With the advances of information technologies, the interest in SmartWork including extended version of telework and flexible work are increasing, and various types of SmartWork attempted to make working time and place flexible with the goal of work and life balance. Despite its emphasis on work and life balance, SmartWork is expected to make the boundaries between work and nonwork blur and role conflicts occur more than before, and thus the goal of work and life balance becomes more distant. A number of SmartWork users are significantly increasing in Korea, but little is known concerning the antecedents and mechanisms to explain psychological work and interferences in the SmartWork environment. In this paper, using boundary theory, we empirically investigate factors affecting the interferences at both work and nonwork domains. The results, based on data collected from SmartWork users in one of the biggest telecommunication companies in Korea where SmartWork is adopted and extensively used, suggest the factors may be affecting differently interferences at the work and nonwork domains.

Despite the growing amount of mobile peer-to-peer (P2P) payment applications available on mobile app stores, these applications are still in their infancy and have yet to see mass adoption. This study aims to explore the factors that influence the adoption of such mobile P2P payment applications by using a large-scale data set based on the tracking of users' actual mobile application usage behavior. Our main findings reveal that the duration of each session that users use of traditional bank application has a significant relationship with their adoption of mobile P2P payment applications. In addition, we explore the social aspect of such mobile P2P payment applications by analyzing their social network applications usage and found that the amount of social network service applications used and usage duration positively impacted one's adoption of mobile P2P payment applications. These findings have important theoretical and practical implications for stakeholders of mobile P2P payment solution providers as well as intermediaries/banks who provide their own payment applications to their customers.

With the proliferation of social media, it has become easier for people to spread rumors online, which can aggravate the issues arising from online rumors. There are many individuals and organizations that are adversely affected by malicious online rumors. Despite their importance, there has been little research into why and how people spread rumors online, thus inhibiting the understanding of factors that affect the spreading of online rumors. With attention seeking to address this gap, this paper draws upon the dual process theory and the de-individuation theory to develop a theoretical model of factors affecting the spreading of an online rumor, and then empirically tests it using survey data from 211 individuals about a specific rumor. The results indicate that the perceived credibility of the rumor affects the individuals' attitudes toward spreading it, which consequently affects the rumor spreading behavior. Vividness, confirmation of prior beliefs, argument strength, and source credibility positively influence the perceived credibility of online rumors. Finally, anonymity moderates the relationship between attitude toward spreading online rumors and the spreading behavior.

Now is the time for IS scholars to demonstrate the added value of academic theory through its integration with text mining, clearly outline how to implement this for text mining experts outside of the academic field, and move towards establishing this integration as a standard practice. Therefore, in this study we develop a systematic theory-based text-mining framework (TTMF), and illustrate the use and benefits of TTMF by conducting a text-mining project in an actual business case evaluating and improving hotel service quality using a large volume of actual user-generated reviews. A total of 61,304 sentences extracted from actual customer reviews were successfully allocated to SERVQUAL dimensions, and the pragmatic validity of our model was tested by the OLS regression analysis results between the sentiment scores of each SERVQUAL dimension and customer satisfaction (star rates), and showed significant relationships. As a post-hoc analysis, the results of the co-occurrence analysis to define the root causes of positive and negative service quality perceptions and provide action plans to implement improvements were reported.

Negative consequences incurred from employees' non-work-related computing (NWRC) have been one of the security-related issues in information intensive organizations. While most studies have focused on the factors that motivate employees to engage in NWRC, this study examines the mediating effect of moral beliefs on the relationship between sanctions and NWRC using a moral beliefs-based model. The research model posits that the formal (i.e., punishment severity and detection certainty) and informal sanctions (subjective norms and descriptive norms) enhance employees' moral beliefs against NWRC intention. From a cross-sectional scenario-based survey involving 176 employees working at banks in Mongolia, our results indicate that moral beliefs fully mediate the relationship between detection certainty/subjective norms and NWRC intention and act as a partial mediator in the relationship between descriptive norms and NWRC. The findings from this study present empirical evidence that both informal and formal sanctions could be an effective deterrent for NWRC intention through employees' moral beliefs.

The Internet enables businesses to acquire a great deal of information, including prices in the open markets. In this study, we investigate what the value of reference price information is to a company in the market and how the company can make use of such information. Using business analytics, we were able to estimate prices of used cars for a rent-a-car company. The results show that a smart pricing information system is useful for collecting online reference price information and for estimating future prices of used cars and rental prices.

