• Journal of Labour Economics
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• v.24 no.3
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• pp.13-38
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• 2001

The question about whether a shorter workweek may increase employment has been a serious issue and been furiously debated among collective bargainers. The advocators recommend publicly that a reduction in standard working hours will provide benefits to the unemployed through the provision of new jobs, and also can improve the quality of life workers. The opponents argue that a shorter workweek will increase labor costs and induce firms to reduce their production levels, and consequently cut back their demand for labor. Although the debate is still continuing, considerable has been made toward achieving the goal workweek reduction. The analytical framework of this paper is a Putty-clay-model, in which the short-and long-term impacts of changes in working time on the employment associated with the interrelations of wages, prices, hourly labour productivity, the firm's labor demand, business cycle and economic growth etc. must be analyzed.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.117-137
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• 2009

Recently, intelligent traffic information systems have enabled people to forecast traffic conditions before hitting the road. These convenient systems operate on the basis of data reflecting current road and traffic conditions as well as distance-based data between locations. Thanks to the rapid development of ubiquitous computing, tremendous context data have become readily available making vehicle route planning easier than ever. Previous research in relation to optimization of vehicle route planning merely focused on finding the optimal distance between locations. Contexts reflecting the road and traffic conditions were then not seriously treated as a way to resolve the optimal routing problems based on distance-based route planning, because this kind of information does not have much significant impact on traffic routing until a a complex traffic situation arises. Further, it was also not easy to take into full account the traffic contexts for resolving optimal routing problems because predicting the dynamic traffic situations was regarded a daunting task. However, with rapid increase in traffic complexity the importance of developing contexts reflecting data related to moving costs has emerged. Hence, this research proposes a framework designed to resolve an optimal route planning problem by taking full account of additional moving cost such as road traffic cost and weather cost, among others. Recent technological development particularly in the ubiquitous computing environment has facilitated the collection of such data. This framework is based on the contexts of time, traffic, and environment, which addresses the following issues. First, we clarify and classify the diverse contexts that affect a vehicle's velocity and estimates the optimization of moving cost based on dynamic programming that accounts for the context cost according to the variance of contexts. Second, the velocity reduction rate is applied to find the optimal route (shortest path) using the context data on the current traffic condition. The velocity reduction rate infers to the degree of possible velocity including moving vehicles' considerable road and traffic contexts, indicating the statistical or experimental data. Knowledge generated in this papercan be referenced by several organizations which deal with road and traffic data. Third, in experimentation, we evaluate the effectiveness of the proposed context-based optimal route (shortest path) between locations by comparing it to the previously used distance-based shortest path. A vehicles' optimal route might change due to its diverse velocity caused by unexpected but potential dynamic situations depending on the road condition. This study includes such context variables as 'road congestion', 'work', 'accident', and 'weather' which can alter the traffic condition. The contexts can affect moving vehicle's velocity on the road. Since these context variables except for 'weather' are related to road conditions, relevant data were provided by the Korea Expressway Corporation. The 'weather'-related data were attained from the Korea Meteorological Administration. The aware contexts are classified contexts causing reduction of vehicles' velocity which determines the velocity reduction rate. To find the optimal route (shortest path), we introduced the velocity reduction rate in the context for calculating a vehicle's velocity reflecting composite contexts when one event synchronizes with another. We then proposed a context-based optimal route (shortest path) algorithm based on the dynamic programming. The algorithm is composed of three steps. In the first initialization step, departure and destination locations are given, and the path step is initialized as 0. In the second step, moving costs including composite contexts into account between locations on path are estimated using the velocity reduction rate by context as increasing path steps. In the third step, the optimal route (shortest path) is retrieved through back-tracking. In the provided research model, we designed a framework to account for context awareness, moving cost estimation (taking both composite and single contexts into account), and optimal route (shortest path) algorithm (based on dynamic programming). Through illustrative experimentation using the Wilcoxon signed rank test, we proved that context-based route planning is much more effective than distance-based route planning., In addition, we found that the optimal solution (shortest paths) through the distance-based route planning might not be optimized in real situation because road condition is very dynamic and unpredictable while affecting most vehicles' moving costs. For further study, while more information is needed for a more accurate estimation of moving vehicles' costs, this study still stands viable in the applications to reduce moving costs by effective route planning. For instance, it could be applied to deliverers' decision making to enhance their decision satisfaction when they meet unpredictable dynamic situations in moving vehicles on the road. Overall, we conclude that taking into account the contexts as a part of costs is a meaningful and sensible approach to in resolving the optimal route problem.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.221-238
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• 2019

