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The purpose of this paper is to investigate switching intention from traditional enterprise information systems (EISs) to private cloud EIS in large enterprises. The authors propose that the factors motivate and inhibit enterprises’ switching intention to private EIS by integrating technology–organization–environment (TOE) framework and two-factor theory.

A research model draws from TOE framework and two-factor theory. Data were collected from 227 top managers and owners of the enterprises in China and used to analyze 11 hypotheses.

The results show that the technological context (compatibility), organizational context (financial support) and environmental context (vendor support and industry pressure) significantly influence switching benefits while data security and costs significantly influence switching costs. Switching benefits and switching costs significantly influence switching intention.

Past studies have focused mainly on the adoption of cloud computing. However, few studies have addressed the switching issues, especially in large enterprises. The findings are useful to understand switching issues from traditional EIS to private cloud EIS for both researchers and practitioners.

Considering both online and offline service scenarios, this study aims to explore the factors affecting doctors' intention to offer consulting services in eHealth and compare the factors between the free- and paid-service doctors. The theory of reasoned action and social exchange theory are integrated to develop the research model that conceptualizes the role of extrinsic motivations, intrinsic motivations, costs, and attitudes in doctors' behavioral intentions.

Partial least square structural equation modeling (PLS-SEM) was leveraged to analyze 326 valid sample data. To provide robust results, three non-parametric multigroup analysis (MGA) methods, including the PLS-MGA, confidence set, and permutation test approaches, were applied to detect the potential heterogeneity between the free- and paid-service doctors.

The results with overall samples reveal that anticipated rewards, anticipated associations, anticipated contribution, and perceived fee are all positively related to attitude, which in turn positively influences behavioral intention, and that perceived fee positively moderates the relationship between attitude and behavioral intention. Attitude's full mediation is also confirmed. However, results vary between the two groups of doctors. The three MGA approaches return relatively convergent results, indicating that the effects of anticipated associations and perceived fee on attitude are significantly larger for the paid-service doctors, while that of anticipated rewards is found to be significantly larger for the free-service doctors.

eHealth, as a potential contactless alternative to face-to-face diagnoses, has recently attracted widespread attention, especially during the continued spread of COVID-19. Most existing studies have neglected the underlying heterogeneity between free- and paid-service doctors regarding their motivations to engage in online healthcare activities. This study advances the understanding of doctors' participation in eHealth by emphasizing their motivations derived from both online and offline service scenarios and comparing the differences between free- and paid-service doctors. Besides, horizontally comparing the results by applying diverse MGA approaches enriches empirical evidence for the selection of MGA approaches in PLS-SEM.

The purpose of this paper is to investigate factors that influence the patients’ intentions to visit doctors face-to-face for consultations from the perspective of online doctor–patient interaction. Justice theory, SERVQUAL and the halo effect are integrated to develop a research model based on the performance-evaluation-outcome framework. The authors hypothesize that perceived justice and service quality are the significant factors in reflecting the performance of online doctor–patient interaction, which influences patient satisfaction evaluation and online and offline behavioral intentions.

The study conducted an online survey to collect data. Patients on a healthcare consulting website were invited to participate in the survey. The research model and hypotheses were tested with 254 collected data from patients and analyzed using the partial least squares method.

The results show that perceived justice and service quality have a positive effect on patient satisfaction, and satisfaction and the intention of online consultation have a positive effect on the intention of face-to-face consultation.

This study offers suggestions on how doctors interact with patients and build their brand image. The findings also offer effective insights into improving doctors’ online services to retain patients and even encourage patients to go to clinics.

Online health consultation is one of the most popular online health services and is growing quickly. After patients consult online doctors, they are able to visit their doctors in person for further diagnosis and treatment if they have the need. This study investigates how patients’ online interactive experience influences their offline behavioral intentions, which are different from most of the past literature on eHealth.

This study uses the travel industry as the research context and investigates online–offline integration between the different business entities of hoteliers and online travel agencies (OTAs). The purpose of this paper is threefold: first, to examine cooperation between hotels and OTAs; second, to examine how online and offline satisfaction increase behavioral intentions toward online and offline channels; and third, to investigate the factors that increase online and offline satisfaction.

To investigate customers’ behavioral intentions toward online and offline channels, the authors collected 241 data points from ten hotels from four well-known chains. The partial least squares structural equation modeling approach was used to test the research model and the 13 hypotheses.

The results show that system quality, information quality and service quality of OTA websites increase online satisfaction. Online satisfaction further increases behavioral intentions toward online channels but has no significant effect on offline satisfaction and behavioral intentions toward offline channels. Emotional value and social value offered by hotels increase offline satisfaction. Offline satisfaction further increases behavioral intentions toward online and offline channels. Finally, behavioral intentions toward online channels indeed reinforce behavioral intentions toward offline channels.

