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Recently the advances in wireless communication technology and the popularity of portable computers have rendered mobile computing environments from which mobile users with battery‐operated palmtops can access the information via wireless channels, without space and time restriction. In mobile computing environments, mobile users cache the data items to use the bandwidth efficiently and improve the response time of mobile transactions. If the data items cached in mobile users are updated at the server, the server broadcasts an invalidation report for maintaining the cache consistency of mobile users. However, this method has an obstacle that does not guarantee the serializable execution of mobile transactions. The purpose of this paper is to propose the four types of reports for mobile transaction (FTR‐MT) method for ensuring the serializable execution of mobile transactions.

The paper describes the FTR‐MT method, which is composed of four types of algorithms, e.g. group report composition algorithm, immediate commit decision algorithm, cache consistency algorithm, and disconnection cache consistency algorithm. FTR‐MT method for improving the response time of mobile transactions makes a commit decision by using the four types of reports.

With the FTR‐MT method, mobile users can make a commit decision by using the four types of reports. The response time of mobile transactions can be reduced. Furthermore, the FTR‐MT method can improve the cache efficiency in the case that the disconnection of mobile users is longer than the broadcast interval of the window report.

This paper proposes a new method for guaranteeing the serializable execution of mobile transactions, called FTR‐MT, using four types of reports. Also, it can prevent the entire cache dropping, even though the disconnection of a mobile host is longer than the broadcast interval of a window report. Through the analytical model, this method is felt to be superior to other methods, in terms of the average response time and the commit rate of mobile transactions, and bandwidth usage.

Drawing on self-determination theory, this paper compares the effects of QR code payment method (autonomous vs dependent payment) on payment pleasure, its mechanism and the boundary condition in the mobile payment setting.

Four studies were conducted to examine the effect of QR code payment method on payment pleasure. In study 1, 108 undergraduate students were asked to recall a recent experience when they made either autonomous payment or dependent payment. Study 2 assigned 74 undergraduate students to either the autonomous or dependent payment. Study 3 replicated study 2, but recruited 75 customers in the field. For study 4, a total of 134 undergraduate students participated in a 2 (payment method: autonomous payment vs dependent payment) × 2 (product involvement: high vs low) between-subjects design.

The results of these four studies demonstrate that (1) customers derive more payment pleasure from autonomous payment, compared with dependent payments (study 1); (2) the sense of control mediates the effect of the payment method on payment pleasure (study 2 and study 3); and (3) product involvement moderates the mediating effect of the sense of control (study 4).

These findings contribute to the literature on mobile payment and payment experience. These findings also provide insight to merchants when they select an appropriate payment method and manage the customer payment experience.

The bursting of the e‐bubble affected expectations with regard to mobile initiatives and willingness to invest in them very negatively. Business managers request detailed and thorough analyses prior to engaging in mobile initiatives. The paper aims to present a method, with which mobile business can be introduced to the customer relationship management (CRM) field.

The paper follows the design science paradigm as outlined by March and Smith, and Hevner et al.

The findings provide a framework for the definition of a mobile CRM strategy derived from the corporate strategy, suggest a method for the identification and exploitation of the mobilization potential in CRM processes in line with the strategy, and provide guidance for the design of mobile information systems to support these processes.

The proposed method extends the body of available methods with a method for the introduction of mobile ISs into marketing, sales and service organizations. As design research, it does not strive for statistical generalization. The level of detail given in the elements of the method is to be increased in further research.

The method helps to reduce risk and uncertainty of mobile CRM initiatives, since it provides a structured and consistent procedure for the definition of goals, the identification of potentials for the fulfillment of these goals as well as recommendations for the systematic exploitation of these potentials.

With the application of this structured method, an organization should be able to avoid the pitfalls of technology‐driven information technology initiatives which various companies have experienced, particularly with mobile technologies.

Purpose — In this chapter, we will review several alternative methods of collecting data from mobile phones for human mobility analysis. We propose considering cellular network location data as a useful complementary source for human mobility research and provide case studies to illustrate the advantages and disadvantages of each method.

