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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.

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 aims to simultaneously examine customer complaints through the proposed novel Design for Six Sigma (DFSS) model which incorporates of creating the new insurance services to win customers' hearts and mind for the insurance industry.

A novel DFSS research methodology which includes the theory of inventive problem solving (TRIZ), Pugh concept selection, creative product analysis matrix and importance–satisfaction model (I–S Model) was proposed. In addition, a real insurance company case was studied to illustrate the effectiveness of the proposed DFSS model.

The results of a novel DFSS model not only can establish new services, but also can dramatically reduce the cost of resolving customer complaints.

The findings of this study are useful for insurance companies and other related service providers in devising tailored strategies to offer quality and suitable services to their customers.

This study addresses the paucity of research and marketing gaps through the proposed novel DFSS model for the first time in the insurance industry. These study findings would enable researchers and practitioners to formulate strategies for solving customer complaints effectively and develop new services from time to time.

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.

This paper aims to analyze the pressure distribution of rectangular aerostatic thrust bearing with a single air supply inlet using the complex potential theory and conformal mapping.

The Möbius transform is used to map the interior of a rectangle onto the interior of a unit circle, from which the pressure distribution and load carrying capacity are obtained. The calculation results are verified by finite difference method.

The constructed Möbius formula is very effective for the performance characteristics researches for the rectangular thrust bearing with a single air supply inlet. In addition, it is also noted that to obtain the optimized load carrying capacity, the square thrust bearing can be adopted.

The Möbius transform is found suitable to describe the pressure distribution of the rectangular thrust bearing with a single air supply inlet.

The research on leaching behaviors of heavy metals in municipal solid waste incineration (MSWI) fly ash is of great significance. Because of the limitations like experimental condition, experiment data volume of heavy metals is difficult to achieve the prediction of requirements. In order to solve the problem of uncertainty and fuzziness caused by small sample, a new method based on fuzzy theory is proposed in this paper. By comparing fitting results from measured data and Visual MINTEQ simulation results, the method in this paper is considered to be more reliable and has a better interpretation for the leaching behaviors of heavy metals. The simulation results show the feasibility and superiority of the proposed method.

This study aims to demonstrate the positive effect of human capital disclosure on firm performance, and to specify the boundary conditions of the relationship.

The study applies the signaling and stakeholder perspectives and uses a one‐year lag design to avoid reverse causality in exploring the human capital disclosure and performance link. Content analysis of annual reports and hierarchical regression are applied.

Human capital disclosure positively impacts on organizational performance such as market‐to‐book ratio and ROA. Organizational size negatively moderates the relationship between disclosure of human capital information and firm performance. Knowledge intensity has curvilinear positive moderation effect between the relationship above.

Human capital disclosure can help communicate to various stakeholders. Organizational performance can thus be enhanced through the communication process. Disclosure in the context of higher knowledge intensity is more beneficial.

The paper theoretically and empirically links up human capital disclosure and organizational performance. It also identifies both the diminishing return and increasing return moderation effects by organizational size and knowledge intensity between the human capital disclosure and performance link.

Although organizations expect employees to share knowledge with each other, knowledge hiding has been documented among coworker dyads. This paper aims to draw on social exchange theory to examine if and why knowledge hiding also occurs in teams.

Two studies, using experimental (115 student participants on 29 teams) and field (309 employees on 92 teams) data, explore the influence of leader-member exchange (LMX) on knowledge hiding in teams, as well as the moderating role of collective (team-level) prosocial motivation.

The results of experimental Study 1 showed that collective prosocial motivation and LMX reduce knowledge hiding in teams. Field Study 2 further examined LMX, through its distinctive economic and social facets, and revealed the interaction effect of team prosocial motivation and social LMX on knowledge hiding.

This study complements existing research on knowledge hiding by focusing specifically on the incidence of this phenomenon among members of the same team. This paper presents a multi-level model that explores collective prosocial motivation as a cross-level predictor of knowledge hiding in teams, and examines economic LMX and social LMX as two facets of LMX.