Customer Attributes Research Articles

Experiencing P2P accommodations: Anecdotes from Chinese customers

This study explores the key dimensions and attributes of Chinese customers’ experiences with peer-to-peer (P2P) accommodations through Airbnb in China. Thirty-four in-depth interviews were conducted with Airbnb customers. Results suggest that seven dimensions underpin the customer experience with P2P accommodations: physical utility, sensorial experience, core service, guest-host relationship, sense of security, social interaction, and local touch. This indicates that unlike traditional commercial hotels, P2P accommodations appeal to customers to a larger extent for their social and cultural characteristics. Implications are provided for stakeholders, including P2P accommodation practitioners and users, to co-create experiential value.

Boundary conditions of the effect of customer coproduction: the case of service failure

ABSTRACTResearch on the relationship between customer coproduction and satisfaction has produced mixed results even after accounting for the role of attribution. This paper extends prior research on boundary conditions and examines the role of customer participation readiness (PR) during encounters that end with service failure. Based on congruence and zone-of-tolerance theories, the two empirical studies suggest that (a) increasing levels of coproduction combined with increasing levels of PR escalates dissatisfaction and weakens attitudinal loyalty; (b) psychological reactance and expectancy disconfirmation mediate these relationships; (c) increasing levels of coproduction combined with increasing levels of external attribution reduce dissatisfaction and mitigate the adverse effect on attitudinal loyalty; and (d) PR interacts with customer coproduction to influence how customers make attributions. Future research should consider the role of PR in service failures. Where feasible, managers should gather and utilise individual-level PR data in order to predict customer attributions and dissatisfaction.

Predicting overall customer satisfaction: Big data evidence from hotel online textual reviews

Customer online reviews of hotels have significant business value in the e-commerce and big data era. Online textual reviews have an open-structured form, and the technical side, namely the linguistic attributes of online textual reviews, is still largely under-explored. Using a sample of 127,629 reviews from tripadvisor.com, this study predicts overall customer satisfaction using the technical attributes of online textual reviews and customers’ involvement in the review community. We find that a higher level of subjectivity and readability and a longer length of textual review lead to lower overall customer satisfaction, and a higher level of diversity and sentiment polarity of textual review leads to higher overall customer satisfaction. We also find that customers’ review involvement positively influences their overall satisfaction. We provide implications for hoteliers to better understand customer online review behavior and implement efficient online review management actions to use electronic word of mouth and enhance hotels’ performance.

Customer experiences and return patronage in airport hotels: Evidence from OR Tambo International Airport, South Africa

Orientation: Although OR Tambo International Airport is the busiest airport in Africa, hotel occupancies and revenue per available room at or near the airport have been lower than the industry average.Research purpose: The aim of this research was to gain an improved understanding of customer experiences and return intentions in hotels at or near OR Tambo International Airport.Motivation for the study: The research was pursued to determine hotel attributes that are important for customer experiences and return intentions in hotels at or near OR Tambo International Airport. To determine the difference in domestic visitors’ experiences and return intentions in hotels and to determine a difference in customers’ experiences and return intentions in the respective hotels.Research design, approach and method: This study was mainly quantitative with some qualitative elements. A meeting was held with five hoteliers and airport management (qualitative), and 400 return guests of hotels at or near OR Tambo International Airport successfully completed questionnaires (quantitative).Main findings: The findings showed that ‘reliability’, ‘empathy’ and ‘assurance’ are important attributes contributing to customers’ experiences in hotels at or near airports. Reliability and overall hotel experience were regarded as important attributes for hotel customers’ decision to return to hotels in airports.Practical/managerial implications: To improve customer experiences, hoteliers should emphasise more the following attributes: ‘reliability’, ‘empathy’ and ‘assurance’.Contribution/value-add: To the best of the researchers’ knowledge, this study is a first attempt to determine customer experiences and return patronage in hotels at or near airports in South Africa. The results could help airport hotels to gain a competitive advantage over other hotel categories.

