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Previous researches have proven that customer satisfaction and loyalty are affected by complicated relationships and are challenging to European customer satisfaction index (ECSI) model. Existing approaches mostly limit their hypotheses to linear relationships, which hinder much information that would lead to better modeling and understanding the relationship between customer satisfaction and loyalty. The purpose of this paper is to reveal potential nonlinear and interaction effects that might be embedded in antecedents of ECSI by exemplifying it in Turkish telecommunications sector.

This papar has justified the validity and reliability of the ECSI model implementation in Turk Telekom Company. The path models are tested via conventional structural equation modeling (SEM) and using a novel method, i.e. universal structure modeling with Bayesian neural networks.

The findings of this study reveal that quality has the most important impact on customer satisfaction. The next important construct was found to be the company image. The relationship between customer expectation and customer satisfaction was revealed to be insignificant. This study reveals the fact that while using the ECSI model more attention must be paid to the consideration of potential nonlinear relationships that might be available among model constructs.

This research presents uniqueness in that it reveals significant nonlinear relationships between the model constructs of the ECSI model. Previous studies have identified purely linear relationships, which may not hold true in reality. However, in this study it is revealed that improving one determinant of customer satisfaction may not be as worthy as it is assumed to be in theory, which refers to a nonlinear relationship.

Partial least squares (PLS) path modeling is a variance-based structural equation modeling (SEM) technique that is widely applied in business and social sciences. Its ability to model composites and factors makes it a formidable statistical tool for new technology research. Recent reviews, discussions, and developments have led to substantial changes in the understanding and use of PLS. The paper aims to discuss these issues.

This paper aggregates new insights and offers a fresh look at PLS path modeling. It presents new developments, such as consistent PLS, confirmatory composite analysis, and the heterotrait-monotrait ratio of correlations.

PLS path modeling is the method of choice if a SEM contains both factors and composites. Novel tests of exact fit make a confirmatory use of PLS path modeling possible.

This paper provides updated guidelines of how to use PLS and how to report and interpret its results.

Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.

The purpose of this paper is to propose an automated machine learning (AutoML) and multi-agent system approach to improve overall product delivery satisfaction under limited resources.

An AutoML method is purposed to model delivery satisfaction of individual customer, and a heuristic method and multi-agent system are proposed to improve overall satisfaction under limited processing capability. A series of simulation experiments have been conducted to illustrate the effectiveness of the proposed methodology.

The simulated results show that the proposed method can effectively improve overall delivery satisfaction, especially when the demand of customer orders is highly fluctuating and when the customer satisfaction models are highly diversified.

The proposed framework provides a more dynamic and continuously improving way to model delivery satisfaction of individual customer, thereby supports companies to provide personalized services and develop scalable and flexible business at a lower cost, and ultimately improves the overall quality, efficiency and effectiveness of delivery services.

The proposed methodology utilizes AutoML and multi-agent system to model individual customer delivery satisfaction and improve the overall satisfaction. It can cooperate with the existing delivery resource planning methods to further improve customer delivery satisfaction. The authors propose an AutoML approach to model individual customer delivery satisfaction, which enables continuous update and improvements. The authors propose multi-agent system and a heuristic method to improve overall delivery satisfaction. The numerical results show that the proposed method can improve overall delivery satisfaction with limited processing capability.

The purpose of this paper is to investigate the role of the retailer’s brand strength as a potential predictor of loyalty. It also examines the role of customer satisfaction (CS) to the retailer’s loyalty as well as its impact on the retailer’s brand strength.

The study was conducted in the grocery context and in a market under recession using the European Customer Satisfaction Index (ECSI) model. Data were collected through a telephone survey from 2,000 participants responsible for the household grocery shopping with a quota of 250 respondents from each of the leading grocery retailers in Greece. A formative measurement model was developed and the collected data were analyzed using partial least square path modeling.

The findings revealed that the strength of the retailer’s brand and CS influence retail loyalty and that brand strength mediate the strength of CS to loyalty. Results also suggested that the expectations and the perceptions toward the retailer’s product offering are the most important drivers of CS and loyalty. Thus, the study has proved the importance of the functional store attributes to CS and loyalty in the grocery store setting.

Research examining the suitability of the ECSI model in the grocery setting and in a market under economic crisis is scarce. This paper addresses these shortcomings by examining a customer loyalty model which incorporates the brand strength construct and investigates the role of brand strength as a potential predictor of loyalty as well as the role of CS in the brand strength and loyalty.

This paper aims to investigate the influential aspects of industrial branding in building customer brand engagement from the buyer’s and the seller’s points of view. Collecting buyer and seller information is essential to understand business-to-business interaction better. Buyer’s and seller’s perspective integration is significant for stakeholders to develop proper strategies to achieve customer brand engagement.

This study uses a structural equation model to examine the antecedents of customer brand engagement from the buyer’s perspective; then, the result is compared with the seller’s view by conducting an analytical hierarchy process. The authors exercise 140 valid data from the buyer’s industry and 9 experts from the seller’s industry.

This study finds that in developing customer brand engagement, rational brand quality is the most influential from the buyer’s view and top priority from the seller’s view. Surprisingly, both parties have different perspectives about the second and third priorities. The buyers put emotional brand associations as a second priority; perceived value is meaningless and insignificant. On the contrary, the sellers set the perceived value as the second priority and emotional brand associations as the last.

