Showing papers in "Industrial Management and Data Systems in 2021"
TL;DR: The HTMT2 is introduced as an improved version of the traditional HTMT and provides two advantages: the ease of its computation, since HT MT2 is only based on the indicator correlations, and the relaxed assumption of tau-equivalence.
Abstract: Roemer, E., Schuberth, F., & Henseler, J. (2021). HTMT2– an improved criterion for assessing discriminant validity in structural equation modeling. Industrial Management and Data Systems. [Advanced online publication on 3 September 2021]. https://doi.org/10.1108/IMDS-02-2021-0082
67 citations
TL;DR: This study seems to be the first of its kind in which 25 digitization enablers categorized in four main categories are ranked using a multi-criteria decision-making (MCDM) tool and ranked the organizations in their SC performance based on weights/ranks of digitizationEnablers.
Abstract: The aim of this study is to identify and prioritize a list of key digitization enablers that can improve supply chain management (SCM). SCM is an important driver for organization's competitive advantage. The fierce competition in the market has forced companies to look the past conventional decision-making process, which is based on intuition and previous experience. The swift evolution of information technologies (ITs) and digitization tools has changed the scenario for many industries, including those involved in SCM.,The Best Worst Method (BWM) has been applied to evaluate, rank and prioritize the key digitization and IT enablers beneficial for the improvement of SC performance. The study also used additive value function to rank the organizations on their SC performance with respect to digitization enablers.,The total of 25 key enablers have been identified and ranked. The results revealed that “big data/data science skills”, “tracking and localization of products” and “appropriate and feasibility study for aiding the selection and adoption of big data technologies and techniques ” are the top three digitization and IT enablers that organizations need to focus much in order to improve their SC performance. The study also ranked the SC performance of the organizations based on digitization enablers.,The findings of this study will help the organizations to focus on certain digitization technologies in order to improve their SC performance. This study also provides an original framework for organizations to rank the key digitization enablers according to enablers relevant in their context and also to compare their performance with their counterparts.,This study seems to be the first of its kind in which 25 digitization enablers categorized in four main categories are ranked using a multi-criteria decision-making (MCDM) tool. This study is also first of its kind in ranking the organizations in their SC performance based on weights/ranks of digitization enablers.
50 citations
TL;DR: This study extends information processing theory to the digital context and identifies the interaction of organizational design (vertical bottom-up learning and horizontal information sharing) and digital investment (manufacturing digitalization).
Abstract: The aim of this study was to examine how manufacturing digitalization can be leveraged to promote green innovation in the digital era by investigating the effects of manufacturing digitalization on green process innovation, and thus firm performance. The authors also explored how the role of manufacturing digitalization varies with horizontal information sharing, vertical bottom-up learning and technological modularization.,Five hypotheses were examined by performing regression analyses on survey data from 334 manufacturing firms in China.,Manufacturing digitalization positively affects green process innovation, and thus firm performance. Furthermore, this positive effect is strengthened by horizontal information sharing and technological modularization and weakened by vertical bottom-up learning.,This study extends the literature rooted in the natural-resource-based view by identifying the crucial role of green process innovation and investigating the value of manufacturing digitalization for developing green capabilities in the digital era. It also contributes to this line of research by revealing contingent factors to leverage manufacturing digitalization from the information processing perspective. Furthermore, this study extends information processing theory to the digital context and identifies the interaction of organizational design (vertical bottom-up learning and horizontal information sharing) and digital investment (manufacturing digitalization).
44 citations
TL;DR: This study is the first attempt in integrating UTAUT and PCT for exploring the adoption of contact tracing apps in Australia and combines SEM and ANN for analysing the survey data, leading to better understanding of the critical determinants for the adopted apps.
Abstract: Purpose This study aims to explore the adoption of contact tracing apps through a hybrid analysis of the collected data using structural equation modelling (SEM) and artificial neural networks (ANN), leading to the identification of the critical determinants for the adoption of contact tracing apps in Australia. Design/methodology/approach A research model is developed within the background of the unified theory of acceptance and use of technology (UTAUT) and the privacy calculus theory (PCT) for investigating the adoption of contact tracing apps. This model is then tested and validated using a hybrid SEM-ANN analysis of the survey data. Findings The study shows that effort expectancy, perceived value of information disclosure and social influence are critical for adopting contact tracing apps. It reveals that performance expectancy and perceived privacy risks are indirectly significant on the adoption through the influence of perceived value of information disclosure. Furthermore, the study finds out that facilitating condition is insignificant to the adoption of contact tracing apps. Practical implications The findings of the study can lead to the formulation of targeted strategies and policies for promoting the adoption of contact tracing apps and inform future epidemic control for better emergency management. Originality/value This study is the first attempt in integrating UTAUT and PCT for exploring the adoption of contact tracing apps in Australia. It combines SEM and ANN for analysing the survey data, leading to better understanding of the critical determinants for the adoption of contact tracing apps.