Motivations of Selecting Restaurants for Eating-out: Focus on Fashion Premium Outlets

Purpose - This research examined the effect of hedonic and utilitarian eating-out motivations on the evaluation of restaurant selection attributes in a fashion premium outlet. Additionally, the influence of experimental and functional attributes on customers' preference for hedonic eating-out motives and utilitarian eating-out motives, and variation of moderating effects through the gender was examined. Research design, data, and methodology - A survey was conducted to verify the established research hypothesis. The questionnaire items for the research were modified to fit the situation of the present study. In order to elaborate the questionnaire, the literature of the previous researchers was reviewed and supplemented. The survey conducted 207 online questionnaires for consumers who have visited domestic fashion premium outlets from July 4, 2017 to July 27, 2017. A total of 207 questionnaires were collected, and a total of 206 questionnaires were used for the empirical analysis after excluding one inappropriate response. In order to verify the reliability and validity of the measured variables, exploratory factor analysis and reliability analysis were performed using SPSS 20.0. Next, the structural equation model (SEM) statistical method was used to test the hypotheses of the study. Results - Hedonic motivation had more influence on experimental attribute importance than the functional attribute importance of the restaurant. However, this result was different depending on the gender. The effects of hedonic motives on empirical attributes were more influenced by female groups, and when influencing functional attributes, male groups were more influenced. However, it was statistically significant (p <0.05) in the female group only when the hedonic eating out motives influenced empirical attributes. Conclusions - This study analyzed the effect of eating-out motivation on the restaurant preference attributes and suggested practical implications. First, customers with hedonic eating-out motivations were evaluating experiential attributes to be more important than functional attributes. Second, for customers who are motivated to use practical eating-out habits, companies should provide services that meet practical and economic needs. In particular, female customers visiting restaurants need differentiated marketing strategies that make them feel new experiences rather than practical ones. In addition, it is necessary to study more complex and integrated studies which will influence restaurant selection attributes of premium outlets customers by adding various eating out motives and selection attributes.

AN EFFICIENT FUZZY COMPUTATIONAL FRAMEWORK FOR CUSTOMER SEGMENTATION MODEL IN CREDIT ANALYSIS

In this paper we propose an efficient fuzzy computational framework for customer segmentation model in credit analysis. Normally segmentation methods cannot perform complex analysis so as to obtain the customer segments with high value. If the knowledge of experts in the data domain can be imparted to the generation of segments, this can bring in better results in the performance of classification models. In our approach customer attributes are selected after knowledge experts analysis and are segmented based on the limits set by them on the real numerical values. For each segment we generate the segmentation rules with definition of fuzzy basic linguistic term set. Each linguistic term set is assigned to a fuzzy membership function to generate the segmentation function. Combining the segmentation rules and generated functions the real valued numerical attributes are converted to fuzzified values in the interval [0, 1]. Both linguistic and numeric information are aggregated by a series of computations and a 2-tuple linguistic value is generated for each attribute in the database. The same term after a series of computations can be used in many decision making problems as it suffers no loss of information.

Validation of demand pull strategies for accomplishing sustainable development in manufacturing organizations through structural equation modeling

PurposeThe purpose of this paper is to investigate as well as select various significant demand pull (DP) strategies affecting sustainable development (SD) in manufacturing organizations. The study deploys the structural equation modeling (SEM) technique to empirically validate the interrelationships between significant DP strategies and SD indicators in an SEM-DP Model.Design/methodology/approachConfirmatory factor analysis approach is applied to generate an effective SEM-DP model using the AMOS 21 (Analysis of Moment Structures) software. The data have been collected from different manufacturing organizations practicing DP strategies, using a well-framed DP questionnaire for the evolution of the SEM-DP model.FindingsSEM of various DP strategies like stringent implementation of government regulations (SIGR), transforming capabilities, unionized labor (UL), and customer attributes (CA) toward achieving SD in manufacturing industries has been performed. The SEM-DP model has been planned and reports obtained before and after modification indices of the model are correlated, which further establishes improvements in the model’s effectiveness. The research concludes that significant DP strategies, namely, SIGR, UL, and CA support the manufacturing industries in accomplishing SD in terms of competitiveness, business performance enhancements, flexibility, customer satisfaction, and technological development.Research limitations/implicationsIn the present study, contributions of DP practices are determined to accomplish SD in Indian manufacturing organizations only. Hence, the results obtained may need some modifications before applying to other countries. Moreover, issue-wise independent modeling can also be performed to assess the importance of DP practices under specific orientations.Social implicationsThe results of various interrelationships among DP practices and SD indicators in the SEM-DP model portray the effectiveness of DP practices for achieving organizational goals and social commitments.Originality/valueThe outcomes of the study will help DP practitioners, organizational managers, and HR executives in the manufacturing industries to develop a clear understanding about the significant DP strategies to be followed holistically for accomplishing SD. The manufacturing enterprises will be able to frame and organize their policies, handle their UL issues and CA in a more appropriate way. Hence, the knowledge obtained from present study will help improve the overall performance of manufacturing industries involved in the present context.

