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Though Blockchain has been studied in numerous contexts, the understanding of the impacts of Blockchain in achieving competitive advantages remains unexplored. Many industries, organizations and firms are still in a “wait and see” mode. This study aims at examining the effects of the technological, organizational and environmental factors drawn from the TOE framework in generating competitive advantage.

A dual-staged deep learning structural equation modeling artificial neural network analysis was conducted on 211 samples of small and medium enterprises. Four neural network models were engaged to rank the normalized importance of each of the predictor variables.

The research model can expound 57.99 and 47.33% of the variance in Blockchain adoption and competitive advantage correspondingly. The study successfully identified nonlinear relationships. The theoretical and managerial contributions are useful to scholars and practitioners such as industrial players, investors, chief executive officers (CEOs), managers, decision-makers and other stakeholders that intend to use Blockchain technology.

Unlike the existing technological–organizational–environmental (TOE) framework that uses a linear model and theoretically assumes that all relationships are linear, this has been the first study, which has successfully validated that there exist nonlinear relationships in the TOE framework. Further, very little has been theorized on the impacts of Blockchain adoption on competitive advantage, especially in the context of SMEs. Therefore, this study is the first one to provide the necessary theoretical foundation that may further extend the current knowledge of Blockchain technology adoption and its impacts.

Mobile payment (m-payment) has existed for over 25 years; however, there is no standard framework to guide scholars and practitioners in advancing m-payment research in the digital accelerated era. With this, this study aims to develop an m-payment framework and to analyse the evolution of m-payment research from 1997 to 2021. These were achieved by identifying the motor themes, basic and transversal themes, the emerging/declining themes and the highly developed but isolated themes. In addition, the publication trends of m-payment research were also identified.

A total of 1,999 articles of m-payment were identified from the Scopus database and 8,125 keywords were grouped into 483 word groups for the co-word analysis. Thematic networks were generated based on density and centrality. Performance analysis was performed on the themes of the four quadrants in the strategic diagrams. The m-payment framework was then developed based on the evolution map and a practical guide was built from the framework.

The m-payment framework consists of four dimensions, namely behaviour, technology, risk, and context. Some themes were declining while at the same time, m-payment remains strongly relevant and can stand the test of time. Several new themes have emerged in the 2013–2021 sub-periods.

This study is limited in that the study only investigates from the context of m-payment.

This study has provided a research framework (i.e. the m-payment framework) with a practical guide for scholars and practitioners to refer to, thus enabling them to embark on future studies without any hassle. The m-payment framework was developed based on empirical data using science mapping analysis of 1,999 articles.

The proposed m-payment research framework contributes towards more understanding of m-payment ten, usage amongst consumers and thus will help to reduce the rate of COVID-19 infection because m-payment is a contactless payment. Hence, the study may help in creating a safer and healthier society amid the unprecedented pandemic.

This study developed the m-payment framework based on empirical co-word analysis. A practical guide to extending the framework is also provided to guide the scholars and practitioners. Moreover, the evolution map for m-payment was created to understand the evolution of m-payment research. Practitioners may conduct market studies to unveil the factors that contribute to consumers' behaviours in m-payment in order to gain more understanding of the reasons behind their behaviours.

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.

This study looks to understand the opposing forces that would influence continuance intention. This is significant as users will take into account the positive and negative use experiences in determining their continuance intention. Therefore, this study looks to highlight the opposing forces of users’ continuance intention by proposing the Expectation-Confirmation-Resistance Model (ECRM).

Through an online survey, 411 responses were obtained from mobile payment users. Subsequently, a hybrid approach comprised of the Partial Least Squares-Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) was utilized to analyze the data.

The results revealed that all hypotheses proposed in the ECRM are supported. More precisely, the facilitating and inhibiting variables were found to significantly affect continuance intention. In addition, the ECRM was revealed to possess superior explanatory power over the original model in predicting continuance intention.

This study successfully developed and validated the ECRM which captures both facilitators and inhibitors of continuance intention. Besides, the relevance and significance of users’ innovative resistance to continuance intention have been highlighted. Following this, effective business and research strategies can be developed by taking into account the opposing forces that affect users’ continuance intention.

Tourism review (TR) celebrates its 75th anniversary in 2020. The purpose of this paper is to proffer a holistic overview of TR based on bibliometric analyses of the publications from 2001 to 2019.