The respondents from the buyer industry cover various industries, and the research is limited to the buyer and the seller in the chemical polymer emulsion market, a market where product quality and application quality on the buyers’ side are essential and where the buyer–seller interaction is intense. Replicating the study in other industries and cultural backgrounds is recommended for generalization.

The paper’s novelty is that there are different priorities and perspectives from the buyer’s and the seller’s views. This study contributes to industrial brand engagement research studies. Investigation of the buyer’s and the seller’s perspectives in industrial brand engagement research studies is still limited.

The prediction of graduation rates of college students has become increasingly important to colleges and universities across the USA and the world. Graduation rates, also referred to as completion rates, directly impact university rankings and represent a measurement of institutional performance and student success. In recent years, there has been a concerted effort by federal and state governments to increase the transparency and accountability of institutions, making “graduation rates” an important and challenging university goal. In line with this, the main purpose of this paper is to propose a hybrid data analytic approach which can be flexibly implemented not only in the USA but also at various colleges across the world which would help predict the graduation status of undergraduate students due to its generic nature. It is also aimed at providing a means of determining and ranking the critical factors of graduation status.

This study focuses on developing a novel hybrid data analytic approach to predict the degree completion of undergraduate students at a four-year public university in the USA. Via the deployment of the proposed methodology, the data were analyzed using three popular data mining classifications methods (i.e. decision trees, artificial neural networks, and support vector machines) to develop predictive degree completion models. Finally, a sensitivity analysis is performed to identify the relative importance of each predictor factor driving the graduation.

The sensitivity analysis of the most critical factors in predicting graduation rates is determined to be fall-term grade-point average, housing status (on campus or commuter), and which high school the student attended. The least influential factors of graduation status are ethnicity, whether or not a student had work study, and whether or not a student applied for financial aid. All three data analytic models yielded high accuracies ranging from 71.56 to 77.61 percent, which validates the proposed model.

This study presents uniqueness in that it presents an unbiased means of determining the driving factors of college graduation status with a flexible and powerful hybrid methodology to be implemented at other similar decision-making settings.

As the modern manufacturing twining seamlessly with logistics operations for value adding services, logistics service is becoming more and more significant. Under this research background, the purpose of this paper is to introduce an innovative evaluation model for customer satisfaction using fuzzy analytic hierarchy process (FAHP).

This model uses triangular fuzzy concept to determine the weight of each index so that subjective or objective weighting is addressed. A case study from two large express companies in China is used to demonstrate the feasibility and practicality of the proposed model for examining customer satisfaction.

One of the key findings is that Company B has higher customer satisfaction than Company A due to its quick response and flexible logistics strategy. This paper has several contributions. First, A FAHP-based customer satisfaction evaluation model is proposed for the logistics service. Second, the triangular fuzzy concept is introduced to determine the weight of each index so as to addresses the limitation of subjective or objective weighting method. Third, a case study demonstrates the implementation of the model.

First, this paper considers the fuzzy AHP for the customer satisfaction evaluation. Comparing with other multi-criteria decision-making methods like data envelopment analysis, evidential reasoning approach, and multi-attribute value theory will be carried out in the near future. Second, the manufacturing modes like make-to-order, make-to-stock, and mass-customized production may have different logistics support so that the final products may reach the final targets quickly. How to evaluate various mode-based logistics and their customer satisfactions have great significance. Finally, Big Data-enabled customer satisfaction evaluation approaches may be a possible solution.

Based on the data from questionnaire, it is found that, in practical applications, manufacturing enterprises should amend the index system according to the specific business scope and the production characteristics. Manufacturing enterprises need to collect large amounts of data through market research and conduct the measurement on the related coefficient between the measurement indicators and customer satisfaction degree. After that, they can make sorting and filtering on the measurement index according to the measurement results.

Customer satisfaction is very important to manufacturing and logistics enterprises due to its time constraints. The physical products with services like logistics are paid close attention to by the final customers.

The contribution of this paper is as follows: a FAHP-based customer satisfaction evaluation model is proposed for the logistics service; triangular fuzzy concept is introduced to determine the weight of each index so as to addresses the limitation of subjective or objective weighting method; a case study was used to demonstrate the implementation of the model. One of the key findings is that Company B has higher customer satisfaction than Company B due to its quick response and flexible logistics strategy.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

The purpose of this paper is to assess the satisfaction of teenagers who are suffering from or are exposed to social damages of children and young adults’ publications in Iran.

A descriptive surveys approach with practical purposes is applied here. The tools used in this study include two researcher-made questionnaires. Two sets of participants constitute the statistical populations: 120 and 50 teenagers who were affected by or are at the risk of social damages. Data collection from the first set was through census, while the same from the second set is through the disproportionate stratified random sampling method. Another statistical population is the group of premier children publishers during 2006-2016.

The teenagers’ satisfaction mainly is involved with: perceived quality, expectations and perceived value. “Music” and “Recreational and performing arts”, internet-based resources, “Electronic materials” and “Real stories” are ranked as having the highest mean value in information needs, formats and literacy genre among teenagers, respectively. The findings here indicate that the teenagers participated are satisfied with children publications to a great extent.

This is the first research which used the CSI Model for assessing the satisfaction of teenagers at risk and vulnerable to social damages.