A Network‐Based Approach to Modeling and Predicting Product Coconsideration Relations

Understanding customer preferences in consideration decisions is critical to choice modeling in engineering design. While existing literature has shown that the exogenous effects (e.g., product and customer attributes) are deciding factors in customers’ consideration decisions, it is not clear how the endogenous effects (e.g., the intercompetition among products) would influence such decisions. This paper presents a network‐based approach based on Exponential Random Graph Models to study customers’ consideration behaviors according to engineering design. Our proposed approach is capable of modeling the endogenous effects among products through various network structures (e.g., stars and triangles) besides the exogenous effects and predicting whether two products would be conisdered together. To assess the proposed model, we compare it against the dyadic network model that only considers exogenous effects. Using buyer survey data from the China automarket in 2013 and 2014, we evaluate the goodness of fit and the predictive power of the two models. The results show that our model has a better fit and predictive accuracy than the dyadic network model. This underscores the importance of the endogenous effects on customers’ consideration decisions. The insights gained from this research help explain how endogenous effects interact with exogeous effects in affecting customers’ decision‐making.

Uticaj marketing odnosa sa kupcima na finansijske performanse proizvodjača suncokretovog ulja u Srbiji

The purpose of this study is to investigate empirically how determinant attributes of price and brand, based on relationship marketing concept, influence customer perceived value as well as long-term proft in the Serbian sunflower oil industry. For the purpose of this study, a self-administrated questionnaire was distributed to 125 respondents. This questionnaire has been created to collect primary data and it has a structure that reflects statements which show customers’ satisfaction determinant attributes. A descriptive and multiple regression analysis was used to identify which factors of determinant attributes of customers’ satisfaction influenced customer-perceived value. Therefore, we perceive the influence of satisfaction and its attributes as nonfinancial measures on financial measures which are related to enterprise performance. This study found that factors of determinant attributes of price and brand significantly and statistically influenced the satisfaction, whereas the manufacturer’s brand has the greatest influence. Furthermore, analyzing the loyalty of customers as a second key factor of relationship marketing, the results of this study undoubtedly confirmed that only the brand significantly influenced customers’ loyalty. The price represents a significant factor in providing immediate customers’ satisfaction. But, in the long run, the focus of customers is on those oil manufacturers who have a powerful brand (corporate reputation). The study opens possibilities of discussion in the existing literature due to the fact that it reveals how determinant attributes of customers’ satisfaction and loyalty influences the creation of superior value for the customer as well as the creation of long-term value for the enterprise. This study found that the process of creation enterprise’s value was not based on a traditional formula, where it began with input and ended with the output. The mere input for the creation of enterprise’s output emerges from interaction with customers. In this context, it’s very important to take into account the economic effect between the consistent creation of superior value for the customer and long-term profit. Moreover, the importance of this study emerges from research results which show that modern market conditions impose the need for tailoring financial statements that are based on various interest groups. In this way, additional non-financial measures of success are provided as well as information in relation to those that can be obtained from existing basic financial statements.

Multi-attribute intelligent queueing method for onboard call centers

The present research proposes a multi-attribute onboard call center intelligent queueing method applicable to the vehicle service field. In the previous studies concerning the call center queueing methods, the characteristics associated with the incoming call customers include the impatient waiting and the notification of waiting time, etc; however, the present paper takes into consideration a suite of additional customer attributes, i.e., emergency calls from intelligent onboard terminals, whether the call requests are from the area covered by the company’s 4S service, whether the call waiting time stays within a reasonable range, and whether there exists an necessity for engaging the third party service system. Also, priority classification is conducted based on the multiple attributes associated with the aforementioned customers. Besides, to optimize the service structure of the agent service representatives, the present study divides the agent service representatives into two groups, namely professional skill group and regular skill group. This allows the processing of call business to be carried out in two different modes. Correspondingly, preset processing time tables for the call business are established for regular and professional skill groups, respectively, so as to enable the service of agent representatives to better distinguish between regular business and complicated business, and conduct suitable processing accordingly. As a result, the processing efficiency is reinforced, the customers’ waiting time is reduced, and consequently the total processing time of agent service representatives is minimized. The research methodology adopted in the present manuscript is highly applicable to the design and management of call centers.

