comparison of alternative scenarios with the as-is, thereby suggesting the most suitable WMS customization to adopt. Practical implications The tool supports 3PL managers in enhancing the efficiency of the operations and the fulfilling of the required service level, which is increasingly challenging given the large inventory mix and the variable clients portfolio that 3PLs have to manage. Particularly, the choice of the WMS customization that better perform with each business can be problematic, given the scarce information visibility of the provider on the client’s processes. Originality/value To the author’s knowledge, this paper is among the first to address a still uncovered gap of the warehousing literature by illustrating a DST that exploits optimization and simulation techniques to quantify the impacts of the information availability on the warehousing operations performance. As a second novel contribution, this tool enables to create a digital twin of a WMS and foresee the evolution of the warehouse’s performance over time. Citation: Industrial Management & Data Systems PubDate: 2018-08-17T09:43:48Z DOI: 10.1108/IMDS-01-2018-0033
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to identify the key facilitators and inhibitors of fitness wearable technology (FWT) adoption and the intention to recommend this technology. Design/methodology/approach An innovative and integrated research model was developed by combining constructs from two well-established theoretical models, the extended unified theory of acceptance and use of technology (UTAUT2) and diffusion of innovation ( DOI ). The proposed research model was empirically validated using data collected from 392 respondents in China. The data was analyzed using the partial least squares method, a statistical analysis technique based on structural equation modeling. Findings The results indicate that performance expectancy, effort expectancy, social influence, habit, compatibility and innovativeness have significant direct and indirect effects on FWT adoption and the intention to recommend it. The significance of people’s intention to recommend FWT to others in social networking sites (e.g. Facebook, Weibo, and WeChat) is also confirmed. Practical implications The findings may facilitate the design and implementation of FWT products, applications and functionalities that can achieve high consumer acceptance and positive recommendations in social networks. Originality/value This study is among the first to investigate FWT adoption from behavioral, social and environmental perspectives. It also highlights the importance of social marketing campaigns and suggests directions of future wearable technology adoption research. Citation: Industrial Management & Data Systems PubDate: 2018-08-17T09:40:37Z
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to propose a dedication-constraint-temptation (DCT) model to study the factors influencing customers’ loyalty to mobile data service (MDS) providers. The DCT model explicitly explores the important yet overlooked role of alternative attractiveness (the temptation-based mechanism) as a mediator and the boundary condition of their interrelationships (e.g. relationship length). The model also integrates new and established antecedents of customer-based brand equity (C-BBE) (the dedication-based mechanism) and switching barriers (the constraint-based mechanism). Design/methodology/approach The proposed model is tested using partial least squares–structural equation modeling with a sample of 331 MDS users. Findings The results indicate that C-BBE has an indirect effect on customer loyalty (via alternative attractiveness) in both relationship groups (shorter- vs longer-term). However, the indirect effect of switching barriers on customer loyalty only exists in longer established relationships. The results from multi-group analysis reveal that the effect of switching barriers on alternative attractiveness significantly differs across groups. In addition, customer value anticipation and procedural switching costs appear to be the most salient antecedents of C-BBE and switching barriers for both groups. Originality/value This study makes an incremental contribution by incorporating the temptation-based mechanism as a mediator and relationship length as a moderator into the dedication-constraint model. This study also extends the information systems and brand management literatures by demonstrating the strategic importance of customer value anticipation in the information and communication technology brand equity-building. Citation: Industrial Management & Data Systems PubDate: 2018-08-16T07:42:05Z DOI: 10.1108/IMDS-07-2017-0326
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to examine the role of supply chain integration (SCI) in improving sustainability management practices (SMPs) and performance. Design/methodology/approach Based on data collected from 931 manufacturing firms in multiple countries and regions, the authors conducted a structural equation modeling analysis to test the proposed hypotheses. Findings The findings suggest that supplier and customer integration are vital enablers for both intra- and inter-organizational SMPs. The results also reveal that both intra- and inter-organizational SMPs are significantly and positively associated with sustainability performance (i.e. economic, environmental and social performance) and function as complements to jointly enhance environmental and social performance. Originality/value This study incorporates SCI into the sustainability literature, providing a new perspective on sustainability and supply chain management research. Citation: Industrial Management & Data Systems PubDate: 2018-08-15T11:59:49Z DOI: 10.1108/IMDS-01-2018-0004
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to develop a decision support system to consider geographic information, logistics information and greenhouse gas (GHG) emission information to solve the proposed green inventory routing problem (GIRP) for a specific Taiwan publishing logistics firm. Design/methodology/approach A GIRP mathematical model is first constructed to help this specific publishing logistics firm to approximate to the optimal distribution system design. Next, two modified Heuristic-Tabu combination methods that combine savings approach, 2-opt and 1-1 λ-interchange heuristic approach with two modified Tabu search methods are developed to determine the optimum solution. Findings Several examples are given to illustrate the optimum total inventory routing cost, the optimum delivery routes, the economic order quantities, the optimum service levels, the reorder points, the optimum common review interval and the optimum maximum inventory levels of all convenience stores in these designed routes. Sensitivity analyses are conducted based on the parameters including truck loading capacity, inventory carrying cost percentages, unit shortage costs, unit ordering costs and unit transport costs to support optimal distribution system design regarding the total inventory routing cost and GHG emission level. Originality/value The most important finding is that GIRP model with reordering point inventory control policy should be applied for the first replenishment and delivery run and GIRP model with periodic review inventory control policy should be conducted for the remaining replenishment and delivery runs based on overall simulation results. The other very important finding concerning the global warming issue can help decision makers of GIRP distribution system to select the appropriate type of truck to deliver products to all retail stores located in the planned optimal delivery routes depending on GHG emission consumptions. Citation: Industrial Management & Data Systems PubDate: 2018-08-15T11:58:29Z DOI: 10.1108/IMDS-11-2017-0533