39 citations
TL;DR: The empirical research shows that the constructed conceptual model can thoroughly explain the influencing factors of hotel robot acceptance, enrich the acceptance theory and provide academic support for the use and popularization of hotel service robots.
Abstract: Robots, as the crystallization of new artificial intelligence, are being applied in various fields, especially the hotel industry. They are seizing the opportunities, using technology to improve the overall quality and comprehensive competitiveness. However, they also cause many problems due to practical limitations. The purpose of this paper is to study customers' recognition and acceptance of hotel service robots to guide the successful promotion of this technology.,This paper proposed a comprehensive model based on the theory of planned behavior, the technology acceptance model and then the perceived value-based acceptance model. Exploratory factor analysis, confirmatory factor analysis, grouped regression analysis and path analysis was adopted to validate the impacts of each variable to obtain the final reliable model using data collected from hotel guests using a self-designed questionnaire.,The empirical research based on the theoretical model shows that the constructed conceptual model can thoroughly explain the influencing factors of hotel robot acceptance, enrich the acceptance theory and provide academic support for the use and popularization of hotel service robots. Among all variables, attitude, usefulness and perceived value are the factors that have the greatest impact on acceptance. They have significant differences in the effects of adjustment variables such as gender, educational level, whether hotel robots have been used, and whether other robot services have been experienced on different paths in the model.,This paper explored the customer acceptance of service robots in hotels, helped to understand the process of decision-making on service robot selection and contributed to the theoretical extension of the hospitality industry. The work guides hotel management to promote better-personalized products and services of robot technology in the hospitality industries.,The acceptance study on hotel service robots provides insight into the hotel industry to understand customers' attitudes and acceptance of emerging technology.
31 citations
TL;DR: This study found that user satisfaction with service robots in a hotel had a positive impact on user satisfaction, attitude towards the hotel and room purchase intention, and the results showed that users were most likely to accept medium-human likeness robots and least likely toaccept high– human likeness robots.
Abstract: The main purpose of this study is to investigate the impact of service robots on hotel visitors' behaviour and to verify the role of anthropomorphism(human likeness) in customer satisfaction with robots.,An online survey of 381 respondents was conducted, divided into three types of robots according to the level of anthropomorphism. The research model was thoroughly tested using the PLS-SEM method. Research model was tested thoroughly using the PLS-SEM method.,This study found that user satisfaction with service robots in a hotel had a positive impact on user satisfaction, attitude towards the hotel and room purchase intention. Moreover, our results showed that users were most likely to accept medium-human likeness robots and least likely to accept high–human likeness robots.,This study proposes influencing factors to be considered when researching hotel service robots, as well as practical suggestions for any hotel intending to use or currently using a service robot.
31 citations
TL;DR: In this article, a review of the literature on modeling supply chain resilience from 2007 to 2020 is analyzed in tandem with the vaccine supply chain manufacturing literature, and a matrix analysis is applied to seven Securities and Exchange Commission (SEC) annual filings of pharmaceutical corporations involved in COVID-19 vaccine manufacture and distribution.
Abstract: Despite rapid success in bringing SARS-CoV-2 vaccines to distribution by multiple pharmaceutical corporations, supply chain failures in production and distribution can plague pandemic recovery. This review analyzes and addresses gaps in modeling supply chain resilience in general and specifically for vaccines in order to guide researchers and practitioners alike to improve critical function of vaccine supply chains in the face of inevitable disruptions.,Systematic review of the literature on modeling supply chain resilience from 2007 to 2020 is analyzed in tandem with the vaccine supply chain manufacturing literature. These trends are then used to apply a novel matrix analysis to seven Securities and Exchange Commission (SEC) annual filings of pharmaceutical corporations involved in COVID-19 vaccine manufacture and distribution.,Pharmaceutical corporations favor efficiency as they navigate regulatory, economic and other threats to their vaccine supply chains, neglecting resilience – absorption, adaptation and recovery from inevitable and unexpected disruptions. However, explicitly applying resilience analytics to the vaccine supply chain and further leveraging emerging network science tools found in the academic literature, such as artificial intelligence (AI), stress tests and digital twins, will help supply chain managers to better quantify efficiency/resilience tradeoffs across all associated networks/domains and support optimal system performance post disruption.,This is the first review addressing resilience analytics in vaccine supply chains and subsequent extension to operational management through novel matrix analyses of SEC Filings. The authors provide analyses and recommendations that facilitate resilience quantification capabilities for vaccine supply chain managers, regulatory agencies and corporate stakeholders and are especially relevant for pandemic response, including application to the SARS-CoV-2 vaccines.
30 citations
TL;DR: In this article, a large-scale review of relevant literature on AI in B2B marketing by obtaining and comparing the most influential works based on a series of analyses, identifying five domains of research into how AI can be used for facilitating business-to-business marketing innovation, and classifying relevant articles into five different time periods in order to identify both past trends and future directions.