The research method entails performance analyses and science mapping analyses on TR. The performance analyses engage a sequence of bibliometric statistics, including citation analysis, most cited authors and papers, most influential and productive authors, countries and institutions to name a few. The authors also used visualization of similarities viewer to perform the science mapping analysis of TR based on co-citations of cited authors, bibliographic couplings of authors and countries and co-occurrences of authors’ keywords from 2001 to 2019 and from 2014 to 2019. To examine the thematic evolution using SciMAT, a de-duplicating process was conducted in which 1,485 keywords were refined to 128 word groups before thematic evolution map and strategic diagrams for the three sub-periods were generated.

The thematic evolution map revealed ten thematic areas. The key themes of each of these thematic areas are destination studies, tourism destination and hospitality tourism; destination studies, competitiveness and innovations, co-operations and experience tourism; business studies, sports tourism, tourism destination and satisfaction; quality studies, networks, social studies and co-operation; business model and sports tourism; tourism management and tourism destination; political studies, perception and satisfaction; political studies, sustainability studies, social studies and health tourism; behavior, perception and satisfaction; and cultural tourism and tourism destination.

The study has managed to unveil the key trends of publications, authors, affiliations, nations and authors’ keywords. The findings are useful for potential authors to have a quick snapshot of what is expected from and what is happening in TR.

The study serves as a historical record of TR’s publications. It presents comprehensive bibliometric analyses of the publications in TR and identifying the key research trends.

Tourism Review（TR）将于2020年庆祝其成立75周年。本论文研究的目的是基于2001年至2019年出版的相关文献数量加以分析, 并对TR进行全面概述。

研究方法需要对TR进行性能分析和科学映射分析。绩效分析涉及一系列的文献数量统计, 包括引用分析：引用最多的作者和论文, 最具影响力和生产力的作者, 国家和机构。作者们还使用VOSviewer对引用作者的共同引用, 作者与国家/地区的书目耦合以及2001年至2019年以及2014年至2019年共同出现的作者关键词来进行TR的科学制图分析, 并使用SciMAT, 进行了重复数据删除过程, 将1485个关键字简化为128个词组, 进行随后三个阶段主题性的进化图与策略图的制作。

从2001年到2019年, TR的出版物和其引用率出现了巨大的飞跃, 因为它引起了全世界各项研究人员, 机构和国家的极大兴趣。被引用最多的文献和最有影响力的作者多来自发达国家的机构, 包括美国（US）, 英国（UK）以及其他欧洲和大洋洲国家。但是, 亚洲和非洲大陆的国家也正在加入潮流, 并开始在TR中树立影响力。

该研究设法揭示了出版物, 作者, 隶属关系, 国家和作者的关键词的主要趋势。这些发现对于有潜能的作者快速了解TR预期和正在发生的事情很有帮助。

这项研究可作为TR出版物的历史记录。它将提供TR出版物的综合文献计量分析, 并确定了关键的研究趋势。

旅游期刊, 文献计量分析, 科学制图, 书目耦合, 共引, 共现, VOSviewer, SciMAT

研究论文

Tourism Review (TR) celebra su 75 aniversario en 2020. El objetivo de esta investigación es ofrecer una visión global de TR basada en análisis bibliométricos de las publicaciones de 2001 a 2019.

el método de investigación implica análisis de rendimiento y análisis de mapeo científico en TR. Los análisis de desempeño involucran una secuencia de estadísticas bibliométricas, que incluyen análisis de citas, autores y artículos más citados, autores más influyentes y productivos, países e instituciones, por nombrar algunos. Los autores también utilizaron el VOSviewer para realizar el análisis de mapeo científico de TR basado en citas compartidas de autores citados, acoplamientos bibliográficos de autores y países y co-ocurrencias de las palabras clave de los autores desde 2001 hasta 2019 y desde 2014 hasta 2019. Para examinar el evolución temática utilizando SciMAT, se llevó a cabo un proceso de desduplicación en el que se refinaron 1485 palabras clave a 128 grupos de palabras antes de que se generaran mapas de evolución temáticos y diagramas estratégicos para los tres subperíodos.

hay un salto gigantesco en las publicaciones, así como las citas de TR de 2001 a 2019, ya que ha ganado mucho interés por parte de varios investigadores, instituciones y países de todo el mundo. La mayoría de los autores más citados e influyentes provienen de instituciones de países desarrollados, incluidos los Estados Unidos (EE. UU.), El Reino Unido (Reino Unido) y otras naciones europeas y de Oceanía. Sin embargo, países de los continentes asiático y africano se están uniendo al carro y comenzaron a establecer su influencia en TR.

el estudio ha logrado revelar las tendencias clave de publicaciones, autores, afiliaciones, naciones y palabras clave de los autores. Los hallazgos son útiles para que los autores potenciales tengan una instantánea rápida de lo que se espera de lo que está sucediendo en TR.

el estudio sirve como un registro histórico de las publicaciones de TR. Presenta análisis bibliométricos exhaustivos de las publicaciones en TR e identifica las tendencias de investigación clave.

