Factors affecting physicians using mobile health applications: an empirical study

Background Mobile health applications (mHealth apps) have created innovative service channels for patients with chronic diseases. These innovative service channels require physicians to actively use mHealth apps. However, few studies investigate physicians’ participation in mHealth apps. Objective This study aims to empirically explore factors affecting physicians’ usage behaviors of mHealth apps. Based on the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) and mHealth apps features, we propose a research model including altruism, cognitive trust, and online ratings. Methods We collected data from physicians who have used mHealth apps and conducted a factor analysis to verify the convergence and discriminative effects. We used a hierarchical regression method to test the path coefficients and statistical significance of our research model. In addition, we adopted bootstrapping approach and further analyzed the mediating effects of behavioral intention between all antecedent variables and physicians’ usage behavior. Finally, we conducted three robustness analyses to test the validity of results and tested the constructs to verify the common method bias. Results Our results support the effects of performance expectancy, effort expectancy, social influence, and altruism on the behavioral intentions of physicians using mHealth apps. Moreover, facilitating conditions and habits positively affect physicians using mHealth apps through the mediating effort of behavioral intention. Physicians’ cognitive trust and online rating have significant effects on their usage behaviors through the mediating efforts of behavioral intention. Conclusions This study contributes to the existing literature on UTAUT2 extension of physicians’ acceptance of mHealth apps by adding altruism, cognitive trust, and online ratings. The results of this study provide a novel perspective in understanding the factors affecting physicians’ usage behaviors on mHealth apps in China and provide such apps’ managers with an insight into the promotion of physicians’ active acceptance and usage behaviors.

Back to Basics: City Services and 311 Service Requests

With data from service delivery requests in twenty-nine cities, this article reports on a comparative analysis of city service request systems (311 systems) and their operations. The study uses actual 311 service request data available through cities’ open data portals. A typology of thirty service type categories was created. Various hypotheses were tested. As city population increases, the number of service requests also increases. Garbage/recycling is the most commonly requested service, followed by code enforcement requests, parking, pickup of bulk items, and abandoned vehicles. Cities are no longer just using telephones for their users to submit requests but have also now incorporated other service channels. Service resolution times vary across cities and service category but there is some evidence that safety and health types of services receive priority and are resolved more quickly. The article ends with managerial and policy implications.

Self­-service for public transport payments: A business case for conversational artificial intelligence

<p>Although self-service (i.e. mobile top-ups) is at the heart of Snapper’s customer service offering, customers have a disjointed experience managing their public transport payment cards across a range of customer service touchpoints, including more traditional support channels such as helpdesks and in-person support centres. Customer feedback indicates that some Snapper users perceive the process of resolving support issues through these traditional support channels to be inconvenient and time-consuming. Activity through these traditional channels still forms a large proportion of Snapper’s customer service, despite Snapper’s ongoing investment in their self-service channels; including mobile applications, the website, the MySnapper desktop application, and kiosks. Just as Snapper innovated to meet customer demand for self-service through a mobile app (Snapper Services Ltd., 2017a), the evolution of conversational artificial intelligence (AI), or chatbot technology, presents an opportunity for Snapper to lead the way in meeting customer demand for a faster, more accessible way to resolve common support issues. The successful development of such a solution will further position Snapper as a market-leader in customer-centric innovation. In order to understand the commercial potential of such an automated customer service offering, the research aims to understand customer use and perceptions of Snapper’s support channels; to identify barriers to the adoption of self-service, and understand how these can be addressed; and to understand customer attitudes towards automated customer service. Using a mixed methods approach, research began with analysis of secondary data accessed from Snapper’s internal customer service reporting. Findings validated customer demand for additional self-service options, as well as the repetitive nature of Snapper’s customer service queries, indicating that these are ripe for automation. In-depth interviews were conducted with Snapper cardholders, giving further insight into how they select and interact with Snapper’s customer service channels. The avoidance of perceived effort was identified as a key theme when explaining how customers navigate service channels, supporting the role of “ease of use” in explaining customer adoption of self-service technologies (Davis, Bagozzi, & Warshaw, 1989). Types of perceived effort were identified as social, cognitive and logistical effort. These categories are proposed as an extension to the Technology Acceptance Model (Davis et al., 1989), giving additional insight into what constitutes “ease of use”. Following the in-depth interviews, market analysis and discussions with AI and chatbot service providers explored best practice in automated customer service, to understand the adoption of conversational AI technology in the New Zealand context, as well as how other companies have successfully implemented a chatbot product. The project report concludes with a stand-alone business case for applying conversational AI technology to Snapper’s customer service offering. The business case summarises the business model and delivery methodologies recommended for the project development (see Section 6.1), including LEAN startup methods. The market validation phase (Section 6.2) then addresses the strategic business case, assessing the case for change and incorporating key findings from the customer and market research conducted earlier in the research. Building on the opportunities identified in the PESTEL analysis, the product validation phase (Section 6.3) utilises a SWOT analysis, before providing clear recommendations around the required feature-set of the proposed solution, and a possible roadmap for implementing these features. Finally, the economic, financial and commercial cases are addressed; including a cost-benefit analysis of the proposed solution, a recommended development methodology, and high-level resources and requirements required for implementation. By validating that delivering such an enhanced self-service offering is commercially viable, the project aims to deliver a more delightful experience to Snapper users, driving better uptake of Snapper’s self-service channels.</p>