Abstract: Although the value of artificial intelligence (AI) has been acknowledged by companies, the literature shows challenges concerning AI-enabled business-to-business (B2B) marketing innovation, as well as the diversity of roles AI can play in this regard. Accordingly, this study investigates the approaches that AI can be used for enabling B2B marketing innovation.,Applying a bibliometric research method, this study systematically investigates the literature regarding AI-enabled B2B marketing. It synthesises state-of-the-art knowledge from 221 journal articles published between 1990 and 2021.,Apart from offering specific information regarding the most influential authors and most frequently cited articles, the study further categorises the use of AI for innovation in B2B marketing into five domains, identifying the main trends in the literature and suggesting directions for future research.,Through the five identified domains, practitioners can assess their current use of AI and identify their future needs in the relevant domains in order to make appropriate decisions on how to invest in AI. Thus, the research enables companies to realise their digital marketing innovation strategies through AI.,The research represents one of the first large-scale reviews of relevant literature on AI in B2B marketing by (1) obtaining and comparing the most influential works based on a series of analyses; (2) identifying five domains of research into how AI can be used for facilitating B2B marketing innovation and (3) classifying relevant articles into five different time periods in order to identify both past trends and future directions in this specific field.
30 citations
TL;DR: The results mean that the app design and aesthetics with the most negative reviews should be reviewed from the user's perspective rather than from the company's point of view.
Abstract: PurposeThe purpose of this study is to propose a method of measuring service quality as well as suggesting to detect customer complaints through analysis of customer online reviews of mobile bank, which is unstructured data.Design/methodology/approachThis study uses text mining approach for customer online reviews analysis. The research procedure includes: (1) extracting users' reviews for Kakao Mobile Bank, (2) pre-processing of the extracted review data, (3) analyzing the sentiment of each review, (4) measuring the service quality score of each dimension by analyzing keyword frequency and network for each polarity, (5) evaluating total score for mobile bank service quality, and (6) detecting customer complaints on online reviews.FindingsThere are some findings. First, from the customer's point of view, it was possible to see which factors are important among the various dimensions of service quality and which factors should be managed well in mobile banking setting. Second, by periodically finding customer complaints, service failures can be prevented early, and service quality and customer satisfaction can be improved.Practical implicationsFrom a practical point of view, mobile bank managers should pay more attention to the service quality dimensions of practicality and enjoyment. In addition, the results mean that the app design and aesthetics with the most negative reviews should be reviewed from the user's perspective rather than from the company's point of view. Second, it is possible for them to establish a systematic complaint management system that can prevent service failure in advance by detecting customer complaints early. Third, it is possible for them to make quick decisions regarding service quality with the help of real-time customer response through dashboard construction.Originality/valueThis paper is a pioneer study measuring service quality with sentiment analysis, one of the text mining applications, using customers' reviews of a mobile bank.
20 citations
TL;DR: The findings suggest that although digitalization requires retailers to accept the long-term investment challenges, it has a significant positive effect on the key of OCR strategy implementation, i.e. SCI.
Abstract: PurposeSupply chain integration (SCI) is key to implementing omni-channel retailing (OCR) strategy. In this paper, the authors explore the role of digitalization as a driver of SCI, as well the role of human capital (HC) in digitalization, using a knowledge management (KM) perspective.Design/methodology/approachAn empirical study was conducted using survey research. A sample of 188 omni-channel retailers in the Chinese market was analyzed using factor analysis and structured equation modeling (SEM) to examine the hypotheses presented in the conceptual model.FindingsThis study reveals that HC is positively related to the level of a firm's digitalization in OCR, and that digitalization is positively related to the retailer's SCI. Moreover, the authors found that employees' capital has a greater impact on digitalization than managers' capital, while digitalization has a stronger driving effect on internal and customer integration.Research limitations/implicationsThe findings suggest that although digitalization requires retailers to accept the long-term investment challenges, it has a significant positive effect on the key of OCR strategy implementation, i.e. SCI. The findings also provide evidence for the application of KM in OCR, as this theoretical lens enriches our understanding of the phenomena of SCI in OCR and provides explanation to our results by linking digitalization and HC.Originality/valueDigitalization is quantified and examined in OCR. Moreover, this study reveals the importance of HC on the implementation of digitalization and the different effects of digitalization on each dimension of SCI.
19 citations
TL;DR: The findings are that the emerging digital technology application and peer competition degrees have positive effects on empowerment capability, while the demand personalization degree negatively affects empowerment capability in the short (long) term.
Abstract: This study explores the influencing factors of smart logistics ecological chain's (SLEC's) organizational collaboration and designs a corresponding conceptual framework.,The multi-case study is applied to this paper. Specifically, this study is a combination of exploratory and explanatory case studies.,The findings are threefold. First, empowerment capability and the information-sharing level are unique factors that affect SLEC's organizational collaboration. Second, greater empowerment capability stimulates the increase of information-sharing level. Third, emerging digital technology, personalized demand and peer competition affect the degree of SLEC's organizational collaboration through an intermediary variable – empowerment capability. Specifically, the emerging digital technology application and peer competition degrees have positive effects on empowerment capability, while the demand personalization degree negatively (positively) affects empowerment capability in the short (long) term.,As an important part of supply chain performance, organizational collaboration is receiving more attention. However, in the smart economy context, no theoretical framework exists for analyzing factors that affect the organizational collaboration degree of SLEC. This study fills this gap.
