Maximizing customer satisfaction through an online recommendation system: A novel associative classification model

Jiang, Yuanchun; Shang, Jennifer; Liu, Yezheng

doi:10.1016/j.dss.2009.06.006

Cited by 118 publications

(49 citation statements)

References 31 publications

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“…Second, the present study emphasizes the complementary role of trust and satisfaction in influencing consumer behaviour such as recommending service providers; these are two key factor that affect loyalty and long term relationship (Kim et al, 2009;Sahin et al, 2011). Third, unlike previous studies that have emphasized the role of satisfaction and trust in developing strong client recommendations of service providers (File & Prince, 1992;Jiang, Shang, & Liu, 2010;Brown et al, 2005;Lerrthaitrakul & Panjakajornsak, 2014), the present study, has validated that, in financial service context, quality information provision is also an important determinant of customer recommendation of service providers.…”

Section: Theoretical Contributionmentioning

confidence: 64%

“…Relatively few past studies have examined customer recommendation from different research contexts mobile services (Chen, Huang, & Chou, 2008), internet (Lerrthaitrakul & Panjakajornsak, 2014), auto insurance (Berger, 1988), online social media (Brown, Broderik, & Lee, 2007), retail service (Brown, Barry, Dacin, & Gunst, 2005), very little research has been done in consumer recommendations in accounting and financial services context (e.g., Cengiz & Yayla, 2007;File & Prince, 1992;Money, 2000;Choudhury, 2011;Shirsavar et al, 2012). Moreover, while previous studies have emphasized the role of satisfaction and trust in developing strong client recommendations of service providers (File & Prince, 1992;Jiang, Shang, & Liu, 2010;Brown et al, 2005;Lerrthaitrakul & Panjakajornsak, 2014), there is void in the literature regarding the role of information quality provision in influencing trust and recommendation behaviour directly. This study attempts to contribute to filling these gaps in the context of loan/credit services from a developing country perspective.…”

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confidence: 98%

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Getting Loan Clients to Recommend Financial Service Providers: The Role of Satisfaction, Trust and Information Quality

Mbawuni

Nimako²

2014

AFR

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This paper examines the role of satisfaction, trust and information quality as determinants of loan clients' recommendation of financial service providers in a developing country. Drawing on existing literature, a conceptual model was developed and validated with data from loan clients in Ghana. The study involved a cross-sectional survey of 371 loan customers of leading financial service providers in Ghana. The results show that satisfaction, trust and loan information quality are significant factors influencing clients' recommendation of loan products. Moreover, satisfaction and information quality also contributed significantly to influencing clients' trust for financial service providers. The findings provide important implications for inducing loan clients' recommendation of financial services to others. While this study is limited in terms of generalizability of the findings in developing countries, it provides avenues for further research for modelling the determinants of loan acquisition intentions of clients in financial markets in other research settings.

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Section: Theoretical Contributionmentioning

confidence: 64%

mentioning

confidence: 98%

Getting Loan Clients to Recommend Financial Service Providers: The Role of Satisfaction, Trust and Information Quality

Mbawuni

Nimako²

2014

AFR

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“…These factors may alienate customers and impede the sustainable development of e-business. As an information technology to resolve the above problems [3,4], the recommendation system is widely applied by e-commerce practitioners and has become an important research topic in information science and decision-support systems [5,6]. Currently, the research on recommendation systems generally includes content-based filtering (CBF) [7], collaborative filtering (CF) [8,9], and other data-mining techniques [10], such as decision trees [11,12], association rules [13], and the semantic approach [14].…”

Section: Introductionmentioning

confidence: 99%

Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business

Guo

Yin²,

Li³

et al. 2018

Sustainability

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A lack of in-depth excavation of user and resources information has become the main bottleneck restricting the predictive analytics of recommendation systems in mobile commerce. This article provides a method which makes use of multi-source information to analyze consumers' requirements for e-commerce recommendation systems. Combined with the characteristics of mobile e-commerce, this method employs an improved radial basis function (RBF) network in order to determine the weights of recommendations, and an improved Dempster-Shafer theory to fuse the multi-source information. Power-spectrum estimation is then used to handle the fusion results and allow decision-making. The experimental results illustrate that the traditional method is inferior to the proposed approach in terms of recommendation accuracy, simplicity, coverage rate and recall rate. These achievements can further improve recommendation systems, and promote the sustainable development of e-business.

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“…Castillo et al, 2008;Loh, Lorenzi, Saldana, & Licthnow, 2004;Ricci & Nguyen, 2007;Wallace, Maglogiannis, Karpouzis, Kormentzas, & Kollias, 2003), often based on artificial intelligence techniques, as was pre dicted in the early nineties by Crouch (1991) Lenar & Sobecki, 2007;Ngai & Wat, 2003), Bayesian networks (e.g. Huang & Bian, 2009;Jiang, Shang, & Liu, 2009), to cite just a few.…”

Section: Introductionmentioning

confidence: 99%

Sem-Fit: A semantic based expert system to provide recommendations in the tourism domain

García-Crespo

López-Cuadrado

Colomo‐Palacios

et al. 2011

Expert Systems with Applications

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a b s t r a c tThe hotel industry is one of the leading stakeholders in the tourism sector. In order to reduce the trav eler's cost of seeking accommodations, enforce the return ratio efficiency of guest rooms and enhance total operating performance, evaluating and selecting a suitable hotel location has become one of the most critical issues for the hotel industry. In this scenario, recommender services are increasingly emerg ing which employ intelligent agents and artificial intelligence to ''cut through'' unlimited information and obtain personalized solutions. Taking this assumption into account, this paper presents Sem Fit, a seman tic hotel recommendation expert system, based on the consumer's experience about recommendation provided by the system. Sem Fit uses the consumer's experience point of view in order to apply fuzzy logic techniques to relating customer and hotel characteristics, represented by means of domain ontolo gies and affect grids. After receiving a recommendation, the customer provides a valuation about the rec ommendation generated by the system. Based on these valuations, the rules of the system are updated in order to adjust the new recommendations to past user experiences. To test the validity of Sem Fit, the validation accomplished includes the interaction of the customer with the system and then the results are compared with the expert recommendation for each customer profile. Moreover, the values of preci sion and recall and F1 have been calculated, based on three points of view, to measure the degree of rel evance of the recommendations of the fuzzy system, showing that the system recommendations are on the same level as an expert in the domain.

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Maximizing customer satisfaction through an online recommendation system: A novel associative classification model

Cited by 118 publications

References 31 publications

Getting Loan Clients to Recommend Financial Service Providers: The Role of Satisfaction, Trust and Information Quality

Getting Loan Clients to Recommend Financial Service Providers: The Role of Satisfaction, Trust and Information Quality

Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business

Sem-Fit: A semantic based expert system to provide recommendations in the tourism domain

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