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose This paper posits, develops and tests the effects of general environmental awareness factors on consumers’ intentions in the Environmental Awareness Purchasing Intention Model. Environmental awareness is tested within the context of purchasing vehicles and, thus, vehicle relevant factors (perceived quality and self-image) are also included. Further, the purpose of this paper is to explain the relative contribution of general environmental awareness factors on purchasing intentions compared to quality and self-image. Design/methodology/approach The main thrust of the research involved a survey of 248 young automobile buyers from a large public university in Texas, USA. The data were analyzed using both multiple regression and partial least square-structural equation modeling (PLS-SEM). Findings Environmental attitude has a positive impact on purchasing intention, while perceived control toward environmentally responsible behavior has a negative impact. Results also show a positive impact of quality and self-image on intention to purchase environmentally friendly cars. Furthermore, the results reveal that environmental awareness factors explain less variance than perceived quality. At the same time, the results show that environmental factors, relatively, explain more variance in the decision-making process than self-image. Originality/value This is the first study to examine the relationship between general environmental awareness and the intention to purchase an environmentally friendly car. This study contributes to sustainable consumption literature in operations management. The paper also provides insights for developing managerial guidelines for car manufacturers based on the understanding of factors influencing environmentally friendly car purchasing intentions. Citation: Industrial Management & Data Systems PubDate: 2018-08-15T10:56:20Z DOI: 10.1108/IMDS-12-2017-0591
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose This purpose of this paper is to evaluate Millennials’ intention to use service mobile apps and assess gender as a moderator. Design/methodology/approach An extended technology acceptance model framework that includes information quality and self-efficacy guides this research. PLS-SEM is used to evaluate the data and test the hypotheses. Findings The study reveals that information quality, self-efficacy, perceived ease of use and usefulness, and attitude influence Millennials’ intentions to use service mobile apps. Additionally, gender is found to partially moderate the results. Practical implications Service companies that rely on mobile apps to deliver services ought to consider the disparities among the Millennial generation, increasing the likelihood that Millennial customers will adopt service mobile apps and that they receive acceptable customer experiences. Originality/value This paper examines the factors influencing adoption and use of service mobile apps among Millennials and examines gender as a moderator. Additionally, guidelines for service mobile app design are included. Citation: Industrial Management & Data Systems PubDate: 2018-08-15T10:53:39Z DOI: 10.1108/IMDS-10-2017-0479
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to compare the relative impacts of trust and risk on individual’s transaction intention in consumer-to-consumer (C2C) e-marketplaces from both the buyers’ and the sellers’ perspectives. Design/methodology/approach Two surveys were used to collect data regarding buyers’ and sellers’ perceptions and transaction intentions at a typical C2C e-marketplace. Partial least squares was used to analyze the data. A complementary qualitative study was conducted to triangulate the results from the quantitative study. Findings Institution-based trust (IBT) exerts a stronger influence on transaction intentions for buyers than for sellers. Sellers perceive a stronger impact of trust in intermediary (TII) than buyers on transaction intentions. The impacts of perceived risk in transactions are not different between buyers and sellers. Furthermore, IBT mediates the impacts of TII and perceived risk on transaction intentions for buyers. Research limitations/implications The results indicate that the impacts of trust and risk on transaction intention in e-marketplaces do differ between buyers and sellers. This suggests a need to further investigate the buyer–seller difference in online transactions. Practical implications Intermediaries need to focus on different types of trust-building mechanisms when attracting buyers and sellers to make transactions in the e-marketplace. Originality/value C2C e-marketplaces cannot survive without participation from both buyers and sellers. Most prior research is conducted from the buyers’ perspective. This research sets a starting point for future research to further explore the differences between buyers’ and sellers’ behavior in C2C e-commerce environments. Citation: Industrial Management & Data Systems PubDate: 2018-08-15T10:50:19Z DOI: 10.1108/IMDS-10-2017-0489
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to identify antecedents of e-finance continuance intention with Alibaba’s Yu’E Bao as an example. Design/methodology/approach An online questionnaire was used to collect the data (n=293), and partial least squares structural equation modeling was employed for data analysis. Four e-finance features (perceived reputation, website quality, e-finance familiarity and situational normality) are introduced with trust acting as a moderator between the users’ satisfaction and continuance intention to use an e-finance platform. Findings The results find that website quality, familiarity and situational normality can influence perceived ease of use (PEOU) and perceived usefulness (PU). PEOU and PU, together with reputation, are positively associated with confirmation which further leads to satisfaction. The positive effects that satisfaction and trust have on e-finance continuance intention are confirmed, and trust is found to be a significant moderator on the relationship between satisfaction and continuance intention. Practical implications The findings can be used to guide e-finance providers to improve their platform design and services to retain users. Originality/value This study combines the theory of trust, Technology Acceptance Model and Expectations Confirmation Theory to investigate the factors that influence the continuance intention in the context of e-finance in China. Citation: Industrial Management & Data Systems PubDate: 2018-08-14T09:12:54Z DOI: 10.1108/IMDS-12-2017-0602