TL;DR: Whether the IRT theory is indeed valid and whether IRT is culturally invariant from the Eastern and Western cultures is determined, and it is revealed that tradition is the strongest barrier followed by the value, risk, image and usage barrier.
Abstract: In the literature of industrial management, the focus is normally given on examining the factors that contribute to product innovation acceptance. The advocates of “pro-innovation bias” assume that consumers are open to new products and are willing to accept an innovative product. However, there is a high failure rate of technological innovations and most of the technological innovations were rejected due to users' resistance. Since the inception of innovation resistance theory (IRT), the number of studies that used IRT has gained much attention from scholars. However, the findings from these studies from various contexts are inconsistent, lack universality, and a clear understanding of technological innovation barriers. The study aims to determine whether the IRT theory is indeed valid and whether IRT is culturally invariant from the Eastern and Western cultures.,A meta-analysis based on a random-effects model and studies drawn from 24 countries and/or regions with a consolidated sample size of 10,463 was conducted. Cultural invariance was identified based on subgroup analysis. Moderator analysis was performed by applying the weighted linear regression.,The results reveal that tradition is the strongest barrier followed by the value, risk, image and usage barrier. Interestingly, there is a cultural invariance in IRT from the Eastern and Western cultures. Besides, there are significant moderating effects due to the temporal factor.,The study has contributed useful theoretical and managerial implications in advancing the product innovation literature.
TL;DR: A hybrid quantitative and qualitative approach combining data-driven analysis, fuzzy Delphi method (FDM), entropy weight method (EWM), and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is employed to address the uncertainty in the context of sustainable supply chain finance as discussed by the authors.
Abstract: Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving SSCF This study proposes a bibliometric data-driven analysis from the literature to illustrate a clear overall concept of SSCF that reveals hidden indicators for further improvement,A hybrid quantitative and qualitative approach combining data-driven analysis, fuzzy Delphi method (FDM), entropy weight method (EWM) and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is employed to address the uncertainty in the context,The results show that blockchain, cash flow shortage, reverse factoring, risk assessment and triple bottom line (TBL) play significant roles in SSCF A comparison of the challenges and gaps among different geographic regions is provided in both advanced local perspective and a global state-of-the-art assessment There are 35 countries/territories being categorized into five geographic regions Of the five regions, two, Latin America and the Caribbean and Africa, show the needs for more improvement, exclusively in collaboration strategies and financial crisis Exogenous impacts of wars, natural disasters and disease epidemics are implied as inevitable attributes for enhancing the sustainability,This study contributes to (1) boundary SSCF foundations by data driven, (2) identifying the critical SSCF indicators and providing the knowledge gaps and directions as references for further examination and (3) addressing the gaps and challenges in different geographic regions to provide advanced assessment from local viewpoint and to diagnose the comprehensive global state of the art of SSCF
TL;DR: The results show that consumer technological knowledge is positively and significantly related to EVs' perceived usefulness, perceived ease of use, perceived fun to use and consumers' intention to adopt EVs.
Abstract: Technological innovation is one of the remarkable characteristics of electric vehicles (EVs). This study aims to analyze how consumers' technological knowledge affects their intention to adopt EVs.,Original data were collected via a survey of 443 participants in China. An extended technology acceptance model was constructed to identify the factors influencing consumers' intention to adopt EVs and related technological knowledge pathways.,The results show that consumer technological knowledge is positively and significantly related to EVs' perceived usefulness, perceived ease of use, perceived fun to use and consumers' intention to adopt EVs. In addition, no direct and significant relationship is found between perceived fun to use and willingness to adopt EVs, from the technical knowledge dimension.,Imparting consumers with EV technological knowledge and usefulness may be an effective way to enhance their awareness and willingness to use EVs. Moreover, the role of females in the decision to adopt EVs should not be ignored, especially in decisions to purchase a family car.,Prior studies lack a technological knowledge-based view, and few studies have discussed how to explore the effects of consumer technological knowledge about EVs on their adoption intention. This study fills the research gap.
TL;DR: The findings of the study can assist service providers, industrial managers and government organisations in understanding the barriers and subsequently evaluating interrelationships and ranks of barriers in the successful adoption of BDA in a manufacturing organisation context.
Abstract: This study initially aims to identify the barriers to the big data analytics (BDA) initiative and further evaluates the barriers for knowing their interrelations and priority in improving the performance of manufacturing firms.,A total of 15 barriers to BDA adoption were identified through literature review and expert opinions. Data were collected from three types of industries: automotive, machine tools and electronics manufacturers in India. The grey-decision-making trial and evaluation laboratory (DEMATEL) method was employed to explore the cause–effect relationship amongst barriers. Further, the barrier's influences were outranked and cross-validated through analytic network process (ANP).,The results showed that “lack of data storage facility”, “lack of IT infrastructure”, “lack of organisational strategy” and “uncertain about benefits and long terms usage” were most common barriers to adopt BDA practices in all three industries.,The findings of the study can assist service providers, industrial managers and government organisations in understanding the barriers and subsequently evaluating interrelationships and ranks of barriers in the successful adoption of BDA in a manufacturing organisation context.,The paper is one of the initial efforts in evaluating the barriers to BDA in improving the performance of manufacturing firms in India.
