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The Bed and Breakfast (B&B) enterprises generally lack sufficient human resources and time to conduct research on important social media marketing factors for visitors’ satisfaction and visitors’ intentions. Therefore, this study aims to provide crucial social media marketing and factors and service quality elements for improving customer satisfaction and customer loyalty in B&B sectors. This study also provides some recommendations for attracting more visitors and increasing customer satisfaction and customer loyalty through social media.

First, social media marketing factors and service quality elements were identified through the systematic literature review. Then these identified factors and elements were used to design a survey questionnaire for collecting data. The research data included responses of 64 B&B enterprises and 625 customers. The collected data was analyzed by feature selection approaches including Decision Tree algorithm and Information Gain to identify the key factors for improving customer satisfaction and customer loyalty.

The findings of this study determined that featured choice is an important social media marketing factor, and assurance is the common service quality element for both B&B enterprises and their customers in terms of satisfaction and loyalty.

This study adds a value to the growing literature on customer satisfaction and loyalty in B&B sectors by exploring key social media marketing factors and service quality elements. The study reveals several implications for theories and practices. The findings hopefully help B&B enterprises better social media marketing with less workforce and budget.

Previous studies have vague views about whether employees who are required to complete large amounts of work (i.e. role overload) would proactively create a change in their job characteristics (i.e. job crafting), because the cognitive mechanism underlying the nexus between role overload and job crafting is unclear. The aim of this study is to identify why and when role overload has an impact on job crafting.

This study builds a second-stage moderated mediation model. Using a two-wave panel field study of 213 employee–supervisor matched data, this study examines the proposed hypotheses.

Results show that role overload decreases construal level, which can determine the tendency of employees to focus on the feasibility (low level of construal) or desirability (high level of construal) of behaviors. Goal self-concordance is the degree to which employees pursue their personal goals based on feelings of personal interests and values. The authors find that goal self-concordance guides employees who have higher levels of construal to exert more effort in job crafting. The authors further find that goal self-concordance moderates the mediating role of construal level. Specifically, for employees in pursuit of self-concordant goals, role overload reduces their construal level, resulting in less effort in job crafting. For employees who do not pursue self-concordant goals, role overload decreases their construal level, thereby improving job crafting.

The findings of this study enrich the literature on role overload and job crafting by revealing the mechanism and boundary conditions of the relationship between role overload and job crafting.

Due to the high use of mobile devices, the market share of mobile advertisements (Ads) is significantly growing. Although mobile Ads can contact potential customers at any time and in any location depending on their unique demands, one of the biggest problems for advertisers is how to improve customer repurchases with their Ads. The development and empirical support of customer repurchase through mobile Ads context have not been addressed. Therefore, the purpose of this paper is to define and identify the key attributes of customer repurchase in a mobile Ads context.

In this research, the set of attributes was derived from a systematic literature review and finalized by applying the Fuzzy Delphi method. To develop a hierarchical model and classify the cause/effect groups among identified key attributes, the Fuzzy mixed approach uses a combination of Fuzzy interpretive structural modeling-decision-making trial and evaluation laboratory.

The findings suggest that language, type of website and social media are classified to as essential attributes for improving customer repurchase through mobile Ads.

The focus of the current research is limited to identify and develop the hierarchical interrelationships between customer repurchase attributes that are unique to the mobile Ads business context. Additional research may be conducted for various media contexts and other products/services categories.

This study illustrated how multicriteria decision-making techniques could be used effectively using Fuzzy theory to explore the research area of customer repurchase in mobile Ads concept.

This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.

The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.

DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”

This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.

Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

Development of urban-rural integration is essential to fulfill sustainable development goals worldwide, and comprehension about urban-rural integration types has been highlighted as increasingly relevant for an efficient policy design. This paper aims to utilize an unsupervised machine learning approach to identify urban-rural integration typologies based on multidimensional metrics regarding economic, population and social integration in China.

The study introduces partitioning around medoids (PAM) for the identification of urban-rural integration typologies. PAM is a powerful tool for clustering multidimensional data. It identifies clusters by the representative objects called medoids and can be used with arbitrary distance, which help make clustering results more stable and less susceptible to outliers.

The study identifies four clusters: high-level urban-rural integration, urban-rural integration in transition, low-level urban-rural integration and early urban-rural integration in backward stage, showing different characteristics. Based on the clustering results, the study finds continuous improvement in urban-rural integration development in China which is reflected by the changes in the predominate type. However, the development still presents significant regional disparities which is characterized by leading in the east regions and lagging in the western and central regions. Besides, achievement in urban-rural integration varies significantly across provinces.

The machine learning techniques could identify urban-rural integration typologies in a multidimensional and objective way, and help formulate and implement targeted strategies and regionally adapted policies to boost urban-rural integration.

This is the first paper to use an unsupervised machine learning approach with PAM for the identification of urban-rural integration typologies from a multidimensional perspective. The authors confirm the advantages of this machine learning techniques in identifying urban-rural integration types, compared to a single indicator.

This paper aims to test the effects of social presence (SP) and other related factors, including trust, self-construal and brand familiarity, in affecting consumers’ purchase intention of live-streaming workout courses.

Three between-subjects, Web-based experimental studies were conducted. There were separately 108, 208 and 284 valid questionnaires collected in the three studies. Moderation and mediation analyses were performed to test the research hypotheses.