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose Although lean thinking is deemed to be a gold standard of modern production management, a lot of scepticism still remains regarding its applicability in small- and medium-sized enterprises (SMEs). The purpose of this paper is to understand the perception of lean in SMEs and establish the relationship between lean adoption and operational performance. Design/methodology/approach With the help of a survey, data were collected from 425 SMEs in India and analyzed using structural equation modeling. Findings Operational performance of the firms was found to be positively related to lean implementation. Originality/value This study also furnishes practitioners with a better understanding of lean thinking in SMEs and its impact on performance. Citation: Industrial Management & Data Systems PubDate: 2018-08-14T02:05:57Z DOI: 10.1108/IMDS-02-2018-0088
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to empirically examine the factors that influence the acceptance and use of mobile casual games. Design/methodology/approach A theoretical model is proposed based on the theory of reasonable action, the uses and gratifications theory, the network externalities (NEs) paradigm and the human–computer interaction literature. An empirical study was conducted through an online survey of mobile casual gamers in Spain, using a convenience sample. The proposed model was tested by an analysis of the collected data through a structural equation model using the partial least squares method. Findings The results indicate that human–mobile game interaction and NEs have a significant indirect impact on intention to play (IP), through utilitarian, hedonic and relational motivations. In addition, the full mediation effect of attitude was found between these constructs and IP, which is a very important determinant of actual use. Originality/value This study is among the few that focuses on users’ acceptance of mobile games apps, the features of which differ significantly from personal computer and console games. It highlights the effects of human–game interaction and NEs on the adoption of mobile casual games. Hence, the study contributes to the theoretical and practical understanding of the factors that lead users to adopt an entertainment mobile application. Citation: Industrial Management & Data Systems PubDate: 2018-08-14T02:04:56Z DOI: 10.1108/IMDS-11-2017-0544
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to provide implementation insights and implications regarding the strategic orientations of servitization by testing its impacts on firm performance, including financial performance and customer service performance. Design/methodology/approach Empirical research is conducted using an online survey disseminated to manufacturing firms in Southeast China. This research develops and verifies a strategic fit framework to understand the relationship between the strategic orientation of servitization and service innovation (SI), and its resulting impacts on firm performance. Findings The results show that service orientation (SO) has direct positive impacts on firm performance in the manufacturing sector. Customer orientation (CO) and learning orientation (LO) have no direct impact on firm performance, although they have indirect impacts on it via the mediating role of SI capability. Moreover, SO has a similar indirect impact on firm performance via SI capability. Research limitations/implications The survey focuses only on China; future studies should verify whether different cultural backgrounds impact the research results. Practical implications The results suggest that firms should build up three strategic orientations (SO, CO and LO) for implementing servitization to facilitate SI capability and, thus, to improve firm performance. Originality/value This research contributes to enhancing the theory of servitization by developing a strategic fit model of servitization and revealing the impact mechanism of servitization in the manufacturing sector. Citation: Industrial Management & Data Systems PubDate: 2018-08-14T02:03:36Z DOI: 10.1108/IMDS-10-2017-0485
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to understand how the consistency between objective and perceived environmental uncertainty might affect supply chain flexibilities that cope with supply chain risk. Design/methodology/approach This study adopted a case study of comparative four companies in order to obtain an in-depth knowledge of the environmental conditions under which the companies implement different types of supply chain risk management (SCRM) strategies: logistics flexibility and relationship flexibility. Findings The case analysis not only distinguished the different effects of objective and perceived environmental uncertainty on supply chain flexibility, but also established the propositions about the effects of the consistency between objective and perceived environmental uncertainty on logistics flexibility and relationship flexibility in SCRM. Originality/value In principle, supply chain flexibility aims to cope with complex and turbulent environments. Yet, empirical findings about the effects of environmental uncertainty on supply chain flexibility are inconclusive. This study addressed this question by differentiating between objective and perceived environmental uncertainty as well as between logistics and relationship supply chain flexibilities. Citation: Industrial Management & Data Systems PubDate: 2018-08-13T08:28:43Z DOI: 10.1108/IMDS-09-2017-0410
