Deals with three issues in the area of perceived service quality. First, it compares the gap model with the performance model. Second, it investigates the direction of causality between service quality and satisfaction. Finally, it examines whether the influences of some dimensions of service quality vary across service industry types. Three service firms were selected and respondents were interviewed in each firm. As hypothesized, the performance model appeared to be superior to the gap model. In addition, the result shows that perceived service quality is an antecedent of satisfaction, rather than vice versa. Finally, tangibles appeared to be a more important factor in the facility/equipment-based industries, whereas responsiveness is a more important factor in the people-based industries. Managerial implications and future research directions are discussed.

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Purpose Open innovation communities are a growing trend across diverse industries because they provide opportunities of collaborating with customers and exploiting their knowledge effectively. Although open innovation communities can be strategic assets that can help firms innovate, firms nonetheless face the challenge of information overload incurred due to the characteristic of the community. The purpose of this paper is to mitigate the problem of information overload in an open innovation environment. Design/methodology/approach This study chose MyStarbucksIdea.com (MSI) as a target open innovation community in which customers share their ideas. The authors analyzed a large data set collected from MSI utilizing text mining techniques including TF-IDF and sentiment analysis, while considering both term and non-term features of the data set. Those features were used to develop classification models to calculate the adoption probability of each idea. Findings The results showed that term and non-term features play important roles in predicting the adoptability of ideas and the best classification accuracy was achieved by the hybrid classification models. In most cases, the precisions of classification models decreased as the number of recommendations increased, while the models’ recalls and F1s increased. Originality/value This research dealt with the problem of information overload in an open innovation context. A large amount of customer opinions from an innovation community were examined and a recommendation system to mitigate the problem was proposed. Using the proposed system, the firm can get recommendations for ideas that could be valuable for its business innovation in the idea generation phase, thereby resolving the information overload and enhancing the effectiveness of open innovation.

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Companies have been collecting innovative ideas that can help them to develop new products and services through co-creation with their customers. As more customers participate in suggesting ideas, companies are likely to acquire more valuable ones. At the same time, however, some fundamental problems occur such as managing and selecting useful ideas from a large number of collected ideas. Semantic web mining techniques allow us to manage a large number of customers' ideas effectively, extract meaningful information from the ideas, and provide useful information for idea selection. In order to cope with such problems and enhance the value of co-creation, we propose an ontology-based co-creation enhancing system (OnCES) developed using semantic web mining techniques. To this end, we 1) defined a cocreation idea ontology (CCIO) that includes common concepts related to customers' ideas from MyStarbucksIdea.com, their attributes, and relationships between them; 2) transformed the customers' ideas into semantic data in RDF format according to the CCIO; 3) conducted text mining to extract new knowledge from the ideas such as keywords, the number of positive words, the number of negative words, and the sentiment score; and 4) built prediction models using keywords and other features such as those about customer and idea in order to predict the adoptability of each idea. The results of text mining and prediction analysis were also added to the semantic data. We implemented the OnCES system, which provides useful services such as idea navigation, idea recommendation, semantic information retrieval, and idea clustering, utilizing the stored semantic data while saving the time and effort required to process a huge number of customers' ideas.

The government makes great efforts to maintain the soundness of policy funds raised by the national budget and lent to corporate. In general, previous research on the prediction of company insolvency has dealt with large and listed companies using financial information with conventional statistical techniques. However, small- and medium-sized enterprises (SMEs) do not have to undergo mandatory external audits, and the quality of accounting information is low due to weak internal control. To overcome this problem, we developed an insolvency prediction model for SMEs using data mining techniques and technological feasibility assessment information as non-financial information. We divided the dataset into two types of data based on three years of corporate age. The synthetic minority over-sampling technique (SMOTE) was used to solve the data imbalance that occurred at this time. Six insolvency prediction models were created using logistic regression, a decision tree, an artificial neural network, and an ensemble (i.e., boosting) of each algorithm. By applying a boosted decision tree, the best accuracies of 69.1% and 82.7% were derived, and by applying a decision tree, nine and seven influential factors affected the insolvency of SMEs established for fewer than three years and more than three years, respectively. In addition, we derived several insolvency rules for the two types of SMEs from the decision tree-based prediction model and proposed ways to enhance the health of loans given to potentially insolvent companies using these derived rules. The results of this study show that it is possible to predict SMEs’ insolvency using data mining techniques with technological feasibility assessment information and find meaningful rules related to insolvency.