Users’ sense of SP when watching live-streaming courses positively affects their willingness to purchase courses, and users’ sense of trust plays a mediating role in the influence of users’ SP on their purchase intention. In addition, when users belong to dependent self-construal rather than independent self-construal, their trust in live-streaming courses plays a stronger mediating role in the influence of users’ SP on their purchase intentions. What is more, when users have high brand familiarity rather than low brand familiarity, their trust in online live-streaming plays a stronger mediating role in the influence of users’ SP on their purchase intentions.

This research delineates the effects of SP on the consumption of live-streaming courses, thus further adding to the understanding of the role of real-time interaction in determining consumer behavior. It also highlights the roles of self-construal and brand familiarity as mediating influences on the relationship between SP and consumer trust.

The purpose of this paper is to reveal the solute segregation behavior in the molten and solidified regions during direct chill (DC) casting of a large-size magnesium alloy slab under no magnetic field (NMF), harmonic magnetic field (HMF), pulsed magnetic field (PMF) and two types of out-of-phase pulsed magnetic field (OPMF).

A 3-D multiphysical coupling mathematical model is used to evaluate the corresponding physical fields. The coupling issue is addressed using the method of separating step and result inheritance.

The results suggest that the solute deficiency tends to occur in the central part, while the solute-enriched area appears near the fillet in the molten and solidified regions. Applying magnetic field could greatly homogenize the solute field in the two-phase region. The variance of relative segregation level in the solidified cross-section under NMF is 38.1%, while it is 21.9%, 18.6%, 16.4% and 12.4% under OPMF2 (the current phase in the upper coil is ahead of the lower coil), HMF, PMF and OPMF1 (the current phase in the upper coil lags behind the lower coil), respectively, indicating that OPMF1 is more effective to reduce the macrosegregation level.

There are few reports on the solute segregation degree in rectangle slab under magnetic field, especially for magnesium alloy slab. This paper can act a reference to make clear the solute transport behavior and help reduce the macrosegregation level during DC casting.

This chapter examines the significance of green innovation and technologies for sustainable business in Asia, focussing on the issues and challenges confronted by contemporary sustainable business models. The concept of sustainable development is introduced, which seeks to minimize negative impacts on ecosystems and preserve the environment for future generations. This chapter's methodology entails a comprehensive review of existing literature and research on green innovation, green technology, and sustainable business models in Asia. The expansion of ‘green’ energy is directly proportional to the global demand for energy resources. Understanding how green innovation influences a company's capacity for sustainable development is essential for identifying the factors that influence sustainable business models and their economic consequences. Green innovation practices encompass a variety of factors, including government regulations, preferences, supplier competence, and consumer concerns. Green technologies, such as green human resource management (HRM) practices and green innovation practices, play a crucial role in attaining sustainable development by conserving energy, protecting the environment, and enhancing business efficiency. Businesses that adopt green innovation acquire a competitive edge and enhance their performance. This chapter emphasizes the importance of green innovation research and application for business stability in Asia, where sustainability and green concepts are acquiring momentum. Customer, government, and societal pressures further emphasize the significance of green innovation in businesses. For the success of ecological innovation practices, collaboration and knowledge-sharing among various stakeholders are crucial. The adoption of green innovation practices is influenced by external environmental impacts, stakeholder pressure, and organizational support. Green technology innovation, which concentrates on resource conservation, energy efficiency, and environmental protection, is crucial to the sustainability of a business. This chapter concludes by emphasizing the importance of business sustainability in achieving environmental and economic goals and assuring sustainable corporate development. Long-term success requires an understanding of the process of value creation, delivery, and capture within sustainable business models.

Corporate governance scholars have built on agency theory premises to document chief executive officers' (CEOs’) debt-based compensation, also known as inside debt, as an effective tool to control excessive risk and deter risky corporate strategies. In this study, the authors draw on behavioral agency model to put these well-established assumptions to the test in a different setting and argue for the context-specific effects of CEOs' long-term compensation.

Focusing on corporate mergers and acquisitions in a post-crisis period (2011–2017), the authors cast doubt on agency theory predictions on debt-like compensation, point to the more realistic assumptions of behavioral decision models, and call for more contingency approaches in theoretical arguments.

An analysis of more than 4000 observations reveals that neither CEOs nor shareholders react significantly to inside debt after the economy recovers. Firm risk is also influenced only marginally by long-term compensation in a normal period of time.

While extant literature is rather unanimous on risk-reducing impact of inside debt, the study periods span the financial crisis of 2007. This research is the first conducted in regular times to demonstrate that previous findings are biased and heavily influenced by an exogenous shock.

The purpose of this study is to examine whether and how financial performance feedback influences green innovation performance by drawing on the behavioral theory of the firm (BTOF) and relying on motivation-based logic.

A total of 17,558 firm-year observations from 3,062 publicly traded firms in China are used as the research sample.

The results reveal that low-performing firms are less likely to conduct green innovation activities because managers burden pressure to meet short-term targets. This study further finds that these relations are moderated by institutional ownership.

This study contributes to the BTOF literature by linking performance feedback to green innovation activities. This study applies a motivation-based logic to relate performance below and above aspirations to green innovation activities. This study introduces institutional ownership as a boundary condition.