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose Despite existing advances in information technology infrastructure services (ITISs), there remains no consensus in the literature regarding what dimensions or criteria are best suited for the measurement of their quality. The purpose of this paper is to fill this gap, proposing a methodological approach to measuring the quality of ITISs, as perceived by users. Design/methodology/approach Cronbach’s α and item-total correlations were used to measure the reliability of the questionnaire; multiple linear regression analysis was employed to determine the items (criteria) most related to the quality of ITISs; and finally, importance–performance analysis was conducted to determine the most critical criteria in service provision. An exploratory study was conducted to evaluate the quality of ITISs at a Brazilian university. Findings Control of the activities of the information technology (IT) team, periodic maintenance of hardware and software, the security policy and the skill of the labor affect the quality of services most, relative to the dimensions of infrastructure, people and processes. Practical implications The results indicate what dimensions and items should be considered by IT managers to improve the quality of ITISs. Special attention should be paid to the effective use of equipment, software and network infrastructure: it should be ensured that these are in good working order and can be utilized by users who expect to be trained to take advantage of their functions. Originality/value This paper shows how to integrate relevant techniques to assess the quality of ITISs. An original set of criteria to evaluate the quality of ITISs, derived from a systematic review of the literature, is suggested. Citation: Industrial Management & Data Systems PubDate: 2018-08-10T03:00:21Z DOI: 10.1108/IMDS-09-2017-0415
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to explore how a foreign invested manufacturing company’s (FIMC) components sourcing process evolves in order to improve its supply chain outcomes in the context of China’s processing trade. Design/methodology/approach Grounded in the theory bases of the international sourcing process and supply chain integration, this study utilizes a single-case-study approach with a small- to medium-sized FIMC engaged in China’s processing trade. Findings This study identifies three stages of the components sourcing process: simple assembly stage, components localization stage and supply chain integration stage. In addition, the case study suggests that the type of processing trade evolves from processing with supplied materials to processing with imported materials as the sourcing process proceeds through the three stages and the internal and external environments change. Originality/value To our knowledge, this paper is the first to focus on an FIMC’s components sourcing process in the context of China’s processing trade. It contributes to a better understanding of how FIMCs progress through the components sourcing process and apply different types of processing trade in China to maximize their supply chain outcomes. Citation: Industrial Management & Data Systems PubDate: 2018-08-10T02:58:41Z DOI: 10.1108/IMDS-11-2017-0528
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to propose a risk-sharing model to coordinate the decision-making behavior of players in a cassava-based bioethanol supply chain under random yield and demand environment, so as to mitigate the yield and demand uncertainty risk and improve the bioethanol supply chain resiliency and performance. Design/methodology/approach The decision-making behavior under three models, namely, centralized model, decentralized model and risk-sharing model, are analyzed. An empirical test of the advantages and feasibility of the proposed risk-sharing model, as well as the test of yield uncertainty risk, risk-sharing coefficients and randomly fluctuating cassava market price on the decision-making behavior and performances are provided. Findings Though the proposed risk-sharing model cannot achieve the supply chain performance in the centralized model, it does help to encourage the farmers and the company to increase the supply of cassava and achieve the Pareto improvement of both players compared to the decentralized model. In particular, these improvements will be enlarged as the yield uncertainty risk is higher. Practical implications The findings will help decision makers in the bioethanol supply chain to understand how to mitigate the yield uncertainty risk and improve the supply chain resiliency under yield and demand uncertainty environment. It will also be conducive to ensure the supply of feedstock and the development of the bioethanol industry. Originality/value The proposed risk-sharing model incorporates the yield uncertainty risk, the random market demand and the hierarchical decision-making behavior structure of the bioethanol supply chain in the model. Citation: Industrial Management & Data Systems PubDate: 2018-08-10T02:57:01Z DOI: 10.1108/IMDS-09-2017-0429
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to investigate models and methods for managing supply chain risks and delays in construction projects. Design/methodology/approach The study mainly employs quantitative analysis in order to identify disruptions in construction supply chains. It also uses paradigms of simulation modeling, which are suitable for risk assessment and management. Both qualitative and quantitative data were collected through a literature review and details of specific construction projects, respectively. A dynamic modeling method was used, and the model was provided with an event-based simulation. Simulation modeling was used to measure the performance of the system. Findings The study shows the benefits of applying the dynamic modeling method to a construction project. Using event-based simulation, it was found that construction delays influence both the magnitude and the probability of disruption. This method contributes to the existing theoretical foundations of risk management practices, since it also considers the time factor. This method supplements the Monte Carlo statistical simulation method, which has no time representation. Using empirical analysis, the study proposes increasing the safety stock of construction materials at the distribution center, so as to mitigate risks in the construction supply chain. Research limitations/implications The research considers a single case of a hypothetical construction project. The simulation models represent a simple supply chain with only one supplier. The calculations are based on the current economic scenario, which will of course change over time. Practical implications The outcomes of the study show that the introduction of a safety stock of construction materials at the distribution center can prevent supply chain disruption. Since the consideration of risks at all stages of construction supply chain is essential to investors, entrepreneurs and regulatory bodies, the adoption of new approaches for their management during strategic planning of the investment projects is essential. Originality/value This dynamic modeling method is used in combination with the Monte Carlo simulation, thus, providing an explicit cause-and-effect dependency over time, as well as a distributed value of outcomes. Citation: Industrial Management & Data Systems PubDate: 2018-08-10T02:55:21Z DOI: 10.1108/IMDS-09-2017-0422