Methodology/approach — We briefly describe cellular phone network architecture and the location data it can provide, and discuss two types of data collection: active and passive localization. Active localization is something like a personal travel diary. It provides a tool for recording positioning data on a survey sample over a long period of time. Passive localization, on the other hand, is based on phone network data that are automatically recorded for technical or billing purposes. It offers the advantage of access to very large user populations for mobility flow analysis of a broad area.

Findings — We review several alternative methods of collecting data from mobile phone for human mobility analysis to show that cellular network data, although limited in terms of location precision and recording frequency, offer two major advantages for studying human mobility. First, very large user samples – covering broad geographical areas – can be followed over a long period of time. Second, this type of data allows researchers to choose a specific data collection methodology (active or passive), depending on the objectives of their study. The big mobile phone localization datasets have provided a new impulse for the interdisciplinary research in human mobility.

Originality/value of chapter — We propose considering cellular network location data as a useful complementary source for transportation research and provide case studies to illustrate the advantages and disadvantages of each proposed method. Mobile phones have become a kind of “personal sensor” offering an ever-increasing amount of location data on mobile phone users over long time periods. These data can thus provide a framework for a comprehensive and longitudinal study of temporal dynamics, and can be used to capture ephemeral events and fluctuations in day-to-day mobility behavior offering powerful tools to transportation research, urban planning, or even real-time city monitoring.

With the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.

In this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.

Experimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.

In this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

Many companies struggle with the assessment of customer experience. This chapter aims to demonstrate how mobile ethnography tackles this issue by assessing data in a holistical way, in-situ, and in real-time.

The chapter describes the implementation of a mobile ethnography project in a tourist destination, including participant recruitment, data collection, data analysis, and the derivation of insights.

The mobile ethnography project allowed to gain deep insights into the customers’ journeys.

Future research will need to further investigate questions of participant recruitment, the effectiveness of incentives as well as the performance of the data collection process. Furthermore the findings of this case need to be replicated in the context of other industries, as well as in other cultural contexts.

Mobile ethnography allows companies to gain more information on customer experience in real-time, thus with reduced cognitive and emotional bias. Therefore, the method can help to improve the touristic service offering and, consequently, customer experience.

As companies are searching for new approaches to research and manage customer experience, this chapter is of high value for both academia and practice.

This paper aims to discuss research ethics in mixed-methods research (MMR) and MMR development with a focus on ethical challenges that stem from working with technical instruments such as mobile eye-trackers.

The case of an interdisciplinary mixed-methods development study that aimed at researching the impacts of emerging mobile augmented-reality technologies on the perception of public places serves as an example to discuss research-ethical challenges regarding (1) the practical implementation of the study, (2) data processing and management and (3) societal implications of developing instruments to track and understand human practices.

This study reports challenges and experiences in ethical decision-making in the practical implementation of the study regarding the relationship to research subjects, the use of mobile research instruments in public places and the interdisciplinary cooperation among research team members. Further, this paper expounds on ethical challenges and recommendations in data processing and management and with a view to societal implications of method development and the aspirations of transdisciplinarity. This study concludes that institutionalized ethics need to become more flexible, while applied ethics and reflection must make their entry into university curricula across disciplines.

Complex interdisciplinary mobile and mixed-methods projects that involve sensors and instruments such as mobile eye-trackers are on the rise. However, there is a significant lack of engagement with practical research ethical challenges, practices and requirements in both mixed-methods and method-development literature. By taking a context- and process-oriented perspective focusing on doing ethics, the paper contributes a concrete empirical case to these underdeveloped fields.

The adoption of mobile payment remains low in certain regions, highlighting the need to identify the factors that enable and inhibit its adoption. This study aims to address this gap by investigating the role of information security, loss aversion and the moderating influence of the herd effect on Inertia and behavioral intentions in the adoption of mobile payment systems.

A structural equation model was developed and tested with 332 valid questionnaires to examine the proposed hypotheses.

The empirical results reveal that information security plays a significant role as an enabler, while loss aversion acts as an inhibitor of mobile payment adoption. Furthermore, the study uncovers the moderating influence of the herd effect on the relationship between Inertia and behavioral intentions.