Open innovation has attracted significant attention from both academics and practitioners. However, theoretical and empirical researchers disagree on how open innovation improves firm performance. The inconsistent results reported in the literature may be attributed to the fact that they failed to provide an integrative view of how to make use of internal and external knowledge to enhance innovation performance. Furthermore, although the adoption value of innovation approaches varies depending on firm size and IT capabilities, their impacts have not been adequately taken into consideration. Drawing on complementarity theory, this study revisits the research problem and develops eight hypotheses. Surveys collected from 339 Korean firms were analyzed to test the hypotheses using the supermodularity functions. The results indicated that an internal knowledge-oriented innovation approach has a positive impact on innovation performance regardless of firm size. However, an external knowledge-oriented innovation approach has a positive effect on innovation performance in large firms while having no significant effect on innovation performance of SMEs. Results also confirmed a complementary relationship between internal and external knowledge-oriented innovation approaches in large firms, whereas substitutable relationships were confirmed in SMEs. This study sheds new light on open innovation by identifying the role of different types of innovation approaches, firm size, and IT capabilities.

The era of digital transformation (or the fourth industrial revolution) has been triggered by the rapid development of software (SW) technologies. In this era, several studies suspected rapid changes in job structures occurring around the world. Thus, there is a growing need for acquiring the skill sets required for the future. However, there are no specific studies on how existing jobs are changing. To cope with this ambiguity of job changes, this paper aims to investigate how the current job structure is changing in response to digital transformation. To identify the dynamic nature of job change over time, we conducted an analysis based on job posting data. As a result, nine job occupations and fifteen jobs were found.

This study examined the impact of various product photo features on the attention of potential consumers in online apparel retailers' environment. Recently, the method of apparel's product photo representation in online shopping stores has been changed a lot from the classic product photos in the early days. In order to investigate if this shift is effective in attracting consumers' attention, we examined the related theory and verified its effect through laboratory experiments. In particular, experiment data was collected and analyzed using eye tracking technology. According to the results of this study, it was shown that the product photos with asymmetry are more attractive than symmetrical photos, well emphasized object within a photo more attractive than partially emphasized, smiling faces are more attractive for customer than emotionless and sad, and photos with uncentered models focus more consumer's attention than photos with model in the center. These results are expected to help design internet shopping stores to gaze more customers' attention.

The purpose of this study is to incorporate telemarketing processes to improve telemarketing performance. For this application, we have attempted to mix the model of machine learning to extract potential customers with personalisation techniques to derive recommended products from actual contact. Most of traditional recommendation systems were mainly in ways such as collaborative filtering, which predicts items with a high likelihood of future purchase, based on existing purchase transactions or preferences for products. But, under these systems, new users or items added to the system do not have sufficient information, and generally cause problems such as a cold start that can not obtain satisfactory recommendation items. Also, indiscriminate telemarketing attempts can backfire as they increase the dissatisfaction and fatigue of customers who do not want to be contacted. To this purpose, this study presented a multi-purpose hybrid recommendation algorithm to achieve two goals: to select customers with high possibility of contact, and to recommend products to selected customers. In addition, we used subscription data from telemarketing agency that handles insurance products to derive realistic applicability of the proposed recommendation system. Our proposed recommendation system would certainly solve the cold start and scarcity problem of existing recommendation algorithm by using contents information such as customer master information and telemarketing history. Also. the model could show excellent performance not only in terms of overall performance but also in terms of the recommendation success rate of the unpopular product.

Deep learning techniques such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) show superior performance in text classification than traditional approaches such as Support Vector Machines (SVMs) and Naïve Bayesian approaches. When using CNNs for text classification tasks, word embedding or character embedding is a step to transform words or characters to fixed size vectors before feeding them into convolutional layers. In this paper, we propose a parallel word-level and character-level embedding approach in CNNs for text classification. The proposed approach can capture word-level and character-level patterns concurrently in CNNs. To show the usefulness of proposed approach, we perform experiments with two English and three Korean text datasets. The experimental results show that character-level embedding works better in Korean and word-level embedding performs well in English. Also the experimental results reveal that the proposed approach provides better performance than traditional CNNs with word-level embedding or character-level embedding in both Korean and English documents. From more detail investigation, we find that the proposed approach tends to perform better when there is relatively small amount of data comparing to the traditional embedding approaches.