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose Based on the factors derived from the structural embeddedness theory, the purpose of this paper is to investigate the antecedents to the adoption intention for eSCM from two perspectives: buyer and supplier. The six factors examined in this study are product complexity, product specificity, the number of partners, relationship duration, dependence disadvantage and dependence advantage. Design/methodology/approach A questionnaire was designed to collect data from Mainland China with 206 valid data received. Regression analysis was employed to test the hypotheses proposed. Findings The differences in the results show that product specificity and dependence disadvantage are significant determinants of eSCM adoption for buyers’ perspective, but not from that of suppliers. In addition, product complexity and dependence advantage (although negatively associated with eSCM adoption) are significant for suppliers, but not for buyers. Number of partners and relationship duration are significant determinants from both perspectives. Originality/value This research contributes to understanding on how the factors embedded in an exchange structure influence the adoption of eSCM from the angles of both the buyers and suppliers. We fill the research gap in the existing literature by recognizing the differences in the roles of the buyer and supplier regarding the antecedents to eSCM adoption. Citation: Industrial Management & Data Systems PubDate: 2018-08-07T12:28:06Z DOI: 10.1108/IMDS-10-2017-0496
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose Risk management is crucial for all organizations, especially those in the global supply chain network. Failure may result in huge economic loses and damage to company reputation. Risk assessment usually involves quantitative and qualitative decisions. The purpose of this paper is to apply fuzzy logic to capture and inference qualitative decisions made in the House of Risk (HOR) assessment method. Design/methodology/approach In the existing HOR model, aggregate risk potential (ARP) is calculated by the risk event times the risk agent value and its occurrence. However, these values are usually obtained from interviews, which may involve subjective decisions. To overcome this shortcoming, a fuzzy-based approach is proposed to calculate ARP instead of the current deterministic approach. Findings Risk analyses are conducted in five major categories of risk sources: internal, global environment, supplier, customer and third-party logistics provider. Moreover, each category is further divided into different sub-categories. The results indicate that the fuzzy-based HOR successfully inferences the inputs of the risk event, risk agents and its occurrence, and can prioritize the risk agents in order to take proactive decisions. Practical implications The proposed fuzzy-based HOR model can be used practically by manufacturers in the global supply chain. It provides a framework for decision makers to systematically analyze the potential risks in different categories. Originality/value The proposed fuzzy-based HOR approach improves the traditional approach by more precise modeling of the qualitative decision-making process. It contributes to a more accurate reflection of the real situation that manufacturers are facing. Citation: Industrial Management & Data Systems PubDate: 2018-08-07T11:28:38Z DOI: 10.1108/IMDS-10-2017-0467
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose In seaport industries, vessel arrival delay is inevitable because of numerous factors, e.g. weather, delay due to the previous stop, etc. The period of delay can be as short at 15 min of as long as a few days. This causes disruption to the planned sea operation operations, and more importantly, to the resources utilization. In traditional berth allocation and quay crane assignment problems (BA-QCA), the risk of vessel arrival delay has not been considered. Accordingly, the purpose of this paper is to employ a proactive planning approach by taking into consideration the vessel arrival delay into the optimization of BA-QCA problems. Design/methodology/approach In the existing BA-QCA problems, vessel arrival time is usually deterministic. In order to capture the uncertainties of arrival delay, this paper models the arrival time as a probability distribution function. Moreover, this paper proposes to model the delay risk by using the period between the expected arrival time and the expected waiting time of a vessel. Lastly, the authors propose a new modified genetic algorithm and a new quay crane assignment heuristic to maximize the schedule reliability of BA-QCA. Findings A number of numerical experiments are conducted. First of all, the optimization quality of the proposed algorithm is compared with the traditional genetic algorithm for verifying the correctness of the optimization approach. Then, the impact of vessel arrival delay is tested in different scenarios. The results demonstrate that the impact of vessel arrival delay can be minimized, especially in the situations of high vessel to potential berth ratio. Research limitations/implications The proposed vessel arrival modeling approach and the BA and QCA approach can increase the operations efficiency of seaports. These approaches can increase the resource utilization by reducing the effect of vessel arrival delay. In other words, this can improve the throughput of seaport terminals. Originality/value This paper proposes to minimize the delay risk based on the conditional probability of the vessel completion time based on the previous vessel at the assigned berth. This modeling approach is new in literature. Citation: Industrial Management & Data Systems PubDate: 2018-08-03T10:08:37Z DOI: 10.1108/IMDS-12-2017-0563
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to scrutinize the performance effects of supply chain risk management (SCRM). Besides financial performance, two aspects of operational performance are examined: operational efficiency and flexibility. Moreover, the authors explore the moderating role of supplier integration in the relationship between SCRM and operational performance. Design/methodology/approach A survey-based methodology was adopted. Based on the data from an international survey, this study applied the structural equation modeling and latent moderated structural equations approach to test the hypotheses. Findings The results indicate that SCRM positively influences both operational efficiency and flexibility, and has an indirect effect on financial performance. In addition, supplier integration enhances the impact of SCRM on operational flexibility, but does not moderate the relationship between SCRM and operational efficiency. Originality/value This study extends the existing literature by providing a comprehensive analysis of the performance effects of SCRM. It also provides managerial insights on both risk management and supplier integration. Citation: Industrial Management & Data Systems PubDate: 2018-08-03T09:18:28Z DOI: 10.1108/IMDS-09-2017-0427