This study was conducted in a specific region and may not be generalizable to other regions. Future studies could expand the sample size and scope to enhance the external validity of the findings.

This study offers practical implications for mobile payment service providers. Understanding the key enabling and inhibiting factors identified in this study can guide providers in designing and improving their services. Strengthening information security measures can help build trust among potential adopters, while offering incentives can mitigate the impact of loss aversion and encourage early adoption.

The findings of this study have social implications as they contribute to promoting the adoption of mobile payment systems. Increased adoption can enhance financial inclusion and stimulate economic development.

This study provides novel insights into the enabling and inhibiting factors of mobile payment adoption and highlights the moderating role of the herd effect. By shedding light on the influence of social norms on individual behavior in the context of mobile payment adoption, this study contributes to the existing literature and advances our understanding of this phenomenon.

Mobile users increasingly employ location-based map searches in their daily lives. However, it is still relatively unknown about mobile users’ map related search behaviors. The purpose of this paper is to discover the interactions between the users and mobile map search systems, to reveal the shortcomings of existing mobile map search functions, and to propose improvement suggestions.

Based on a set of controlled user experiments performed on the Baidu mobile phone map, this paper empirically examines users’ location-based mobile search behaviors, such as timing, metering, judging and so on. This paper also conducts statistical correlation tests to generate relation tables and diagrams regarding each variable, for example, the relation between the retrieval time and the retrieval steps.

The results indicate that mobile map users have two important characteristics in their search behaviors: first, mobile map users always follow the single search path. Second, the mobile map search efficiency of users is always low.

The situation simulation testing method is mainly used for the construction of a mobile information search behavior environment, which may make the users be nervous and have some effect on the search efficiency.

Based on the identification of user behaviors, this paper provides suggestions to optimize and improve mobile map search systems.

This paper studies users’ mobile map search behavior based on location and explores the features of user behavior from the perspective of human-computer interaction.

The purpose of this study is to examine the role of consumption emotion in the digital food-ordering experience by comparing the performances of the three digital ordering methods in an experimental design.

A research model was developed based on the Feelings-as-Information (FaI) theory and the expectancy-disconfirmation theory. A 3 × 2 between-subjects lab experiment was conducted to compare the three digital ordering methods (online, mobile or chatbot) in two different types of restaurants (quick-service or full-service).

The results indicate that the chatbot ordering method evoked more negative emotions and less positive emotions than the other two methods. The online ordering method worked the best for quick-service restaurants, whereas the mobile ordering method was most suitable for full-service restaurants. Both positive and negative emotions (comfort and annoyance) significantly mediated the relationships between the ordering method and internal responses (satisfaction and behavioral intention). Only one negative emotion (anger) significantly mediated the relationship between the ordering method and order amount.

This is the first study that attempts to explore and compare consumers’ emotional responses resulting from restaurant digital ordering experiences in the context of the three food-ordering methods. The use of the FaI theory strengthens the theoretical foundation of research on emotion in the hospitality field. This study also pioneers the application of chatbot technology in the restaurant industry.

本论文旨在研究消费情感在线上点菜体验中的作用, 采用实验设计中的三种线上点菜方式。

本论文基于情绪即信息（Fal理论）和期望-不一致理论创建了研究模型。实验设计为3×2被试间设计来比较三种线上点菜方式（在线、手机、或聊天机器人）在两种不同餐厅类型（快餐vs全服务餐厅）

研究结果表明, 聊天机器人点菜方式相比较其他两种方式, 将带来更多负面情绪和较少的正面情绪。在线点菜方式最适用于快餐类型的饭店, 手机点菜方式最适合全服务餐厅。正面和负面情绪（舒服和烦恼）都对点菜方式和情绪反应（满意度和行为意图）之间的关系起到中介作用。只有一种负面情绪（愤怒）对在线方式和点菜量之间存在显著的中介作用。

本论文是首篇探索和对比消费者面对三种点菜方式, 在餐厅线上点菜体验的情绪反应。本论文使用Fal理论巩固了酒店管理领域中情感研究的理论深度。本论文还探索了饭店中对聊天机器人应用的科研。