A rule-based method for identifying the factor structure in customer satisfaction

Ahmad, Amir; Dey, Lipika; Halawani, Sami M.

doi:10.1016/j.ins.2012.02.056

Cited by 17 publications

(29 citation statements)

References 36 publications

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“…The flexibility and currency of SaaS products lead to an ever increasing demand, with spending on SaaS offerings forecasted to grow to $ 258 billion by 2020 (Forrester, 2011) [2]. However, limited customization capability and ample competing products across venders mean that retaining clients is not an easy task (Ahmad and Dey, 2012) [3]. Venders must frequently upgrade SaaS services and demonstrate sufficient information security and privacy safeguards (Levina and Ross, 2003) [4].…”

Section: Introductionmentioning

confidence: 99%

Does Perceived Value Mediate the Relationship between Service Traits and Client Satisfaction in the Software-as-a-Service (SaaS)?

Liu¹,

Chang²,

Tsai³

2015

JSS

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The Software-as-a-Service (SaaS) product model provides clients to outsource software application processing requirements. Customer satisfaction is an important problem facing SaaS firms. Marketing theory suggests that customer satisfaction is affected by perceived value, but this relationship has not been examined in the SaaS context. This study argues for the mediating role of perceived value in the relationship between traits of the SaaS product and customer satisfaction. Our model builds on prior studies and success theories to consider whether service quality, service response, security, and information quality are mediated in their relationship to customer satisfaction. This study posits that perceived value serves as the primary mediator and trust as a controlling mediator in a model tested using a survey of ASP client firms.

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Section: Introductionmentioning

confidence: 99%

Does Perceived Value Mediate the Relationship between Service Traits and Client Satisfaction in the Software-as-a-Service (SaaS)?

Liu¹,

Chang²,

Tsai³

2015

JSS

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“…Many existing approaches [3,15,[21][22][23] are based on the datasets in which customers provide their estimates on the level of satisfaction with regard to each attribute in a given scale and evaluate total satisfaction from the product. Accordingly, several methods [3,15,[21][22][23] have been proposed to employ this type of data to predict the category of attributes. It is easy to collect these datasets as customers describe their experience about the product attributes, and these values are likely to be more accurate as they are based on real experience.…”

Section: Introductionmentioning

confidence: 99%

“…e proposed method is based on a probabilistic approach used to identify the relationship between the attribute-level performance and the total-level customer satisfaction. en, the rules defined by Ahmad et al [22] are applied to specify the category of an attribute. e proposed method does not imply any assumption on the underlying statistical distribution; therefore, it allows avoiding misspecification of a model.…”

Section: Introductionmentioning

confidence: 99%

“…e proposed method does not imply any assumption on the underlying statistical distribution; therefore, it allows avoiding misspecification of a model. e Kano model suggests the five categories, whereas later studies [3,15,[22][23][24] focus on the three-factor theory (basic (must-be), performance (one-dimensional), and excitement (attractive)). Generally, existing methods classify an attribute into one of these three categories or into a random (indifferent) category.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Approach to Identify the Categories of Attributes for the Three-Factor Structure in Customer Satisfaction

Ahmad

Barukab

2020

Complexity

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Evaluation of customer satisfaction is an important area of marketing research in which products are defined by attributes that can be grouped into different categories depending on their contribution to customer satisfaction. It is important to identify the category of an attribute so that it can be prioritized by a manager. The Kano model is a well-known method to perform this task for an individual customer. However, it requires filling in a form, which is a difficult and time-consuming exercise. Many existing methods require less effort from the customer side to perform data collection and can be used for a group of customers; however, they are not applicable to individuals. In the present study, we develop a data-analytic method that also uses the dataset; however, it can identify the attribute category for an individual customer. The proposed method is based on the probabilistic approach to analyze changes in the customer satisfaction corresponding to variations in attribute values. We employ this information to reveal the relationship between an attribute and the level of customer satisfaction, which, in turn, allows identifying the attribute category. We considered the synthetic and real housing datasets to test the efficiency of the proposed approach. The method correctly categorizes the attributes for both datasets. We also compare the result with the existing method to show the superiority of the proposed method. The results also suggest that the proposed method can accurately capture the behavior of individual customers.

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“…Clustering has many applications in various domains, including text extraction/summarization [7,8], market segmentation [9], web access pattern analysis [10], and product recommendation [11]. However, the identity-clustering cannot be applicable to all clustering domains.…”

Section: Introductionmentioning

confidence: 99%

New metrics for clustering of identical products over imperfect data

Yetgin¹,

Gözükara²

2015

Turk J Elec Eng & Comp Sci

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This paper introduces the concept of product identity-clustering based on new similarity metrics and new performance metrics for web-crawled products. Product identity-clustering is defined here as the clustering of identical products, e.g., for price comparison purposes. Products blindly crawled over web sources, e.g., online marketplaces, have different description formats, where the features describing the same products differ in both number and representation formats. This problem causes imperfect feature vectors, where the vectors are considered to be not uniform in length and structure, with the features of various data types (numeric, categorical), and unknown vector structures. Furthermore, the product information usually contains redundant, missing, or faulty data, which are regarded as noise here. Product identity-clustering becomes a challenge when the vectors' metadata are previously unknown and the imperfect nature of the feature vectors is considered with the occurrence of noise.In this paper, the product identity-clustering concept is introduced as a new mining metric in e-commerce. Then novel similarity metrics are introduced to improve the product identity-clustering performance of legacy metrics. Finally, novel performance metrics are proposed to measure the performance of the identity-clustering algorithms. Using these metrics, a comparison of the legacy-based similarity metrics (Euclidian, cosine, etc.) and the proposed similarity metrics is given. The results show that legacy metrics are not successful in discriminating identical web-crawled products and the proposed metrics enable better achievement in the product identity-clustering problem.

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A rule-based method for identifying the factor structure in customer satisfaction

Cited by 17 publications

References 36 publications

Does Perceived Value Mediate the Relationship between Service Traits and Client Satisfaction in the Software-as-a-Service (SaaS)?

Does Perceived Value Mediate the Relationship between Service Traits and Client Satisfaction in the Software-as-a-Service (SaaS)?

A Novel Approach to Identify the Categories of Attributes for the Three-Factor Structure in Customer Satisfaction

New metrics for clustering of identical products over imperfect data

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