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to consider the management of hazards arising from the make-buy choice in the face of radical technological change. This sourcing choice can lead to distinctive exchange and hierarchical hazards. This study’s main interest is in investigating the research question “How can firms reduce those distinctive exchange and hierarchical hazards arising from the make-buy sourcing strategy when dealing with radical technological change'” Design/methodology/approach The author develops hypotheses that the in-house retention of outsourced component knowledge will likely reduce exchange hazards arising from the buy strategy choice. And prior exploratory technological experience will likely reduce hierarchical hazards arising from the make strategy choice. The author explores the US mountain bicycle industry from 1980 to 1992 to test the developed hypotheses. For endogeneity arising from the make-buy sourcing decision, the author uses Heckman’s two-stage switching regression model. Findings The major findings are that the in-house retention of outsourced component knowledge and prior exploratory technological experience is distinctive moderating factors improving performance of a buy strategy and a make strategy, respectively. Originality/value Since the extant literature tends to focus on which of the two sourcing strategies provides the greatest performance advantages in the face of radical technological change, there is a strong implication to suggest that if a firm performs poorly with one sourcing decision, the firm should switch to an alternative one. Different from the expositions of the literature, this study elevates the understanding regarding how firms can improve the performance of their current sourcing orientation rather than whether they should switch from one sourcing strategy to another. Citation: Industrial Management & Data Systems PubDate: 2018-07-11T08:57:02Z DOI: 10.1108/IMDS-12-2016-0542
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to value put option contracts in hedging the risks in a supply chain consisting of a component supplier with random yield and a manufacturer facing stochastic demand for end products. Design/methodology/approach This paper adopts stochastic inventory theory, game theory, optimization theory and algorithm and MATLAB numerical simulation to investigate the manufacturer’s ordering and the supplier’s production strategies, and to study the coordination and optimization strategies in the context of random yield and demand. Findings The authors find that put options can not only facilitate the manufacturer’s order but also the supplier’s production, that is, the manufacturer and the supplier can effectively manage their involved risks and earn more expected profits by adopting put options. Further, the authors find that the single put option contract fails to coordinate such a supply chain. However, when combined with a protocol, it is able to coordinate the supply chain. Originality/value This paper is the first effort to study the intersection of put option contracts and random yield in the presence of a spot market. From a new perspective, the authors explore the supply chain coordination. The authors propose a mechanism to coordinate the supply chain under put option contracts. Citation: Industrial Management & Data Systems PubDate: 2018-07-10T02:51:18Z DOI: 10.1108/IMDS-09-2017-0393
Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose Since the handling of environmentally sensitive products requires close monitoring under prescribed conditions throughout the supply chain, it is essential to manage specific supply chain risks, i.e. maintaining good environmental conditions, and ensuring occupational safety in the cold environment. The purpose of this paper is to propose an Internet of Things (IoT)-based risk monitoring system (IoTRMS) for controlling product quality and occupational safety risks in cold chains. Real-time product monitoring and risk assessment in personal occupational safety can be then effectively established throughout the entire cold chain. Design/methodology/approach In the design of IoTRMS, there are three major components for risk monitoring in cold chains, namely: wireless sensor network; cloud database services; and fuzzy logic approach. The wireless sensor network is deployed to collect ambient environmental conditions automatically, and the collected information is then managed and applied to a product quality degradation model in the cloud database. The fuzzy logic approach is applied in evaluating the cold-associated occupational safety risk of the different cold chain parties considering specific personal health status. To examine the performance of the proposed system, a cold chain service provider is selected for conducting a comparative analysis before and after applying the IoTRMS. Findings The real-time environmental monitoring ensures that the products handled within the desired conditions, namely temperature, humidity and lighting intensity so that any violation of the handling requirements is visible among all cold chain parties. In addition, for cold warehouses and rooms in different cold chain facilities, the personal occupational safety risk assessment is established by considering the surrounding environment and the operators’ personal health status. The frequency of occupational safety risks occurring, including cold-related accidents and injuries, can be greatly reduced. In addition, worker satisfaction and operational efficiency are improved. Therefore, it provides a solid foundation for assessing and identifying product quality and occupational safety risks in cold chain activities. Originality/value The cold chain is developed for managing environmentally sensitive products in the right conditions. Most studies found that the risks in cold chain are related to the fluctuation of environmental conditions, resulting in poor product quality and negative influences on consumer health. In addition, there is a lack of occupational safety risk consideration for those who work in cold environments. Therefore, this paper proposes IoTRMS to contribute the area of risk monitoring by means of the IoT application and artificial intelligence techniques. The risk assessment and identification can be effectively established, resulting in secure product quality and appropriate occupational safety management. Citation: Industrial Management & Data Systems PubDate: 2018-07-06T11:06:23Z DOI: 10.1108/IMDS-09-2017-0384