TL;DR: In this paper, a hybrid decision-making approach is developed that utilizes (1) fuzzy preference programming (FPP) to decide the importance of one supplier attribute over another and (2) multi-objective optimization on the basis of ratio analysis (MOORA) to prioritize suppliers based on fuzzy performance rating.
Abstract: In the last decade, sustainable sourcing decision has gained tremendous attention due to the increasing governmental restrictions and public attentiveness. This decision involves diverse sets of classical and environmental parameters, which are originated from a complex, ambiguous and inconsistent decision-making environment. Arguably, supply chain management is fronting the next industrial revolution, which is named industry 4.0, due to the fast advance of digitalization. Considering the latter's rapid growth, current supplier selection models are, or it will, inefficient to assign the level of priority of each supplier among a set of suppliers, and therefore, more advanced models merging “recipes” of sustainability and industry 4.0 ingenuities are required. Yet, no research work found towards a digitalized, along with sustainability's target, sourcing.,A new framework for green and digitalized sourcing is developed. Thereafter, a hybrid decision-making approach is developed that utilizes (1) fuzzy preference programming (FPP) to decide the importance of one supplier attribute over another and (2) multi-objective optimization on the basis of ratio analysis (MOORA) to prioritize suppliers based on fuzzy performance rating. The proposed approach is implemented in consultation with the procurement department of a food processing company willing to develop a greener supply chain in the era of industry 4.0.,The proposed approach is capable to recognize the most important evaluation criteria, explain the ambiguity of experts' expressions and having better discrimination power to assess suppliers on operational efficiency and environmental and digitalization criteria, and henceforth enhances the quality of the sourcing process. Sensitivity analysis is performed to help managers for model approval. Moreover, this work presents the first attempt towards green and digitalized supplier selection. It paves the way towards further development in the modelling and optimization of sourcing in the era of industry 4.0.,Competitive supply chain management needs efficient purchasing and production activities since they represent its core, and this arises the necessity for a strategic adaptation and alignment with the requirement of industry 4.0. The latter implies alterations in the avenue firms operate and shape their activities and processes. In the context of supplier selection, this would involve the way supplier assessed and selected. This work is originally initiated based on a joint collaboration with a food company. A hybrid decision-making approach is proposed to evaluate and select suppliers considering operational efficiency, environmental criteria and digitalization initiatives towards digitalized and green supplier selection (DG-SS). To this end, supply chain management in the era of sustainability and digitalization are discussed.
TL;DR: This is the first paper to analyze blockchain performance in an industry setting and an integrated holistic performance assessment model incorporating the 4 criteria and 25 subcriteria is applied.
Abstract: Performance assessment of blockchain in the supply chain requires a systematic approach because of its interdisciplinary and multiobjective nature. Hence, four types of performance domains are identified, namely, environmental, economic, customer and information.,The following methodologies have been utilized: (1) literature review to find relevant factors, (2) factor analysis to validate factors and (3) DEMATEL theory to find the cause and effect relationships amongst performance measures.,An integrated holistic performance assessment model incorporating the 4 criteria and 25 subcriteria is applied.,This is the first paper to analyze blockchain performance in an industry setting.
TL;DR: In this paper, the authors present broad suggestions for IS researchers about how they can direct some of their research efforts to consider, conceptualize and incorporate time into research endeavors and how they might be mindful about considering and specifying time-related scope conditions of their efforts.
Abstract: As information systems (IS) phenomena continue to emerge and evolve in our ever-changing economic and social contexts, researchers need to increase their focus on time in order to enrich our theories. The purpose of this paper is to present broad suggestions for IS researchers about how they can direct some of their research efforts to consider, conceptualize and incorporate time into research endeavors and how they might be mindful about considering and specifying time-related scope conditions of their research efforts.,The authors synthesize empirical studies and discuss three distinct yet related frameworks of time and the benefits they can provide. The authors choose two research streams that reflect dynamic economic and social contexts – namely, enterprise systems and social networks – to illustrate how time and frameworks of time can be leveraged in our theory development and research design.,The authors demonstrate that limited research in IS has incorporated a rich conceptualization and/or discussion of time. The authors build on this gap to highlight guidelines that researchers can adopt to enrich their view of time.,Given the dynamic nature of IS phenomena and the increased availability of longitudinal data, the authors’ suggestions aim to urge and guide IS researchers about ways in which they can incorporate time into their theory and study designs.
TL;DR: In this article, a theoretical framework for conducting a costbenefit analysis of CSAT programs with different types of costs and benefits is developed to evaluate the impact of different costs on a company's optimal degree of security.