Application of House of Quality in assessment of seawater pretreatment technologies

Increasing pressure on water resources is leading to a growing reliance on seawater desalination for the provision of freshwater. Pretreatment of seawater is a major step in seawater desalination, which can mitigate problems with reverse osmosis (RO) membranes. No clearly superior pretreatment technology exists, due to the variation in input water quality, and furthermore, the pretreatment must be designed to be functional with input water quality characteristics that vary considerably over time.The objective of this work is to conduct technology assessment by applying a lissome version of HOQ matrix developed in a case study of dissolved air flotation (DAF) system, which is used as water pretreatment for RO desalination systems. The DAF system technology is compared with other pretreatment processes such as ultrafiltration, sedimentation, and granular media filtration. General analysis provides insight to decision makers to help them improve technologies so that they could meet specific demands. HOQ offers a comparative analysis and identifies specific characteristics that must be improved to develop the chosen technology.The HOQ provides a comprehensive analysis for decision making in management, showing that no pretreatment technologies present a superior performance vis-a-vis all customer attributes, however, it identifies the features of the system, to which efforts should be funneled. For DAF system, these are: water quality changes and energy consumption.

A Study on the Effects of Switch Intention on Customer Attribution: the Case of the Galaxy, Xiaomi and Brand Type

A Study on the Effects of Switch Intention on Customer Attribution: the Case of the Galaxy, Xiaomi and Brand Type

A Dynamic Clustering Approach to Data-Driven Assortment Personalization

We consider an online retailer facing heterogeneous customers with initially unknown product preferences. Customers are characterized by a diverse set of demographic and transactional attributes. The retailer can personalize the customers' assortment offerings based on available profile information to maximize cumulative revenue. To that end, the retailer must estimate customer preferences by observing transaction data. This, however, may require a considerable amount of data and time given the broad range of customer profiles and large number of products available. At the same time, the retailer can aggregate (pool) purchasing information among customers with similar product preferences to expedite the learning process. We propose a dynamic clustering policy that estimates customer preferences by adaptively adjusting customer segments (clusters of customers with similar preferences) as more transaction information becomes available. We test the proposed approach with a case study based on a dataset from a large Chilean retailer. The case study suggests that the benefits of the dynamic clustering policy under the MNL model can be substantial and result (on average) in more than 37% additional transactions compared to a data-intensive policy that treats customers independently and in more than 27% additional transactions compared to a linear-utility policy that assumes that product mean utilities are linear functions of available customer attributes. We support the insights derived from the numerical experiments by analytically characterizing settings in which pooling transaction information is beneficial for the retailer, in a simplified version of the problem. We also show that there are diminishing marginal returns to pooling information from an increasing number of customers.

(Re-)Design of a Demonstration Model for a Flexible and Decentralized Cyber-Physical Production System (CPPS)

Cyber-physical production systems (CPPS) enable completely new possibilities in the factory of the future through the connectivity between the digital world and the physical production system. It has so far been difficult to transfer these advantages and concepts to students as well as professionals in a clear and practical oriented manner. The Fraunhofer Italia team has built a demonstration model for a flexible and decentralized CPPS system for showcase purposes. As part of an improvement of the existing system, Axiomatic Design (AD) was applied as the scientific design theory. Starting from the identification of the Customer Attributes, Functional Requirements were derived and the Design Parameters for the new design were defined by means of the AD decomposition and mapping process. In this paper, the application of AD for product improvement is described step-by-step based on the example of the redesign of the CPPS demonstration model.

A paradigm for customer-driven product design approach using extended axiomatic design

In the present times, due to increase in customer demands, products complexity is on the rise. This calls for the designers to strike a balance between a wide range of design alternatives and a large set of conflicting criteria. Hence, to take a sound decision by identifying a viable combination of customer requirements and satisfy the conflicting requirements is a difficult task for both the designer and the manufacturer. This work extends the axiomatic design theory to align the customer requirements (CRs) and design parameters (DPs) and generates multiple possible design alternatives based on the weightages of analytic hierarchy process (AHP). Such design alternatives are evaluated on the basis of their overall performance in line with the expected customer attributes, and the best design is identified by integrating the technique for order of preference by similarity to ideal solution, a ranking multi-criteria decision-making method, with AHP. This work unfolds a support tool for decision makers to accurately and effectively select CRs by a useful aggregation of function requirements and DPs. An industrial example is produced to demonstrate the applicability of the proposed method. This intelligent decision-making method is useful from the customers as well as the manufacturers’ perspective.