Recently, companies and public institutions have been actively introducing chatbot services in the field of customer counseling and response. The introduction of the chatbot service not only brings labor cost savings to companies and organizations, but also enables rapid communication with customers. Advances in data analytics and artificial intelligence are driving the growth of these chatbot services. The current chatbot can understand users' questions and offer the most appropriate answers to questions through machine learning and deep learning. The advancement of chatbot core technologies such as NLP, NLU, and NLG has made it possible to understand words, understand paragraphs, understand meanings, and understand emotions. For this reason, the value of chatbots continues to rise. However, technology-oriented chatbots can be inconsistent with what users want inherently, so chatbots need to be addressed in the area of the user experience, not just in the area of technology. The Fourth Industrial Revolution represents the importance of the User Experience as well as the advancement of artificial intelligence, big data, cloud, and IoT technologies. The development of IT technology and the importance of user experience have provided people with a variety of environments and changed lifestyles. This means that experiences in interactions with people, services(products) and the environment become very important. Therefore, it is time to develop a user needs-based services(products) that can provide new experiences and values to people. This study proposes a chatbot development process based on user needs by applying the design thinking approach, a representative methodology in the field of user experience, to chatbot development. The process proposed in this study consists of four steps. The first step is 'setting up knowledge domain' to set up the chatbot's expertise. Accumulating the information corresponding to the configured domain and deriving the insight is the second step, 'Knowledge accumulation and Insight identification'. The third step is 'Opportunity Development and Prototyping'. It is going to start full-scale development at this stage. Finally, the 'User Feedback' step is to receive feedback from users on the developed prototype. This creates a "user needs-based service (product)" that meets the process's objectives. Beginning with the fact gathering through user observation, Perform the process of abstraction to derive insights and explore opportunities. Next, it is expected to develop a chatbot that meets the user's needs through the process of materializing to structure the desired information and providing the function that fits the user's mental model. In this study, we present the actual construction examples for the domestic cosmetics market to confirm the effectiveness of the proposed process. The reason why it chose the domestic cosmetics market as its case is because it shows strong characteristics of users' experiences, so it can quickly understand responses from users. This study has a theoretical implication in that it proposed a new chatbot development process by incorporating the design thinking methodology into the chatbot development process. This research is different from the existing chatbot development research in that it focuses on user experience, not technology. It also has practical implications in that companies or institutions propose realistic methods that can be applied immediately. In particular, the process proposed in this study can be accessed and utilized by anyone, since 'user needs-based chatbots' can be developed even if they are not experts. This study suggests that further studies are needed because only one field of study was conducted. In addition to the cosmetics market, additional research should be conducted in various fields in which the user experience appears, such as the smart phone and the automotive market. Through this, it will be able to be reborn as a general process necessary for 'development of chatbots centered on user experience, not technology centered'.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.1-15
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• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Journal of Global Scholars of Marketing Science
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• v.18 no.4
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• pp.119-155
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• 2008