Abstract: Employees must receive proper cybersecurity training so that they can recognize the threats to their organizations and take the appropriate actions to reduce cyber risks However, many cybersecurity awareness training (CSAT) programs fall short due to their misaligned training focuses,To help organizations develop effective CSAT programs, we have developed a theoretical framework for conducting a cost–benefit analysis of those CSAT programs We differentiate them into three types of CSAT programs (constant, complementary and compensatory) by their costs and into four types of CSAT programs (negligible, consistent, increasing and diminishing) by their benefits Also, we investigate the impact of CSAT programs with different costs and the benefits on a company's optimal degree of security,Our findings indicate that the benefit of a CSAT program with different types of cost plays a disparate role in keeping, upgrading or lowering a company's existing security level Ideally, a CSAT program should spend more of its expenses on training employees to deal with the security threats at a lower security level and to reduce more losses at a higher security level,Our model serves as a benchmark that will help organizations allocate resources toward the development of successful CSAT programs
TL;DR: The results indicate that perceived ease of use, perceived self-efficacy and social connection have positive effects on the donation intentions of backers through a combination of extrinsic and intrinsic motivations.
Abstract: This study adopts self-determination theory and stimulus-organism-response framework to develop a model that explores the motivations of such donors by considering their self-determination needs and extrinsic and intrinsic motivations.,Based on online survey data collected from 436 crowdfunding donors in China, this study follows a structural equation modeling analysis to test hypotheses.,The results indicate that perceived ease of use, perceived self-efficacy and social connection have positive effects on the donation intentions of backers through a combination of extrinsic and intrinsic motivations.,The findings shed light on various extrinsic and intrinsic motivations advancing knowledge of individual fund motivation in donation-based crowdfunding and provide guidelines for the development of donation-based crowdfunding theory and practice.
TL;DR: In this article, the authors draw upon personality traits and knowledge characteristics theories to develop a theoretical model to empirically examine the effect of individual characteristics and knowledge characteristic on physicians' knowledge sharing behavior.
Abstract: Sharing knowledge of physicians in hospitals is critical and significant in terms of providing better healthcare services. Despite the significance of knowledge sharing in the healthcare setting, very few studies have empirically investigated knowledge sharing drivers among physicians. Particularly, the process of knowledge sharing through the interplay between individual characteristics, knowledge characteristics, and intention in a healthcare setting has received very little empirical support. In this study, the authors draw upon personality traits and knowledge characteristics theories to develop a theoretical model to empirically examine the effect of individual characteristics and knowledge characteristics on physicians' knowledge sharing behavior.,Based on a sample of 215 physicians from 20 hospitals in Jordan, the authors conducted data analysis using the partial least squares statistical technique.,The study revealed that the personality traits (Extraversion, Neuroticism, Agreeableness and Conscientiousness) significantly influence physician intention to share knowledge. Knowledge characteristic (Situatedness) was also found to affect the intention to share knowledge.,Very little is known about the effect of individual characteristics and knowledge characteristics on knowledge sharing behavior among physicians. The study contributes to the related literature by empirically investigating how individual characteristics and knowledge characteristics influence physicians' knowledge sharing behavior. The findings add to the understanding of the role of personality traits and knowledge characteristics in physicians' intention to share knowledge and give important insights for practice and theory.
TL;DR: Compared with previous studies, this study reveals for the first time the correlation between the SSCI performance and technology application, SSCi in structure, management and service, providing new ideas for relevant researches on S SCSI, and providing new theoretical support for managers' decision-making related to S SCCI.
Abstract: This study explores the influencing factors affecting smart supply chain innovation (SSCI) performance of commodity distribution enterprises, and proposes the corresponding framework from the perspective of the application of technology to improve the SSCI performance and make up the research gap in this field.,A multi-case study method is adopted in this study. Four distribution commodity distribution enterprises A, B, C and D in China are chosen as case enterprises. The interviews with senior management team members are used to collect data. The combination of open coding and axial coding are used to process the data. By testing the reliability and validity, the theoretical framework is summarized.,First, we find that the technology application cost inhibits SSCI and that the level of technology suitable for enterprise development will promote SSCI. Second, SSCI in structure, management and services can improve the performance and innovation ability of enterprises. Third, the quality of multi-channel integration and degree of customization around customer demand can significantly modify the above effects.,Compared with previous studies, this study reveals for the first time the correlation between the SSCI performance and technology application, SSCI in structure, management and service, providing new ideas for relevant researches on SSCI, and providing new theoretical support for managers' decision-making related to SSCI.
TL;DR: In this paper, a fuzzy-based approach with ABC classification is proposed to incorporate all the different variables in a multi-criteria configuration, which can optimize purchasing decision-making in the inventory management process.