First page: 1138 Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach to be applied in actual environment. Design/methodology/approach This model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism; and data mining association rules approach for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply the obtained knowledge to support the improvement of different types of processes. Findings The new methodology of the proposed algorithm has been evaluated in a case study and the algorithm shows its potential to determine the primary factors that have a great effect upon the final result of the entire operation comprising a number of processes. In this case example, relevant process parameters have been identified as the important factors causing significant impact on the result of final outcome. Research limitations/implications The proposed methodology requires the dependence on human knowledge and personal experience to determine the various fuzzy regions of the processes. This can be fairly subjective and even biased. As such, it is advisable that the development of artificial intelligence techniques to support automatic machine learning to derive the fuzzy sets should be promoted to provide more reliable results. Originality/value Recent study on the relevant topics indicates that an intelligent process optimization approach, which is able to interact seamlessly with the knowledge-based system and extract useful information for process improvement, is still seen as an area that requires more study and investigation. In this research, the process optimization system with an effective process mining algorithm embedded for supporting knowledge discovery is proposed for use to achieve better quality control. Citation: Industrial Management & Data Systems PubDate: 2018-08-03T09:13:48Z DOI: 10.1108/IMDS-08-2017-0347
First page: 1153 Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to propose a model to understand how and when employees’ perceived privacy violations and procedural injustice interact to predict intent to leave in the context of the use of social networking sites (SNSs) monitoring. Design/methodology/approach This study was conducted in a field setting of Facebook to frame the hypotheses in a structural equation model with partial least squares-structural equation modeling. Variables were measured empirically by administering questionnaires to full-time employed Facebook users who had experienced SNS monitoring. Findings The results showed that when an employee believed that he/she had more ability to control his/her SNS information, he/she was less likely to perceive that his/her privacy had been invaded; and when an employee believed that the transparency of the SNS data collection process was higher, he or she was more likely to perceive procedural justice in SNS monitoring. Research limitations/implications This research draws attention to the importance of intent to leave in the absence of perceived procedural justice under SNS monitoring, and the partial mediation of the perception of justice or injustice by perceived privacy violations. Practical implications For employers, the author recommends that employers come to know how to conduct SNS monitoring and data collection with limited risk of employee loss. Social implications For employees, the author suggests that SNS users learn how to control their SNS information and make sure to check their privacy settings on the SNS that they use frequently. Originality/value This study provided an initial examination and bridged the gap between employer use of SNS monitoring and employee reactions by opening a mediating and moderating black box that has rarely been assessed. Citation: Industrial Management & Data Systems PubDate: 2018-08-07T11:29:15Z DOI: 10.1108/IMDS-10-2017-0453
First page: 1170 Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to examine how interpersonal relationships (IPRs) and inter-organisational relationships (IORs) interact with each other as driving forces of supply chain integration (SCI). More specifically (the) three dimensions of IPR – personal affection, personal credibility, and personal communication – are examined in regards to how they affect inter-organisational relationships during SCI. Design/methodology/approach The research employed an exploratory multiple case study approach with four New Zealand case companies selected as the empirical basis. Data were collected through semi-structured interviews of managerial executives in relation to supply chain activities, which were triangulated with company archival data. Findings The authors found that IPRs are able to interact with IORs to influence the integration of supply chains. More specifically, IPRs influence IORs by initiating organisational relationships in the SCI context; and influences from IPR dimensions on IORs tend to be of differing magnitudes and have different evolutional paths across the whole SCI process. Originality/value This research contributes to knowledge about the roles and mechanisms through which IPRs shape and enable inter-organisational level relationships within the SCI context. Citation: Industrial Management & Data Systems PubDate: 2018-08-07T12:26:45Z DOI: 10.1108/IMDS-05-2017-0216
First page: 1192 Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to explore the impact of supplier development (SD) on supplier’s performance by sharing implicit knowledge in mentorship under the influence of supplier’s organizational culture (OC). Design/methodology/approach A survey questionnaire was employed to collect data from 226 employees of participating suppliers after conducting mentorship training at the suppliers’ site. The data were analyzed by the partial least squares structural equation modeling with software SmartPLS Ver. 3.0. Findings The empirical analysis indicates that SD by mentorship partially mediates the total effects of OC – power distance and uncertainty avoidance – on performance. It completely mediates the collaborative culture on performance. Originality/value This study may confirm that the SD program by mentorship is a viable strategy to enhance the performance of supply chain partners and the selection of suppliers. Citation: Industrial Management & Data Systems PubDate: 2018-08-08T01:01:16Z DOI: 10.1108/IMDS-05-2017-0229
First page: 1209 Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to build a research model that examines social media privacy concerns (SMPC) in relation to users’ trusting beliefs and risk beliefs. Design/methodology/approach An instrument with eight constructs (SMPC: collection, SMPC: secondary usage, SMPC: errors, SMPC: improper access, SMPC: control, SMPC: awareness, trusting beliefs and risk beliefs) was developed and administered to subjects from a mid-sized university in the USA. Collected data were analyzed using partial least square structural equation modeling. Findings The results showed that three of the six SMPC (i.e. secondary usage, improper access and awareness) were negatively and significantly associated with users’ trusting beliefs. In addition, three of the six SMPC (i.e. collection, errors and improper access) were positively and significantly associated with users’ risk beliefs. Practical implications Practical implications were aimed at the social media sites to design simple and straightforward privacy policy statements that are easy to understand; to safeguard users’ online privacy behaviors; and to develop mechanisms to protect personal information. Originality/value This study enhances the literature by contributing to a generalized knowledge of SMPC of users as they relate to their trusting beliefs and risk beliefs. Citation: Industrial Management & Data Systems PubDate: 2018-08-07T11:28:55Z DOI: 10.1108/IMDS-12-2017-0558