Attribution of success and failure in intercultural service encounters: the moderating role of personal cultural orientations

PurposeThis paper aims to examine the role that personal cultural orientations play in customer attributions in intercultural service encounters.Design/methodology/approachA conceptual model was developed depicting the relationships between service delivery outcome, personal cultural orientations and customer attributions. Data were collected from 640 Chinese and Western customers using scenario-based experiments in a restaurant context to assess the hypothesized relationships in the model.FindingsThe findings show that compared to service delivery success, customers tend to hold service employee and firm responsible for service delivery failure rather than themselves and cultural differences. Moreover, personal cultural orientations partially moderated the influence of the service delivery outcome on customer attributions.Research limitations/implicationsFuture research could adopt different methodologies such as critical incident techniques and surveys to replicate the study.Practical implicationsService firms are recommended to design programs to influence customer attributions such as “customer education programs” and “customer appreciation programs” to achieve high customer satisfaction.Originality/valueThis study examines the differences in customer attributions between successful vs unsuccessful service delivery. It also sheds light on the potential moderating role of personal cultural orientations on the relationship between service delivery outcome and customer attributions.

Predicting time-to-churn of prepaid mobile telephone customers using social network analysis

Mobile phone carriers in a saturated market must focus on customer retention to maintain profitability. This study investigates the incorporation of social network information into churn prediction models to improve accuracy, timeliness, and profitability. Traditional models are built using customer attributes, however these data are often incomplete for prepaid customers. Alternatively, call record graphs that are current and complete for all customers can be analysed. A procedure was developed to build the call graph and extract relevant features from it to be used in classification models. The scalability and applicability of this technique are demonstrated on a telecommunications data set containing 1.4 million customers and over 30 million calls each month. The models are evaluated based on ROC plots, lift curves, and expected profitability. The results show how using network features can improve performance over local features while retaining high interpretability and usability.

Customers’ Satisfaction and Loyalty with Motivations to Dine Out and Selected Attributes in Korean Traditional Restaurant

Purpose - This research analyzes the impact of motivations to dine out and selected attributes of customers on customer satisfaction and loyalty....

서비스품질지수를 고려한 품질기능전개를 통한 철도 서비스 품질 개선에 관한 연구

Purpose: This study was to identify customers’ demands in railway services system and then to seek the way to satisfy the customer expectations. Methods: We suggest a Quality Function Deployment(QFD)-based approach comprised of three stages. In first stage, SERVPERF survey was carried out to assess current positions of customer expectations in the market. Then, factor analysis was incorporated into SERVPERF to classify customer expectations for the house of quality. In the second stage, the analytic network process was used to prioritize the importance of the customer attributes. Finally, QFD was performed utilizing customer attributes and their weights. Results: The QFD identified the most important customer expectations as: accident prevention, swift reaction to accident, on-time arrivals and departures of the train. It also shows that driving capability, equipment for safety, and training for disaster are the most critical technical requirements. Conclusion: The results are useful for identifying the customers’ demands in railway services systems, and they can contribute to the service quality and customer satisfaction. Key words: Railway services system, SERVPERF, QFD, customer demand, design target

Eco-friendly Retail Product Attributes, Customer Attributes and the Repurchase Intentions of South African Consumers

Purpose –Consumers are becoming progressively aware of the significance of eco-friendly activities, and their environmental consciousness drives them to consume eco-friendly products and services more, and prefer to support organisations that favor conservational practices. Increased environmental impacts bring a rise in concerns locally and globally, on sustainability issues aimed at reducing non-conservative consumption patterns. Retail organisations are increasingly developing and marketing eco-friendly retail products to promote sustainable, eco-friendly consumption patterns. The purpose of this paper is to examine the relationships between eco-friendly retail product attributes and South African consumers’ eco-friendly product repurchase intentions.Design/methodology/approach – A survey approach was employed involving 241 consumers in Southern Gauteng. Confirmatory factor analysis and structural equation analysis were conducted respectively to test the reliability and validity of the scales as well as the proposed hypotheses.Research findings– Retail product consumers’ response to the attributes of eco-friendly products is still important in predicting their eco-friendly product repurchase intentions. South African consumers’ eco-friendly attributes moderate their environmental sensitivity, hence their repurchase intentions.Practical implications – It is not enough to retailers and wholesalers to promote sustainable and recyclable packaging materials. They must also improve eco-friendly product-related attributes to satisfy consumer needs better, and should be cautious in the direct and moderating effect of eco-friendly customer-related attributes in devising marketing plans to promote eco-friendly products.Originality/value – The paper contributes to research by advancing understanding of how consumers make their repurchase intentions of eco-friendly retail products, providing retailers with managerial insights. The paper also presents limitations and directions for future research.