With the popularity of sharing-based applications such as bicycle and car sharing, the sharing economy has attracted considerable global attention. The factors that affect users’ adoption of the sharing economy must be identified to facilitate the promotion of low-carbon lifestyles and help enterprises attract more active users. By employing the technology acceptance model (TAM) and herd behavior, this study implemented an expanded TAM and identified several factors affecting behavioral intention (BI) toward the sharing economy. A questionnaire was used to obtain the data, which were analyzed through structural equation modeling. The results revealed that perceived usefulness (PU) and perceived ease of use (PEOU) are the main factors affecting BI. Moreover, trust (TRU) was identified as a mediator of subjective norm (SN) and PEOU. Imitating others (IMI) affects BI, and SN affects TRU, PU, and PEOU. Gender moderates SN and IMI. This paper indicates that to improve users’ BI, enterprises should enhance PU, PEOU, and TRU; cooperate with organizations to enhance SN; and guide potential users to imitate others.

Identifying the key influencing factors that affected users’ adoption of sustainable mobile health care and improving alternative mobile health services continuously can help ordinary consumers (especially vulnerable populations) use extensive health resources cheaply and conveniently; which can increase the quality and equity of health services; so as to promote the sustainable development of medical resources. We integrated decision making trial and evaluation laboratory (DEMETEL)-based analytic network process (DANP) and a modified VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to form a DDANPMV (DEMATEL+ DANP + Modified VIKOR) model to examine consumers’ adoption of mobile health care. This method was developed to not only help decision-makers evaluate alternative mobile health care and determine the best option, but also use to identify and improve the performance gap of alternative products with the goal of satisfying the aspirations of consumers. Four types of mobile health care products were analyzed. The results indicate that social norms; product image; and consumer trust are three key factors in consumer adoption. The results can guide policy design and implementation. The government and enterprises should improve mobile health care through these three aspects.

As a new type of public health service product, online medical websites (OMWs) are becoming quite popular. OMWs can address patients’ basic medical problems remotely and give health guidance online. Compared to traditional hospitals, OMWs are more convenient and inexpensive, they can usually provide a better service for patients with poor medical conditions (especially in rural areas), and they also contribute to the rational distribution of medical resources. Therefore, key factors that affect patients’ acceptance of OMWs must be identified to contribute to public health. By integrating perceived risk (PR) and the technology acceptance model (TAM), we proposed a modified TAM and clarified how PR and other factors affect patients’ behavioral intention (BI) towards OMWs. A sample of 245 research participants in China took part in this study and the structural equation model (SEM) was used to test our hypotheses. The results revealed that perceived usefulness (PU) is a positive predictor of BI but has no significant effect on attitude (ATT), while perceived ease of use (PEOU) can affect BI through PU and attitude (ATT). Moreover, trust (TRU) was identified as a mediator of PR and PU/PEOU. Also, the doctor–patient relationship (DPR) was shown to moderate PR and TRU. In order to increase patients’ BI, OMW providers need further innovations to improve patients’ TRU and reduce their PR.

Artificial intelligence (AI) is a cutting-edge technology that has been widely applied in tourism operations. To enhance tourists' experience, many tourism suppliers introduced AI devices to interact with tourists. Previous studies classified AI devices as task- and social- oriented based on their functions; however, current models that explain customers' intention to use AI devices did not reflect the discrepancy between the two different types. Therefore, this paper attempts to fill this gap by proposing a theoretical model for the use of task-oriented AI devices. Based on the multi-stage appraisal framework and the Structural Equation Modeling analysis, this paper presents the following findings: (1) utilitarian motivation, interaction convenience, and task-technology fit are the factors appraised in the first stage; (2) perceived competence and flow experience are the factors appraised in the second stage; (3) utilitarian motivation, interaction convenience, and task-technology fit are positively associated with perceived competence. (4) Perceived competence positively influences flow experience, which further affects customers' switching intention from task-oriented AI devices to human service; (5) the serial mediating effect of perceived competence and flow experience between the stimulus mentioned in the first appraisal stage and the switching intention is confirmed. This study reveals the underlying psychological mechanism when customers use task-oriented AI devices, and it provides a theoretical framework for task-oriented AI device adoption.

Purpose: Although phone calls are widely used by debt collection services to persuade delinquent customers to repay, few financial services studies have analyzed the unstructured voice and text data to investigate how debt collection call strategies drive customers to repay. Moreover, extant research opens the “black box” mainly through psychological theories without hard behavioral data of customers. The purpose of our study is to address this research gap.Design/methodology/approach: The authors randomly sampled 3,204 debt collection calls from a large consumer finance company in East Asia. To rule out alternative explanations for the findings, such as consumers' previous experience of being persuaded by debt collectors or repeated calls, the authors selected calls made to delinquent customers who had not been delinquent before and were being called by the company for the first time. The authors transformed the unstructured voice and textual data into structured data through automatic speech recognition (ASR), voice mining, natural language processing (NLP) and machine learning analyses.Findings: The findings revealed that (1) both moral appeal (carrot) and social warning (stick) strategies decrease repayment time because they arouse mainly happy emotion and fear emotion, respectively; (2) the legal warning (stick) strategy backfires because of decreasing the happy emotion and triggering the anger emotion, which impedes customers' compliance; and (3) in contrast to traditional wisdom, the combination of carrot and stick fails to decrease the repayment time.Originality/value: The findings provide a valuable and systematic understanding of the effect of carrot strategies, stick strategies and the combinations of them on repayment time. This study is among the first to empirically analyze the effectiveness of carrot strategies, stick strategies and their joint strategies on repayment time through unstructured vocal and textual data analysis. What's more, the previous studies open the “black box” through psychological mechanism. The authors firstly elucidate a behavioral mechanism for why consumers behave differently under varying debt collection strategies by utilizing ASR, NLP and vocal emotion analyses.