First page: 1229 Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose Understanding how to develop users’ word of mouth to promote a virtual community (VC) is an important issue in VC management. The purpose of this paper is to investigate the factors that lead to VC participation and promotion from a social influence perspective. Design/methodology/approach This research recruited 368 VC (i.e., Fashion Guide) members in Taiwan and used structural equation model to test research hypotheses. Findings The results showed that both shared vision and language positively influenced norm of reciprocity and social identity, respectively. Norm of reciprocity and social identity influenced VC participation intentions, and subsequently resulted in VC promotion intentions. Originality/value Prior studies neglect investigating the relationships between the three social influence processes (internalization, compliance and identification). This study contributes to the literature by proposing that internalization affects VC participation and promotion via compliance and identification. Citation: Industrial Management & Data Systems PubDate: 2018-08-07T11:28:12Z DOI: 10.1108/IMDS-10-2017-0477
First page: 1251 Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The popularity of e-commerce has increased significantly over recent years. However, this growth is not shared by all European Union states. One reason for this discrepancy is culture which impacts on e-commerce acceptance. The purpose of this paper is to reveal the effect of acculturation process on e-commerce acceptance. Design/methodology/approach Structural equation modelling is employed to test three research models: technology acceptance model, theory of planned behaviour model and motivational model. Findings The findings show that attitudes towards e-commerce change in relation to the perception of control and the influence of subjective norms, which impact intentions to use e-commerce before movement to a host country’s culture. However, its effect diminishes after the exposure to the influence of a host culture. Originality/value This is the first study to demonstrate the existence of the effect of acculturation process on mingling and migrating consumers and their changing attitudes towards e-commerce acceptance. Citation: Industrial Management & Data Systems PubDate: 2018-08-08T12:59:16Z DOI: 10.1108/IMDS-11-2017-0509
First page: 1266 Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose In today’s rapidly changing business environment, the case for accelerated innovation processes has become increasingly compelling at both a theoretical and practical level. Thus, the purpose of this paper is to propose a conceptual framework for accelerated innovation in a data-driven market environment. Design/methodology/approach This research is based on a two-step approach. First, a set of propositions concerning the best approaches to accelerated innovation are put forward. Then it offers qualitative evidence from five case studies involving world-leading firms, and explains how innovation can be accelerated in different kinds of data-driven environments. Findings The key sets of factors for accelerated innovation are: collateral structure; customer involvement; and ecosystem of innovation. The proposed framework enables firms to find ways to innovate – specifically, to make product innovation faster and less costly. Research limitations/implications The findings from this research focus on high-tech industries in China. Using several specific innovation projects to represent accelerated innovation could raise the problem of the reliability and validity of the research findings. Additional research will probably be required to adapt the proposed framework to accommodate the cultural nuances of other countries and business environments. Practical implications The study is intended as a framework for managers to apply their resources to conduct product innovation in a fast and effective way. It developed six propositions about how, specifically, data analytics and ICTs can contribute to accelerated innovation. Originality/value The research shows that firms could harvest external knowledge and import ideas across organisational boundaries. An accelerated innovation framework is characterised by a multidimensional process involving intelligence efforts, relentless data collection and flexible working relationships with team members. Citation: Industrial Management & Data Systems PubDate: 2018-08-07T11:28:16Z DOI: 10.1108/IMDS-11-2017-0542
First page: 1287 Abstract: Industrial Management & Data Systems, Ahead of Print. Purpose The purpose of this paper is to assess the influences and efficiency of a sports sponsorship in an online brand community. Design/methodology/approach The study was conducted through interviews with 609 social network users of a Spanish first league soccer team. The partial least squares (PLS) methodology was applied with a posteriori segmentation (PLS prediction-oriented segmentation (POS)). Findings The attitude toward the sponsor helps to assess the efficiency of sponsorships between companies. This variable is particularly relevant for evaluating sponsorship efficiency in online brand communities. Improving trust and assessing the sense of membership directly improves attitudes toward the team and the sponsored brands. The attitude toward the sponsor has a direct and positive impact on the purchase intentions. The use of a posteriori segmentation with the PLS–POS technique helps discriminate between groups. Research limitations/implications Among the limitations encountered, further study would require using a sample of various sports disciplines and cultures. Practical implications Specific actions and communication strategies are defined for each segment and in general to adapt communication strategies that improve identification with virtual brand communities. The study has revealed involvement-related differences resulting from the impact that engagement with the sponsored team may have on the assessed relationships. Originality/value The study of the effects of sponsorship and the use of a posteriori variables user segmentation in an online brand community are used. Citation: Industrial Management & Data Systems PubDate: 2018-08-03T09:21:52Z DOI: 10.1108/IMDS-08-2017-0349
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