Customer Relationship Management(CRM) has been a sustainable competitive edge of many companies. CRM analyzes customer data for designing and executing targeted marketing analysing customer behavior in order to make decisions relating to products and services including management information system. It is critical for companies to get and maintain profitable customers. How to manage relationships with customers effectively has become an important issue for both academicians and practitioners in recent years. However, the existing academic literature and the practical applications of customer relationship management(CRM) strategies have been focused on the technical process and organizational structure about the implementation of CRM. These limited focus on CRM lead to the result of numerous reports of failed implementations of various types of CRM projects. Many of these failures are also related to the absence of marketing approach. Identifying successful factors and outcomes focused on marketing concept before introducing a CRM project are a pre-implementation requirements. Many researchers have attempted to find the factors that contribute to the success of CRM. However, these research have some limitations in terms of marketing approach without explaining how the marketing based factors contribute to the CRM success. An understanding of how to manage relationship with crucial customers effectively based marketing approach has become an important topic for both academicians and practitioners. However, the existing papers did not provide a clear antecedent and outcomes factors focused on marketing approach. This paper attempt to validate whether or not such various marketing factors would impact on relational quality and CRM performance in terms of marketing oriented perceptivity. More specifically, marketing oriented factors involving market orientation, customer orientation, customer information orientation, and core customer orientation can influence relationship quality(satisfaction and trust) and CRM outcome(customer retention and customer share). Another major goals of this research are to identify the effect of relationship quality on CRM outcomes consisted of customer retention and share to show the relationship strength between two factors. Based on meta analysis for conventional studies, I can construct the following research model. An empirical study was undertaken to test the hypotheses with data from various companies. Multiple regression analysis and t-test were employed to test the hypotheses. The reliability and validity of our measurements were tested by using Cronbach's alpha coefficient and principal factor analysis respectively, and seven hypotheses were tested through performing correlation test and multiple regression analysis. The first key outcome is a theoretically and empirically sound CRM factors(marketing orientation, customer orientation, customer information orientation, and core customer orientation.) in the perceptive of marketing. The intensification of ${\beta}$coefficient among antecedents factors in terms of marketing was not same. In particular, The effects on customer trust of marketing based CRM antecedents were significantly confirmed excluding core customer orientation. It was notable that the direct effects of core customer orientation on customer trust were not exist. This means that customer trust which is firmly formed by long term tasks will not be directly linked to the core customer orientation. the enduring management concerned with this interactions is probably more important for the successful implementation of CRM. The second key result is that the implementation and operation of successful CRM process in terms of marketing approach have a strong positive association with both relationship quality(customer trust/customer satisfaction) and CRM performance(customer retention and customer possession). The final key fact that relationship quality has a strong positive effect on customer retention and customer share confirms that improvements in customer satisfaction and trust improve accessibility to customers, provide more consistent service and ensure value-for-money within the front office which result in growth of customer retention and customer share. Particularly, customer satisfaction and trust which is main components of relationship quality are found to be positively related to the customer retention and customer share. Interactive managements of these main variables play key roles in connecting the successful antecedent of CRM with final outcome involving customer retention and share. Based on research results, This paper suggest managerial implications concerned with constructions and executions of CRM focusing on the marketing perceptivity. I can conclude in general the CRM can be achieved by the recognition of antecedents and outcomes based on marketing concept. The implementation of marketing concept oriented CRM will be connected with finding out about customers' purchasing habits, opinions and preferences profiling individuals and groups to market more effectively and increase sales changing the way you operate to improve customer service and marketing. Benefiting from CRM is not just a question of investing the right software, but adapt CRM users to the concept of marketing including marketing orientation, customer orientation, and customer information orientation. No one deny that CRM is a process or methodology used to develop stronger relationships being composed of many technological components, but thinking about CRM in primarily technological terms is a big mistake. We can infer from this paper that the more useful way to think and implement about CRM is as a process that will help bring together lots of pieces of marketing concept about customers, marketing effectiveness, and market trends. Finally, a real situation we conducted our research may enable academics and practitioners to understand the antecedents and outcomes in the perceptive of marketing more clearly.