Text classification is a challenging task, especially when dealing with a huge amount of text data. The performance of a classification model can be varied depending on what type of words contained in the document corpus and what type of features generated for classification. Aside from proposing a new modified version of the existing algorithm or creating a new algorithm, we attempt to modify the use of data. The classifier performance is usually affected by the quality of learning data as the classifier is built based on these training data. We assume that the data from different domains might have different characteristics of noise, which can be utilized in the process of learning the classifier. Therefore, we attempt to enhance the robustness of the classifier by injecting the heterogeneous data artificially into the learning process in order to improve the classification accuracy. Semi-supervised approach was applied for utilizing the heterogeneous data in the process of learning the document classifier. However, the performance of document classifier might be degraded by the unlabeled data. Therefore, we further proposed an algorithm to extract only the documents that contribute to the accuracy improvement of the classifier.

The medical industry is rapidly evolving into a combination of artificial intelligence (AI) and ICT technology, such as mobile health, wireless medical, telemedicine and precision medical care. Medical artificial intelligence can be diagnosed and treated, and autonomous surgical robots can be operated. For smart medical services, data such as medical information and personal medical information are needed. AI is being developed to integrate with companies such as Google, Facebook, IBM and others in the health care field. Telemedicine services are also becoming available. However, security issues of medical information for smart medical industry are becoming important. It can have a devastating impact on life through hacking of medical devices through vulnerable areas. Research on medical information is proceeding on the necessity of privacy and privacy protection. However, there is a lack of research on the practical measures for protecting medical information and the seriousness of security threats. Therefore, in this study, we want to confirm the research trend by collecting data related to medical information in recent 5 years. In this study, smart medical related papers from 2014 to 2018 were collected using smart medical topics, and the medical information papers were rearranged based on this. Research trend analysis uses topic modeling technique for topic information. The result constructs topic network based on relation of topics and grasps main trend through topic.

Online product reviews are a vital source for companies in that they contain consumers' opinions of products. The earlier methods of opinion mining, which involve drawing semantic information from text, have been mostly applied in one dimension. This is not sufficient in itself to elicit reviewers' comprehensive views on products. In this paper, we propose a novel approach in opinion mining by projecting online consumers' reviews in a multidimensional framework to improve review interpretation of products. First of all, we set up a new framework consisting of six dimensions based on a marketing management theory. To calculate the distances of review sentences and each dimension, we embed words in reviews utilizing Google's pre-trained word2vector model. We classified each sentence of the reviews into the respective dimensions of our new framework. After the classification, we measured the sentiment degrees for each sentence. The results were plotted using a radar graph in which the axes are the dimensions of the framework. We tested the strategy on Amazon product reviews of the iPhone and Galaxy smartphone series with a total of around 21,000 sentences. The results showed that the radar graphs visually reflected several issues associated with the products. The proposed method is not for specific product categories. It can be generally applied for opinion mining on reviews of any product category.

The performance of recommender systems (RS) has been measured mainly in terms of accuracy. However, there are other aspects of performance that are difficult to understand in terms of accuracy, such as coverage, serendipity, and satisfaction with recommended results. Moreover, particularly with RSs that suggest multiple items at a time, such as YouTube, user satisfaction with recommended results may vary not only depending on their accuracy, but also on their configuration, content, and design displayed to the user. This is true when classifying an RS as a single RS with one recommended result and as a multiple RS with diverse results. No empirical analysis has been conducted on the influence of the content and distribution of recommendation items on user satisfaction. In this study, we propose a research model representing the content and distribution of recommended items and how they affect user satisfaction with the RS. We focus on RSs that recommend multiple items. We performed an empirical analysis involving 149 YouTube users. The results suggest that user satisfaction with recommended results is significantly affected according to the HHI (Herfindahl-Hirschman Index). In addition, satisfaction significantly increased when the recommended item on the top of the list was the same category in terms of content that users were currently watching. Particularly when the purpose of using RS is hedonic, not utilitarian, the results showed greater satisfaction when the number of views of the recommended items was evenly distributed. However, other characteristics of selected content, such as view count and playback time, had relatively less impact on satisfaction with recommended items. To the best of our knowledge, this study is the first to show that the category concentration of items impacts user satisfaction on websites recommending diverse items in different categories using a content-based filtering system, such as YouTube. In addition, our use of the HHI index, which has been extensively used in economics research, to show the distributional characteristics of recommended items, is also unique. The HHI for categories of recommended items was useful in explaining user satisfaction.