Some prior studies have focused on the effects of offline channels on online channels, whereas others have examined the influences of online channels on offline channels. However, in previous studies, the online and offline channels were both owned by the same business entities. To the best of the authors’ knowledge, this study is the first to examine how online and offline channels belonging to different business entities can work together to increase customer intentions.

Recently, smart retail technology has emerged as an innovative technology that can improve consumer motivation and behavior in smart stores. Although prior studies have investigated factors influencing the adoption of smart retail technology, to the authors’ knowledge, no previous work has investigated the determinants of purchase intentions. The ultimate goal for retailers should be shopping, not technology adoption. However, traditional brick-and-mortar stores and theories focus on investing in utilitarian factors to attract customers. This study proposes that hedonic motivation should also play an important role, as new technologies may arouse customer curiosity and increase pleasant experiences. Therefore, the purpose of this study is to explore utilitarian and hedonic motivations that promote customers' purchase; intentions in smart stores. Specifically, the authors address the research questions: (1) What are the constituents of utilitarian motivation? (2) What are the constituents of hedonic motivation? (3) What are the factors that influence customers' purchase intentions? By answering the questions, the findings help retailers understand how to motivate customers to make purchases in smart stores.

To investigate consumer motivation and purchase intentions, the customers who made purchases in smart stores were invited to participate in the questionnaire survey. This study collected 307 data in smart retail settings. Partial least squares (PLS) software was used to assess the reliability, validity and the paths and significance of all hypotheses.

The results show that perceived ease of use directly and indirectly influences purchase intentions through utilitarian and hedonic motivations. Utilitarian motivation is a formative second-order construct comprised of merchandise price, merchandise quality, location convenience, speed of shopping and product recommendation. Hedonic motivation is a reflective second-order construct composed of control, curiosity, joy, focused immersion and temporal dissociation. The findings provide insights into the successful implementation of smart retail technology and offer retailers to better understand consumer motivation and purchase intentions in smart stores.

This study is the first to examine how consumer motivation influences purchase intentions in smart stores. This study posits and verifies the extended hedonic system acceptance model (HSAM) to explain consumer motivation for shopping in smart retail settings. This study also models the original first-order utilitarian and hedonic constructs as second-order formative and reflective constructs, respectively. Utilitarian motivation regarding functional benefits is developed based on the 5Ps of marketing and situational factors, while hedonic motivation regarding pleasant experiences is proposed based on cognitive absorption.

The purpose of this study is to investigate multichannel integration of hotels and online travel agencies (OTAs) and to compare consumer behavior between China and Indonesia in the context of online to offline (O2O) commerce. We examine how the services, brand and market share of OTAs influence behavioral intentions in both online and offline channels. SERVQUAL, theory of reasoned action and the halo effect are integrated to develop the research model.

To investigate Chinese and Indonesian customers' experiences and behavioral intention of OTAs and hotels, the customers who booked hotels using OTAs were invited to participate in the questionnaire survey. This study collected 336 and 305 data from China and Indonesia, respectively. A partial least squares structural equation modeling technique was used to test and compare the research hypotheses and model between China and Indonesia.

The results compare the similarities and differences of cross-country customer experiences and behavioral intentions of OTAs and hotels. The effect of website service quality on online satisfaction, the effect of online satisfaction on offline confirmation and the effects of offline confirmation and booking intention on patronage intention are significant and positive in both countries. Website service quality is positively associated with booking intention for Indonesia but not for China. The relationship between perceived size and booking intention is significant for China but not for Indonesia. The findings provide insights into the development of O2O commerce for global markets and multichannel strategies between OTAs and hotels.

With the development of O2O commerce, increasingly more hoteliers are opening up online and offline sales channels by cooperating with OTAs. Although several cross-country studies have investigated consumer behavior or behavioral intentions, behavioral settings are based on online or offline channels rather than the integration of both channels. Although some research has studied the integration or competition of OTAs and hotels, none of these studies have investigated the issues from the perspective of country comparison. This study provides the understanding of how customers in different countries and with different backgrounds react to the same e-commerce development, especially the cooperation of OTA and hotels.

To retain consumer attention and increase purchasing rates, many e-commerce vendors have adopted content-based recommender systems. However, apart from text-based documents, there is little theoretical background guiding element selection, resulting in a limited content analysis problem. Another inherent problem is overspecialization. The purpose of this paper is to establish a value-based recommendation methodology for identifying favorable attributes, benefits, and values on the basis of means-end chain theory. The identified elements and the relationships between them were utilized to construct a recommender system without incurring either problem.