• Journal of Distribution Research
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• v.17 no.4
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• pp.29-53
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• 2012

As so many marketers get to use diverse sales promotion methods, manufacturer and retailer in a channel often use them too. In this context, diverse issues on sales promotion management arise. One of them is the issue of unplanned buying. Consumers' unplanned buying is clearly better off for the retailer but not for manufacturer. This asymmetric influence of unplanned buying should be dealt with prudently because of its possibility of provocation of channel conflict. However, there have been scarce studies on the sales promotion management strategy considering the unplanned buying and its asymmetric effect on retailer and manufacturer. In this paper, we try to find a better way for a manufacturer in a channel to promote performance through the retailer's sales promotion efforts when there is potential of unplanned buying effect. We investigate via game-theoretic modeling what is the optimal cost sharing level between the manufacturer and retailer when there is unplanned buying effect. We investigated following issues about the topic as follows: (1) What structure of cost sharing mechanism should the manufacturer and retailer in a channel choose when unplanned buying effect is strong (or weak)? (2) How much payoff could the manufacturer and retailer in a channel get when unplanned buying effect is strong (or weak)? We focus on the impact of unplanned buying effect on the optimal cost sharing mechanism for sales promotions between a manufacturer and a retailer in a same channel. So we consider two players in the game, a manufacturer and a retailer who are interacting in a same distribution channel. The model is of complete information game type. In the model, the manufacturer is the Stackelberg leader and the retailer is the follower. Variables in the model are as following table. Manufacturer's objective function in the basic game is as follows: ${\Pi}={\Pi}_1+{\Pi}_2$, where, ${\Pi}_1=w_1(1+L-p_1)-{\psi}^2$, ${\Pi}_2=w_2(1-{\epsilon}L-p_2)$. And retailer's is as follows: ${\pi}={\pi}_1+{\pi}_2$, where, ${\pi}_1=(p_1-w_1)(1+L-p_1)-L(L-{\psi})+p_u(b+L-p_u)$, ${\pi}_2=(p_2-w_2)(1-{\epsilon}L-p_2)$. The model is of four stages in two periods. Stages of the game are as follows. (Stage 1) Manufacturer sets wholesale price of the first period($w_1$) and cost sharing level of channel sales promotion(${\Psi}$). (Stage 2) Retailer sets retail price of the focal brand($p_1$), the unplanned buying item($p_u$), and sales promotion level(L). (Stage 3) Manufacturer sets wholesale price of the second period($w_2$). (Stage 4) Retailer sets retail price of the second period($p_2$). Since the model is a kind of dynamic games, we try to find a subgame perfect equilibrium to derive some theoretical and managerial implications. In order to obtain the subgame perfect equilibrium, we use the backward induction method. In using backward induction approach, we solve the problems backward from stage 4 to stage 1. By completely knowing follower's optimal reaction to the leader's potential actions, we can fold the game tree backward. Equilibrium of each variable in the basic game is as following table. We conducted more analysis of additional game about diverse cost level of manufacturer. Manufacturer's objective function in the additional game is same with that of the basic game as follows: ${\Pi}={\Pi}_1+{\Pi}_2$, where, ${\Pi}_1=w_1(1+L-p_1)-{\psi}^2$, ${\Pi}_2=w_2(1-{\epsilon}L-p_2)$. But retailer's objective function is different from that of the basic game as follows: ${\pi}={\pi}_1+{\pi}_2$, where, ${\pi}_1=(p_1-w_1)(1+L-p_1)-L(L-{\psi})+(p_u-c)(b+L-p_u)$, ${\pi}_2=(p_2-w_2)(1-{\epsilon}L-p_2)$. Equilibrium of each variable in this additional game is as following table. Major findings of the current study are as follows: (1) As the unplanned buying effect gets stronger, manufacturer and retailer had better increase the cost for sales promotion. (2) As the unplanned buying effect gets stronger, manufacturer had better decrease the cost sharing portion of total cost for sales promotion. (3) Manufacturer's profit is increasing function of the unplanned buying effect. (4) All results of (1),(2),(3) are alleviated by the increase of retailer's procurement cost to acquire unplanned buying items. The authors discuss the implications of those results for the marketers in manufacturers or retailers. The current study firstly suggests some managerial implications for the manufacturer how to share the sales promotion cost with the retailer in a channel to the high or low level of the consumers' unplanned buying potential.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.53-77
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• 2015