Self­-service for public transport payments: A business case for conversational artificial intelligence

<p>Although self-service (i.e. mobile top-ups) is at the heart of Snapper’s customer service offering, customers have a disjointed experience managing their public transport payment cards across a range of customer service touchpoints, including more traditional support channels such as helpdesks and in-person support centres. Customer feedback indicates that some Snapper users perceive the process of resolving support issues through these traditional support channels to be inconvenient and time-consuming. Activity through these traditional channels still forms a large proportion of Snapper’s customer service, despite Snapper’s ongoing investment in their self-service channels; including mobile applications, the website, the MySnapper desktop application, and kiosks. Just as Snapper innovated to meet customer demand for self-service through a mobile app (Snapper Services Ltd., 2017a), the evolution of conversational artificial intelligence (AI), or chatbot technology, presents an opportunity for Snapper to lead the way in meeting customer demand for a faster, more accessible way to resolve common support issues. The successful development of such a solution will further position Snapper as a market-leader in customer-centric innovation. In order to understand the commercial potential of such an automated customer service offering, the research aims to understand customer use and perceptions of Snapper’s support channels; to identify barriers to the adoption of self-service, and understand how these can be addressed; and to understand customer attitudes towards automated customer service. Using a mixed methods approach, research began with analysis of secondary data accessed from Snapper’s internal customer service reporting. Findings validated customer demand for additional self-service options, as well as the repetitive nature of Snapper’s customer service queries, indicating that these are ripe for automation. In-depth interviews were conducted with Snapper cardholders, giving further insight into how they select and interact with Snapper’s customer service channels. The avoidance of perceived effort was identified as a key theme when explaining how customers navigate service channels, supporting the role of “ease of use” in explaining customer adoption of self-service technologies (Davis, Bagozzi, & Warshaw, 1989). Types of perceived effort were identified as social, cognitive and logistical effort. These categories are proposed as an extension to the Technology Acceptance Model (Davis et al., 1989), giving additional insight into what constitutes “ease of use”. Following the in-depth interviews, market analysis and discussions with AI and chatbot service providers explored best practice in automated customer service, to understand the adoption of conversational AI technology in the New Zealand context, as well as how other companies have successfully implemented a chatbot product. The project report concludes with a stand-alone business case for applying conversational AI technology to Snapper’s customer service offering. The business case summarises the business model and delivery methodologies recommended for the project development (see Section 6.1), including LEAN startup methods. The market validation phase (Section 6.2) then addresses the strategic business case, assessing the case for change and incorporating key findings from the customer and market research conducted earlier in the research. Building on the opportunities identified in the PESTEL analysis, the product validation phase (Section 6.3) utilises a SWOT analysis, before providing clear recommendations around the required feature-set of the proposed solution, and a possible roadmap for implementing these features. Finally, the economic, financial and commercial cases are addressed; including a cost-benefit analysis of the proposed solution, a recommended development methodology, and high-level resources and requirements required for implementation. By validating that delivering such an enhanced self-service offering is commercially viable, the project aims to deliver a more delightful experience to Snapper users, driving better uptake of Snapper’s self-service channels.</p>