This study adopted soft laddering and content analysis to collect popular elements. The relationships between the elements were established by using a hard laddering online questionnaire. The elements and the relationships were utilized to build a hierarchical value map (HVM). A mathematical model was then devised on the basis of the HVM to predict user preferences of attributes.

The results of a performance comparison showed that the proposed method outperformed the content-based attribute recommendation method and a hybrid method by 39 and 68 percent, respectively.

Although hybrid methods have been proposed to resolve the problem of overspecialization in content-based recommender systems, such methods have incurred “cold start” and “sparsity” problems. The proposed method can provide recommendations without causing these problems while outperforming the content-based and hybrid approaches.

The purpose of this paper is to investigate how customer power of environmental factors affects customer support (CS) engineers’ personal motivations in a knowledge-sharing context. The authors examine extrinsic (i.e. organizational rewards, reputation, and reciprocity) and intrinsic motivations (i.e. knowledge self-efficacy) affecting knowledge-sharing intentions based on the social exchange theory (SET) and self-efficacy theory. Furthermore, the authors introduce the concept of the social power theory to investigate the moderating effect of customer power on the relationships between personal motivations and knowledge-sharing intentions.

This study collects 349 questionnaires of CS engineers from 16 countries, including the USA, China, Japan, South Korea, and Taiwan. After the data collection, the research model and hypotheses are tested using partial least squares.

The empirical results show that reputation, reciprocity, and knowledge self-efficacy are significantly and positively related to knowledge-sharing intentions. Also, the results show that customer power can significantly moderate the relationships between personal motivations and knowledge-sharing intentions.

The findings help multinational corporations employ the perception of customer power to motivate CS engineers to share knowledge. Especially, the results can help organizations increase customer added value through effective knowledge sharing.

The research model integrates personal motivations derived from the SET and self-efficacy theory and customer power of environmental factors. Additionally, this study is the first to investigate the moderating effect of customer power on employees’ personal motivations and behavioral intentions.

The co‐word analysis method is commonly used to cluster‐related keywords into the same keyword domain. In other words, traditional co‐word analysis cannot cluster the same keywords into more than one keyword domain, and disregards the multi‐domain property of keywords. The purpose of this paper is to propose an innovative keyword co‐citation approach called “Complete Keyword Pair (CKP) method”, which groups complete keyword sets of reference papers into clusters, and thus finds keywords belonging to more than one keyword domain, namely bridge‐keywords.

The approach regards complete author keywords of a paper as a complete keyword set to compute the relations among keywords. Any two complete keyword sets whose corresponding papers are co‐referenced by the same paper are recorded as a CKP. A clustering method is performed with the correlation matrix computed from the frequency counts of the CKPs, for clustering the complete keyword sets. Since keywords may be involved in more than one complete keyword set, the same keywords may end up appearing in different clusters.

Results of this study show that the CKP method can discover bridge‐keywords with average precision of 80 per cent in the Journal of the Association for Computing Machinery citation bank during 2000‐2006 when compared against the benchmark of Association for Computing Machinery Computing Classification System.

Traditional co‐word analysis focuses on co‐occurrence of keywords, and therefore, cannot cluster the same keywords into more than one keyword domain. The CKP approach considers complete author keyword sets of reference papers to discover bridge‐keywords. Therefore, the keyword recommendation system based on CKP can recommend keywords across multiple keyword domains via the bridge‐keywords.

The purpose of this paper is to contribute to the development of measures to assess the ERP adoption of small and medium‐sized enterprises.

The paper follows Churchill's guideline for developing measures that have desirable reliability and validity. The pilot data are used to develop a proper measurement. The survey data, based on the 126 valid responses of 328 companies, are analysed by structural equation modelling (SEM) statistical methods.

The paper finds that the dimensions affecting ERP adoption show that characteristics of the CEO and perceived benefits possess positive effects on ERP adoption, while cost and technology have negative effects on ERP adoption. However, only “perceived benefits” is a significant dimension. It is surprising that the cost of the ERP system does not significantly affect ERP adoption.

The paper shows that the sample size should be taken into consideration when generalising the findings, and extended data and measures are required for further in‐depth investigation in specific areas.

The paper points out that the managers of SMEs with limited resources can find many ways to get more resources from governments. Government managers should be more realistically set the goal of helping firms in a healthy condition to adopt e‐business instead of setting the goal of improving the e‐business readiness of all SMEs. To help all CEOs of SMEs to realise the potential benefits, governments can work with academic research groups to set up forums and workshops to broadcast knowledge.

The paper develops measurements to assess the ERP adoption of small and medium‐sized enterprises. The results offer practical help for government managers to better understand ERP adoption with institutional help in Taiwan. Meanwhile, researchers interested in IT/IS can use the information provided here to guide their future enquiries.