Abstract: This project attempts to present a space component inventory classification system for space inventory replenishment and management. The authors propose to adopt a classification system that can incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment.,A fuzzy-based approach with ABC classification is proposed to incorporate all the different variables in a multi-criteria configuration. Fuzzy logic is applied as an effective way for formulating classification problems in space inventory replenishment of the soil preparation system (SOPSYS) which is used in grinding and sifting Phobos rocks to sub-millimeter size in the Phobos-Grunt space mission. An information system was developed using the existing platform and was used to support the key aspects in performing inventory classification and purchasing optimization.,The proposed classification system was found to be able to classify the inventory and optimize the purchasing decision efficiency. Based on the information provided from the system, implementation plans for the SOPSYS project and related space projects can be proposed.,The paper addresses one of the main difficulties in handling qualitative or quantitative classification criteria. The model can be implemented using mathematical calculation tools and integrated into the existing inventory management system. The proposed model has important implications in optimizing the purchasing decisions to shorten the research and development of other space instruments in space missions.,Inventory management in the manufacture of space instruments is one of the major problems due to the complexity of the manufacturing process and the large variety of items. The classification system can optimize purchasing decision-making in the inventory management process. It is also designed to be flexible and can be implemented for the manufacture of other space mission instruments.
TL;DR: The results reveal that supply chain agility (SCA) completely mediates the impacts of technical skills on product-oriented and service-oriented MCC and the impact of data-driven decision-making culture (DDC) on service- oriented MCC.
Abstract: This study aims to explore how to respond to market turbulence by big data analytics (BDA) capability and mass customization capability (MCC) from the perspective of organizational information processing theory (OIPT).,This study examines the research hypotheses using hierarchical regression analysis by collecting data from 277 Chinese firms.,The results reveal that supply chain agility (SCA) completely mediates the impacts of technical skills on product-oriented and service-oriented MCC and the impact of data-driven decision-making culture (DDC) on service-oriented MCC. SCA also partially mediates the impacts of managerial skills on two dimensions of MCC and the impact of DDC on product-oriented MCC. In addition, market turbulence strengthens the impact of managerial skills on SCA.,This study provides insightful contributions and implications for enhancing MCC to cope with market turbulence.
TL;DR: The results of this study stress the fact that managers of SMMEs need to look into the role-specific context of the firm before determining which practices would be effective for their companies, especially for SCQM in the Asian context.
Abstract: This research aims to examine the relationships between supply chain quality management (SCQM), organizational learning capability (OLC) and product innovation performance (PIP) among small and medium-sized manufacturing enterprises (SMMEs) in Malaysia.,This is a quantitative study in which 163 valid responses were empirically collected from SMMEs in Malaysia via self-administered structured questionnaires.,Performing a partial least squares–structural equation modelling analysis, the findings revealed that the relationships between SCQM, OLC and PIP are positive and significant. Moreover, OLC partially mediates the relationship between SCQM and product innovation. Serving as a practical guideline, the results of this study stress the fact that managers of SMMEs need to look into the role-specific context of the firm before determining which practices would be effective for their companies.,The value-added additional testing of the mediating effect of OLC is the highlight of this study. This research represents another leap towards redefining and advancing SCQM, especially for SMMEs in the Asian context.
TL;DR: The results show that speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.
Abstract: With the fierce competition in the cold chain logistics market, achieving and maintaining excellent customer satisfaction is the key to an enterprise's ability to stand out. This research aims to determine the factors that affect customer satisfaction in cold chain logistics, which helps cold chain logistics enterprises identify the main aspects of the problem. Further, the suggestions are provided for cold chain logistics enterprises to improve customer satisfaction.,This research uses the text mining approach, including topic modeling and sentiment analysis, to analyze the information implicit in customer-generated reviews. First, latent Dirichlet allocation (LDA) model is used to identify the topics that customers focus on. Furthermore, to explore the sentiment polarity of different topics, bi-directional long short-term memory (Bi-LSTM), a type of deep learning model, is adopted to quantify the sentiment score. Last, regression analysis is performed to identify the significant factors that affect positive, neutral and negative sentiment.,The results show that eight topics that customer focus are determined, namely, speed, price, cold chain transportation, package, quality, error handling, service staff and logistics information. Among them, speed, price, transportation and product quality significantly affect customer positive sentiment, and error handling and service staff are significant factors affecting customer neutral and negative sentiment, respectively.,The data of the customer-generated reviews in this research are in Chinese. In the future, multi-lingual research can be conducted to obtain more comprehensive insights.,Prior studies on customer satisfaction in cold chain logistics predominantly used questionnaire method, and the disadvantage of which is that interviewees may fill out the questionnaire arbitrarily, which leads to inaccurate data. For this reason, it is more scientific to discover customer satisfaction from real behavioral data. In response, customer-generated reviews that reflect true emotions are used as the data source for this research.
TL;DR: The results indicated that photographs on social media of three tourism destinations can be explored based on 11 categories of image themes and provided theoretical evidence of the importance ofimage themes in the context of social media engagement marketing.