Mobile commerce provides a convenient shopping experience in which users can buy products without the constraints of time and space. Mobile commerce has already set off a mega trend in Korea. The market size is estimated at approximately 15 trillion won (KRW) for 2015, thus far. In the Korean market, social commerce and open market are key components. Social commerce has an overwhelming open market in terms of the number of users in the Korean mobile commerce market. From the point of view of the industry, quick market entry, and content curation are considered to be the major success factors, reflecting the rapid growth of social commerce in the market. However, academics' empirical research and analysis to prove the success rate of social commerce is still insufficient. Henceforward, it is to be expected that social commerce and the open market in the Korean mobile commerce will compete intensively. So it is important to conduct an empirical analysis to prove the differences in user experience between social commerce and open market. This paper is an exploratory study that shows a comparative analysis of social commerce and the open market regarding user experience, which is based on the mobile users' reviews. Firstly, this study includes a collection of approximately 10,000 user reviews of social commerce and open market listed Google play. A collection of mobile user reviews were classified into topics, such as perceived usefulness and perceived ease of use through LDA topic modeling. Then, a sentimental analysis and co-occurrence analysis on the topics of perceived usefulness and perceived ease of use was conducted. The study's results demonstrated that social commerce users have a more positive experience in terms of service usefulness and convenience versus open market in the mobile commerce market. Social commerce has provided positive user experiences to mobile users in terms of service areas, like 'delivery,' 'coupon,' and 'discount,' while open market has been faced with user complaints in terms of technical problems and inconveniences like 'login error,' 'view details,' and 'stoppage.' This result has shown that social commerce has a good performance in terms of user service experience, since the aggressive marketing campaign conducted and there have been investments in building logistics infrastructure. However, the open market still has mobile optimization problems, since the open market in mobile commerce still has not resolved user complaints and inconveniences from technical problems. This study presents an exploratory research method used to analyze user experience by utilizing an empirical approach to user reviews. In contrast to previous studies, which conducted surveys to analyze user experience, this study was conducted by using empirical analysis that incorporates user reviews for reflecting users' vivid and actual experiences. Specifically, by using an LDA topic model and TAM this study presents its methodology, which shows an analysis of user reviews that are effective due to the method of dividing user reviews into service areas and technical areas from a new perspective. The methodology of this study has not only proven the differences in user experience between social commerce and open market, but also has provided a deep understanding of user experience in Korean mobile commerce. In addition, the results of this study have important implications on social commerce and open market by proving that user insights can be utilized in establishing competitive and groundbreaking strategies in the market. The limitations and research direction for follow-up studies are as follows. In a follow-up study, it will be required to design a more elaborate technique of the text analysis. This study could not clearly refine the user reviews, even though the ones online have inherent typos and mistakes. This study has proven that the user reviews are an invaluable source to analyze user experience. The methodology of this study can be expected to further expand comparative research of services using user reviews. Even at this moment, users around the world are posting their reviews about service experiences after using the mobile game, commerce, and messenger applications.

• Asia pacific journal of information systems
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• v.17 no.4
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• pp.61-95
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• 2007