The purpose of this paper is to examine the effects of the Big Five Model (BFM), the urge to purchase (UP) and urgency (UR) on impulse purchase (IP) in Facebook commerce (F-commerce), with the F-commerce purchase as control variable. It also investigates the influence of BFM and UR on UP and the effects of BFM on UR.

The survey instrument was rigorously validated via content validity index by expert panel, Q-sort procedure for construct validity by practitioners in pre-test, followed by evaluation of construct reliability in the pilot test. Data gathered from 808 usable questionnaires were analyzed using SmartPLS 3.

The study showed that BFM, UP, UR and F-commerce purchase are significant predictors of the F-commerce IP. UP is influenced by BFM and UR. BFM has a significant positive relationship with UR. F-commerce experience has insignificant moderating effect.

This study provides some useful practical implications for the F-commerce administrators, advertisers, dealers and promoters.

Existing studies focus on the antecedents of IP in conventional stores and online businesses; however, IP in F-commerce has been largely overlooked. The study investigates the impacts of personality traits on IP and its effects on UR and UP. The mediating effects of UR and UP were also examined. The study is able to predict 64.4, 68.0 and 49.0 percent variance in IP, UP and UR, respectively.

Under the pressure of dynamic business environments, firms in the banking and finance industry are gradually embracing Fintech, such as robo-advisors, as part of their digital transformation process. While robo-advisory services are expected to witness lucrative growth, challenges persist in the current landscape where most consumers are unready to adopt and even resist the new service. The study aims to investigate resistance to robo-advisors through the privacy and justice perspective. The human-like attributes are modeled as the antecedents to perceived justice, followed by the subsequent outcomes of privacy concerns, perceived intrusiveness and resistance.

An online survey was conducted to gather consumer responses about their perceptions of robo-advisors. Two hundred valid questionnaires were collected and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).

The results revealed that (1) perceived anthropomorphism and perceived autonomy are the positive determinants of perceived justice, (2) perceived justice negatively impacts privacy concerns and perceived intrusiveness and (3) privacy concerns and perceived intrusiveness positively influence resistance to robo-advisors.

The present study contributes to robo-advisory service research by applying a privacy and justice perspective to explain consumer resistance to robo-advisors, thereby complementing past studies that focused on the technology acceptance paradigm. The study also offers practical implications for mitigating resistance to robo-advisors.

The paper intends to examine mobile taxi (m-taxi) adoption, so as to close the gap in the current literature and clarify the behaviour of users by synthesising technological factors that are related to the characteristics of m-taxi applications with non-technological factors that are associated with the psychological characteristics of customers. The moderating effects of gender were also tested.

A self-administered questionnaire was adopted for data collection with 415 useable responses. The theoretical lens was tested via partial least squares-structural equation modelling. Additionally, state-of-the-art techniques such as permutation and multigroup analysis were applied.

Results indicate that social pressure, technology anxiety, effort expectancy, performance expectancy, and service and system quality are not significantly related to behavioural intention. Also, findings show no significant differences among gender in this study, which suggests that the model is invariant across gender groups.

This study provides a novel insight by taking a broader perspective of ride-hailing terminology by considering both taxis and private vehicles.

This paper purports to analyze the relationship between knowledge management (KM) and technological innovation in the Malaysian manufacturing sector. Furthermore, the interrelationships between the KM dimensions will also be investigated.

Survey data from 162 manufacturing firms were obtained. Multiple linear regression and neural network analysis were performed in this study to examine the relationships between KM and technological innovation; as well as the interrelationships between KM practices themselves.

This research study provides empirical evidence and confirms the results of past researchers that KM practices (i.e. knowledge sharing, knowledge application and knowledge storage) are positively and significantly related to technological innovation (i.e. product and process innovation). Moreover, it is also discovered that the interrelationships between the KM dimensions are positive and significant.

This research implies that when firms understand the significance of KM dimensions, there is a great possibility that the firm will succeed in harvesting the full value of technological innovation. Meanwhile, the result may also serve as a guide to the manufacturing managers to understand the importance of KM in their managerial actions, so that the level of technological innovation in their firms can be greatly enhanced.

Extensive research has not been done in the past to examine the relationship between the KM and technological innovation. Meanwhile, the investigation on the interrelationships between the KM dimensions has also been scarce. Hence, such a research is to be called for. This paper provides the manufacturing practitioners with useful guidelines on the appropriate KM practices to be implemented to ensure the improvement in technological innovation.