Abstract: This study assessed the effect of photo themes to facilitate social media user engagement in Facebook brand pages and emphasized the important role of designing images for developing destination marketing strategies.,This study analyzed 8,900 posts that were published by official tourism destination marketers for each destination (Hong Kong, Japan, South Korea). Text mining analysis, image thematic coding analysis and two-way ANOVA were applied to examine the significant differences across proposed determinants for the following engagements: the numbers of likes, comments and shares.,The results indicated that photographs on social media of three tourism destinations can be explored based on 11 categories of image themes. The themes' significant and distinct effects on three indicators of social media engagement were verified.,This research presented methodological insights by integrating thematic and statistical analyses with social media analytics. The findings of this study provided theoretical evidence of the importance of image themes in the context of social media engagement marketing. Based on the implications of this study, practitioners would enhance the effectiveness of social media marketing.
TL;DR: In this article, a Stackelberg game model dominated by the platform is established and the equilibrium solutions under the two pricing models are derived, and authors combined the supply chain revenue-sharing contract with the car-sharing market to explore the application of the revenue sharing contract in the sharing economy.
Abstract: The car-sharing market has entered the mature stage, and consumers' demand shows a diversified increasing trend. This paper considers two modes of operation and two pricing strategies, which are business-to-consumer and consumer-to-consumer modes, market pricing and platform pricing. Under these conditions, the platform's revenue-sharing ratio will be different. The purpose of this paper is to explore this research question, and seeks an optimal pricing mechanism that can achieve a win–win situation between platform and automobile manufacturer in the two market modes.,The authors design different profit functions for platform under the two contexts. Of course, the platform's function is constrained to the manufacturer's function. By introducing a revenue-sharing contract a Stackelberg game model dominated by the platform is established and the equilibrium solutions under the two pricing models are derived.,The study found that even if only market pricing is executed, the scale of the car-sharing market will continue to expand. As the car-sharing market becomes more saturated, platform pricing is better for the automobile manufacturer; in most cases, the platform prefers platform pricing, but when the number of private cars is relatively small, if the cost of car operation and maintenance for the automobile manufacturer is lower or the revenue-sharing ratio of private cars is high, then market pricing will be more favorable to the platform.,With the cross-border integration of car service platforms and the automobile manufacturing industry, the key to achieving win–win cooperation and sustainable development in the car-sharing market will converge on the question of how to design a suitable pricing mechanism and revenue-sharing method.,Authors have determined how a car-sharing platform achieves a win–win order pricing strategy with the manufacturer and private car owners, respectively. And authors combined the supply chain revenue-sharing contract with the car-sharing market to explore the application of the revenue-sharing contract in the sharing economy.
TL;DR: This study aims to demonstrate a customer journey centred service design approach to receive the design requirements based on customers' needs and to use a systematic approach to generate solutions.
Abstract: A good customer experience means meeting the customer expectation. Thus, unexpected customer experience is usually a good point to initiate improvement or innovation for product or service design. Attempting to enhance the customer experience in the customer journey, this study aims to demonstrate a customer journey centred service design approach to receive the design requirements based on customers' needs and to use a systematic approach to generate solutions.,A holistic service design method named 3E model was proposed. It integrates customer experience journey map (CXJM), the theory of inventive problem solving (TRIZ) and service assembly and service replacement mechanism into three design stages. In stage 1, CXJM is enhanced with emotional range analysis to identify the customer pain points as well as customers' requirements (CRs) in exhibition, tourism and hotel sectors for initializing service design. Stage 2 investigates the specific design requirements (DRs) of the smart exhibition system and the contradictions. Then, the innovative principles were analyzed. In Stage 3, expected exhibition service system was designed.,The new service system which named the smart expo system based on information and communication technology (ICT) is proposed. It consists of “Tourism Link assists”, “i-Kaohsiung hotel service center”, “Smart AEC” and “O2O e-tickets”.,The proposed 3E model builds a systematic and coherent design method for the smart exhibition service area. It provides the linkage and action-oriented guidance from customer pain points, service parameters, innovative principles to solutions.
TL;DR: A smart system based on the concept of Industry 4.0 to prevent customer dissatisfaction and enables hoteliers to interact with customers by understanding what they like/dislike from their behaviors via data analysis is developed.
Abstract: This paper develops a smart system based on the concept of Industry 4.0 to prevent customer dissatisfaction. The value of this prevention system is that it enables hoteliers to interact with customers by understanding what they like/dislike from their behaviors via data analysis. Therefore, this system helps hoteliers to enhance service quality by predicting service issues.,The system, named the dissatisfaction identification system (DIS), is developed. A total of 127 service items were examined by a hotel manager who preset the threshold values for the measurement of service quality. A big data set for the questionnaire survey is statistically generated by a pseudorandom number generator and 10,000 mock data sets are taken as input for comparison.,The results indicated that 36 out of 127 service items are identified as service issues for the participating hotel. Examples include customer code number 01d, “Space of parking lot is adequate” in the safety management category, and number 05a, “A hotel's service time meets my needs” in the front office service category. The items identified require improvement action plans for preventing customer dissatisfaction.,This paper offers a new perspective paper emphasizing customer dissatisfaction using a big data-driven technology system. The DIS, prevention system, is developed to aid hotels by enhancing their relationships with customers using a data-driven approach.