Mathematical Interpretation of Global Competition between Payment Systems

The present study seeks to present a mathematical interpretation of the perception of new entrants to the global payments market on whether FinTech’s innovations promote competitiveness in the market. The study also quantitatively evaluates the competitive trends observed in the global payments market. The sampling frame consists of 504 respondents across 28 countries. The study is based on the analysis of the survey results using SPSS Statistics and Stata. The survey addressed four groups of factors influencing the competitiveness of the payment system (i.e., ‘Costs’, ‘Service Channels’, ‘Privacy and Security’, ‘Quality and Efficiency’). The respondents believe that FinTech innovations have a strong positive effect on the payment system’s competitiveness (M = 4.32). The results of the present study may be of interest to specialists and researchers who deal with the competitive dynamics of the global payments business, with payment innovations, and with convergence and transformation of the payments industry.

Scientific journals should be transformed into science storytellers to improve their visibility

What is the objective for science journals to publish research papers? Would it be enough to collect research manuscripts and simply publish them in print or on the web? Science journal publishers have always strived to find ways of disseminating journal content to as many readers as possible. It is now time for science journal publishers to think about why a journal should be published; whether it is acceptable for valuable scientific findings to lie dormant in a journal’s archive; and whether traditional science communication is still effective. The present article suggests that science journals should transform themselves into science storytellers to improve the visibility and discoverability of their research findings. First, a new communication network between journals, authors, peers, the public, and policymakers is required. Second, conversion of media from academic language to plain language is critical to broadening the audience. Third, audio-visual content should be introduced into journal publishing to facilitate easy comprehension of the content. Fourth, research-focused channels, including EurekAlert, Medium, and social networking service channels are recommended as new media to propagate journals’ content to researchers. Improving visibility and discoverability is an urgent mission, especially for small society journals. To achieve this mission, science journals should be adapted to become storytellers and science communicators, as suggested above. A small society journal’s editor is not merely an editor, but an editor-publisher; therefore, editors should understand and take on this role.

How Does Employee Infusion Use of CRM Systems Drive Customer Satisfaction? Mechanism Differences Between Face-to-Face and Virtual Channels

To attain customer satisfaction, service firms invest significant resources to implement customer relationship management (CRM) systems to support internal customer service (CS) employees who provide service to external customers in both face-to-face and virtual channels. How CS employees apply sophisticated CRM systems to interact with customers and how the mechanisms through which their CRM usage affects customer satisfaction vary across service channels and bear important implications. We approach these issues by investigating the concept of infusion use, defined as CS employees’ assessment of the extent to which they use a CRM system to its fullest potential to best support their work in the CRM-enabled service interaction context. Drawing on the IS success framework and expectation confirmation theory, we first formulate a baseline model that explains the direct and indirect mechanisms through which CS employees’ infusion use of CRM systems leads to customers’ expectation confirmation, which in turn affects customers’ satisfaction. We then draw on the lenses of media richness and communication adaptation to theorize why these two mechanisms exert differential influence in face-to-face and virtual channels. We test the hypotheses by collecting multiwave data from CS employees, customers, and firm archives of a Fortune 500 telecom service firm. We find that (1) CS employee infusion use can directly contribute to customer expectation confirmation and indirectly do so through CS employees’ satisfaction with the system (i.e., user satisfaction), and (2) the direct mechanism plays a more critical role in the face-to-face channel, whereas the indirect mechanism is more important in the virtual channel. Our findings inform managers of the avenues through which employees’ infusion use promotes CRM-enabled service success across face-to-face and virtual service channels.

Performance Evaluation Of 802.11P Vanets With Different Duty Cycles

With the improvement in technology, the number of vehicles on the roads has increased tremendously over the last decade. As large numbers of vehicles are on the road it has become almost a necessity to improve the driving conditions, provide all the facilities and to make sure that driving is safe for drivers around the world. To utilize this information the vehicle has to alternately switch between control (CCH) and service channels (SCH) effectively. So, in our work we evaluate the performance of vehicles on control and service channels with different duty cycle such that no critical message is missed and multimedia applications can be used concurrently. For this purpose we developed 802.11p simulator and the experiments proved that CCH should use duty cycle of 0.6 as delay tolerant data has to be transmitted over it and SCH should work on 0.3 which is delay insensitive, but generates revenue for the industry by using different multimedia applications as requested by the user.