Small-and-medium sized enterprises(SMEs) represent quite a large proportion of the industry as a whole in terms of the number of enterprises or employees. However researches on information system so far have focused on large companies, probably because SMEs were not so active in introducing information systems as larger enterprises. SMEs are now increasingly bringing in information systems such as ERP(Enterprise Resource Planning Systems) and some of the companies already entered the stage of ongoing use. Accordingly, researches should deal with the use of information systems by SME s operating under different conditions from large companies. This study examined factors and mechanism inducing faithful appropriation of information systems, in particular integrative systems such as ERP, in view of individuals` active feedback-seeking behavior. There are three factors expected to affect end users` feedback-seeking behavior for faithful appropriation of information systems. They are management support, peer IT champ support, and IT staff support. The main focus of the study is on how these factors affect feedback-seeking behavior and whether the feedback-seeking behavior plays the role of mediator for realizing faithful appropriation of information systems by end users. To examine the research model and the hypotheses, this study employed an empirical method based on a field survey. The survey used measurements mostly employed and verified by previous researches, while some of the measurements had gone through minor modifications for the purpose of the study. The survey respondents are individual employees of SMEs that have been using ERP for one year or longer. To prevent common method bias, Task-Technology Fit items used as the control variable were made to be answered by different respondents. In total, 127 pairs of valid questionnaires were collected and used for the analysis. The PLS(Partial Least Squares) approach to structural equation modeling(PLS-Graph v.3.0) was used as our data analysis strategy because of its ability to model both formative and reflective latent constructs under small-and medium-size samples. The analysis shows Reliability, Construct Validity and Discriminant Validity are appropriate. The path analysis results are as follows; first, the more there is peer IT champ support, the more the end user is likely to show feedback-seeking behavior(path-coefficient=0.230, t=2.28, p<0.05). In other words, if colleagues proficient in information system use recognize the importance of their help, pass on what they have found to be an effective way of using the system or correct others' misuse, ordinary end users will be able to seek feedback on the faithfulness of their appropriation of information system without hesitation, because they know the convenience of getting help. Second, management support encourages ordinary end users to seek more feedback(path-coefficient=0.271, t=3.06, p<0.01) by affecting the end users' perceived value of feedback(path-coefficient=0.401, t=6.01, p<0.01). Management support is far more influential than other factors that when the management of an SME well understands the benefit of ERP, promotes its faithful appropriation and pays attention to employees' satisfaction with the system, employees will make deliberate efforts for faithful appropriation of the system. However, the third factor, IT staff support was found not to be conducive to feedback-seeking behavior from end users(path-coefficient=0.174, t=1.83). This is partly attributable to the fundamental reason that there is little support for end users from IT staff in SMEs. Even when IT staff provides support, end users may find it less important than that from coworkers more familiar with the end users' job. Meanwhile, the more end users seek feedback and attempt to find ways of faithful appropriation of information systems, the more likely the users will be able to deploy the system according to the purpose the system was originally meant for(path-coefficient=0.35, t=2.88, p<0.01). Finally, the mediation effect analysis confirmed the mediation effect of feedback-seeking behavior. By confirming the mediation effect of feedback-seeking behavior, this study draws attention to the importance of feedback-seeking behavior that has long been overlooked in research about information system use. This study also explores the factors that promote feedback-seeking behavior which in result could affect end user`s faithful appropriation of information systems. In addition, this study provides insight about which inducements or resources SMEs should offer to promote individual users' feedback-seeking behavior when formal and sufficient support from IT staff or an outside information system provider is hardly expected. As the study results show, under the business environment of SMEs, help from skilled colleagues and the management plays a critical role. Therefore, SMEs should seriously consider how to utilize skilled peer information system users, while the management should pay keen attention to end users and support them to make the most of information systems.

• Asia Marketing Journal
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• v.13 no.3
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• pp.183-208
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• 2011

