Search results

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.

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 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.

The purpose of this study is to investigate the relationship between social media and sensing capability for supply chain management (SCM) from an environmental scanning perspective. The authors consider upstream supply and downstream customer markets as two aspects of social media-enabled environmental scanning (SMES). The moderating effects of three uncertainties are explored.

The data were collected from 178 supply chain professionals through a survey. Generalized estimating equations (GEE) were used to analyze the data.

SMES in both supply and customer markets enhance sensing capability. Interestingly, the results reveal an accelerating effect on sensing by the incremental effort of SMES-supply. However, that of SMES-customer leads to a decelerating outcome for sensing. Also, uncertainties, especially the demand- and technology-related, play a series of interacting effects according to SMES levels.

This research contributes to the literature of operations and supply chains regarding social media strategies and dynamic capabilities. It opens the black box of environmental scanning behavior on social media and adds new knowledge on the dynamic influence of such behavior toward organizational sensing capability for SCM. In addition, further understanding on supply chain uncertainty as a moderator is also strengthened through this research.

This research is the first to empirically uncover the effect of social media on sensing capability for SCM through the lens of environmental scanning. The results support the employment of social networking for improving supply and demand sensing.

The global business environment combined with increasing societal expectations of sustainable business practices challenges firms with a host of emerging risk factors. As such, firms seek to increase supply chain transparency, enabling them to monitor operational activities and manage supply chain risks. Drawing on organizational information processing theory, the purpose of this paper is to examine how supply chain analytics (SCA) capabilities support operational supply chain transparency.

Using data from 477 survey participants, hypotheses are tested using seemingly unrelated regression.

The results reveal that: analytics capability in support of planning functions indirectly affects organizational supply chain transparency (OSCT) via SCA capabilities in source, make, and deliver functions; SCA capabilities in source, make, and deliver positively influence OSCT; and supply uncertainty moderates the relationship between SCA capabilities in make and OSCT.

This research suffers from limitations inherent in all survey-based research. Nonetheless, the authors found convincing evidence that suggests firms can employ SCA capabilities to meet transparency requirements.

The findings inform design of SCA systems, noting the importance of linking planning tools with tools that support source, make, and deliver functions. The research also shows how transparency can be increased via employing SCA capabilities.

This is one of first studies to empirically demonstrate that SCA capabilities can be used to increase supply chain transparency. The research also advances organizational information processing theory by illustrating an analytics capability paradox, where increased levels of certain analytics capabilities can become counterproductive in the face of supplier uncertainty.

This study aims to analyze the factors, evaluation techniques of the durability of existing railway engineering.

China has built a railway network of over 150,000 km. Ensuring the safety of the existing railway engineering is of great significance for maintaining normal railway operation order. However, railway engineering is a strip structure that crosses multiple complex environments. And railway engineering will withstand high-frequency impact loads from trains. The above factors have led to differences in the deterioration characteristics and maintenance strategies of railway engineering compared to conventional concrete structures. Therefore, it is very important to analyze the key factors that affect the durability of railway structures and propose technologies for durability evaluation.

The factors that affect the durability and reliability of railway engineering are mainly divided into three categories: material factors, environmental factors and load factors. Among them, material factors also include influencing factors, such as raw materials, mix proportions and so on. Environmental factors vary depending on the service environment of railway engineering, and the durability and deterioration of concrete have different failure mechanisms. Load factors include static load and train dynamic load. The on-site rapid detection methods for five common diseases in railway engineering are also proposed in this paper. These methods can quickly evaluate the durability of existing railway engineering concrete.

The research can provide some new evaluation techniques and methods for the durability of existing railway engineering.

The purpose of this study is to solve the problems of poor stability and high energy consumption of the dynamic window algorithm (DWA) for the mobile robots, a novel enhanced dynamic window algorithm is proposed in this paper.

The novel algorithm takes the distance function as the weight of the target-oriented coefficient, and a new evaluation function is presented to optimize the azimuth angle.

The jitter of the mobile robot caused by the drastic change of angular velocity is reduced when the robot is closer to the target point. The simulation results show that the proposed algorithm effectively optimizes the stability of the mobile robot during operation with lower angular velocity dispersion and less energy consumption, but with a slightly higher running time than DWA.

A novel enhanced dynamic window algorithm is proposed and verified. According to the experimental result, the proposed algorithm can reduce the energy consumption of the robot and improves the efficiency of the robot.

Although the use of instant messaging (IM) at work has been studied in the IS field, its effective use and impact on performance have not been adequately addressed. The purpose of this paper is to explore the antecedents and consequences of the effective use of IM at work by adapting Burton-Jones and Grange’s theory of effective use.

The authors introduce “Comprehensive IM policy” as a facilitator of adaptation and learning actions to improve the effective use of IM, which will improve communication quality and productivity. The impact of IM competence on effective use is also discussed. Based on a survey of 215 managers, this study applies the partial least square technique to test the research model.

The results indicate that comprehensive IM policy encourages adaptation and learning actions, which improve the effective use of IM and thereafter improve communication quality and productivity. Meanwhile, IM competence has a substitutive interaction effect with IM reconfiguration and self-learning on effective use.

The results refine the general theory of effective use and provide managers with an approach to leverage IM use at work for performance gains.

The economy of Fujian has caused tremendous development momentum and increased the economic exchanges between Taiwan and the mainland since the establishment of the free trade area of Fujian province. However, the contradiction between ecological protection and economic development should be considered on the basis of solving the problem of building trade zones. Performing regional trade zone planning from the ecological and economic perspectives is difficult, not only should the ecological and economic perspectives meet and emphasize the ecological and economical levels of interaction, but also achieve the optimization of eco-economic development potential of the free trade zone. In view of the above problems, according to the economy and resource allocation base of the new trade zone in Fujian Province, the space planning strategy development was carried out in this paper. In the overall layout of the new trade zone, the chain service area, the high-end service area, and the city near the port are taken as a major planning axis. North and South regions are gathering areas of shipping, logistics and new industries, developing on three partitions of function, improvement and advance around Haijing Road. The landscape is divided into Cai Jianwei mountain and Jing Kouyan mountain as the basis, and the third South China Sea Road and Haixin road form six corridors between mountain and sea. With the foundation of international cruise port development planning, the ease of east harbors should be promoted to north, and the development rhythm od airport planning areas should be strictly controlled, which can prepare for the later spatial release of the relocation of the airport from city planning areas. The main line of the important traffic node is reserved to improve the stock space, and the industrial neighborhood area near the central fishing port of Fujian and Taiwan is reserved, which can provide the regional space preparation for the planning of the new trade zone.