After more than 25 years of accumulated research evidence, there is little doubt that leadership behavior is related to a wide variety of positive individual and organizational outcomes. Indeed, leadership behavior has been empirically linked to increased employee satisfaction, organizational commitment, extra effort, turnover intention, organizational citizenship behavior, and overall employee performance. However, it is important to point out that although leadership behavior has been linked to a number of positive organizational outcomes, research regarding the antecedents of such behavior is limited. Especially there is little research dealing with the antecedents of inter-organizational leadership behavior. Supply chain leadership can be defined as the activities undertaken by the supply chain leader to influence the management programs and strategies of supply chain members. Supply chain performance is influenced by leadership of supply chain leader. Although research on supply chain leadership can be broadly categorized, many researchers have been preoccupied with analyzing supply chain leadership by the power-influence approach measuring such as control, power, and power bases. Also they have not examined the relationship between leadership and financial performance. This study has started to overcome those research gaps. The purpose of this study is to investigate the effect of relational behaviors on supply chain leadership, and the effect of such leadership behavior on financial performance of supply chain followers. In addition, this study also try to find out moderating variable existing in the relationship. To be concrete, First, this study develops a model of the antecedents of four conceptually distinct forms of relational behaviors such as training, fair reward, offering vision, and inter-organizational communication, and tests the hypothesized differential effects of relational behavior forms on supply chain leadership. Second, this study tests the effect of supply chain leadership on financial performance. Third, this study investigates the extent to which this leadership-performance relationship is moderated by leader ethicality. The reason why this study deals with convenience store supply chain is that there is very strong inter-dependence between a franchisor and its suppliers. Their strong inter-dependence makes their relationship as the relationship between a superior and subordinates and creates an atmosphere that leadership occur without difficulty. For the purpose of empirical testing, 217 respondents of suppliers of convenience store supply chain in Korea were surveyed and the analysis utilizing partial least square model indicated that training, fair reward, inter-organizational communication had positive effects on supply chain leadership, and such leadership had positive effect on financial performance of followers. On the other hand, offering vision had no effect on supply chain leadership. In addition, leader ethicality had moderating effect on the relationship between supply chain leadership and financial performance.

• Journal of Environmental Policy
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• v.13 no.4
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• pp.3-49
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• 2014

Growing interest of stakeholders on corporate responsibilities for environment and tightening environmental regulations are highlighting the importance of environmental management more than ever. However, companies' awareness of the importance of environment is still falling behind, and related academic works have not shown consistent conclusions on the relationship between environmental performance and economic performance. One of the reasons is different ways of measuring these two performances. The evaluation scope of economic performance is relatively narrow and the performance can be measured by a unified unit such as price, while the scope of environmental performance is diverse and a wide range of units are used for measuring environmental performances instead of using a single unified unit. Therefore, the results of works can be different depending on the performance indicators selected. In order to resolve this problem, generalized and standardized performance indicators should be developed. In particular, the performance indicators should be able to cover the concepts of both environmental and economic performances because the recent idea of environmental management has expanded to encompass the concept of sustainability. Another reason is that most of the current researches tend to focus on the motive of environmental investments and environmental performance, and do not offer a guideline for an effective implementation strategy for environmental management. For example, a process improvement strategy or a market discrimination strategy can be deployed through comparing the environment competitiveness among the companies in the same or similar industries, so that a virtuous cyclical relationship between environmental and economic performances can be secured. A novel method for measuring eco-efficiency by utilizing Data Envelopment Analysis (DEA), which is able to combine multiple environmental and economic performances, is proposed in this report. Based on the eco-efficiencies, the environmental competitiveness is analyzed and the optimal combination of inputs and outputs are recommended for improving the eco-efficiencies of inefficient firms. Furthermore, the panel analysis is applied to the causal relationship between eco-efficiency and economic performance, and the pooled regression model is used to investigate the relationship between eco-efficiency and economic performance. The four-year eco-efficiencies between 2010 and 2013 of 23 companies are obtained from the DEA analysis; a comparison of efficiencies among 23 companies is carried out in terms of technical efficiency(TE), pure technical efficiency(PTE) and scale efficiency(SE), and then a set of recommendations for optimal combination of inputs and outputs are suggested for the inefficient companies. Furthermore, the experimental results with the panel analysis have demonstrated the causality from eco-efficiency to economic performance. The results of the pooled regression have shown that eco-efficiency positively affect financial perform ances(ROA and ROS) of the companies, as well as firm values(Tobin Q, stock price, and stock returns). This report proposes a novel approach for generating standardized performance indicators obtained from multiple environmental and economic performances, so that it is able to enhance the generality of relevant researches and provide a deep insight into the sustainability of environmental management. Furthermore, using efficiency indicators obtained from the DEA model, the cause of change in eco-efficiency can be investigated and an effective strategy for environmental management can be suggested. Finally, this report can be a motive for environmental management by providing empirical evidence that environmental investments can improve economic performance.