• Journal of Global Scholars of Marketing Science
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• v.18 no.1
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• pp.141-156
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• 2008

Market structure analysis is a very useful tool to analyze the competition boundary of the brand or the company. But most of the studies in market structure analysis, the concern lies in nondurable goods such as candies, soft drink and etc. because of the their availability of the data. In the field of durable goods, the limitation of the data availability and the repurchase time period constrain the study. In the analysis of the automobile market, those of views might be more persuasive. The purpose of this study is to analyze the structure of automobile market based on some idea suggested by prior studies. Usually the buyers of the automobile tend to buy upper tier when they buy in the next time. That kind of behavior make it impossible to analyze the structure of automobile market under the level of automobile model. For that reason I tried to analyze the market structure in the brand or company level. In this study, consideration data was used for market structure analysis. The reasons why we used the consideration data are summarized as following. Firstly, as the repurchase time cycle is too long, brand switching data which is used for the market analysis of nondurable good is not avaliable. Secondly, as we mentioned, the buyers of the automobile tend to buy upper tier when they buy in the next time. We used survey data collected in the U.S.A. market in the year of 2005 through questionaire. The sample size was 8,291. The number of brand analyzed in this study was 9 among 37 which was being sold in U.S.A. market. Their market share was around 50%. The brands considered were BMW, Chevrolet, Chrysler, Dodge, Ford, Honda, Mercedes, and Toyota. �� ratio was derived from frequency of the consideration set. Actually the frequency is different from the brand switch concept. In this study to compute the �� ratio, the frequency of the consideration set was used like a frequency of brand switch for convenience. The study can be divided into 2 steps. The first step is to build hypothetical market structures. The second step is to choose the best structure based on the hypothetical market structures, Usually logit analysis is used for the choice best structure. In this study we built 3 hypothetical market structure. They are type-cost, cost-type, and unstructured. We classified the automobile into 5 types, sedan, SUV(Sport Utility Vehicle), Pickup, Mini Van, and Full-size Van. As for purchasing cost, we classified it 2 groups based on the median value. The median value was $28,800. To decide best structure among them, maximum likelihood test was used. Resulting from market structure analysis, we find that the automobile market of USA is hierarchically structured in the form of 'automobile type - purchasing cost'. That is, result showed that automobile buyers considered function or usage first and purchasing cost next. This study has some limitations in the analysis level and variable selection. First, in this study only type of the automobile and purchasing cost were as attributes considered for purchase. Considering other attributes is very needful. Because of the attributes considered, only 3 hypothetical structure could be analyzed. Second, due to the data, brand level analysis was tried. But model level analysis would be better because automobile buyers consider model not brand. To conduct model level study more cases should be obtained. That is for acquiring the better practical meaning, brand level analysis should be conducted when we consider the actual competition which occurred in the real market. Third, the variable selection for building nested logit model was very limited to some avaliable data. In spite of those limitations, the importance of this study lies in the trial of market structure analysis of durable good.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.16 no.3
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• pp.177-191
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• 2021

Due to the development of mobile devices and streaming technology, many changes in consumption patterns have appeared. In addition, social impact is becoming an era of non-face-to-face consumption due to the panthermic environment of COVID-19. Accordingly, in line with the non-face-to-face consumption trend, we focused on the importance of live commerce, which is emerging as a new distribution channel, and tried to investigate the causal relationship that the characteristics of live commerce and show hosts have on purchase intention. The respondents of this study were 235 general adults of live commerce users. Interaction, economics, entertainment as the characteristics of live commerce and attractiveness, professionality, and awareness as the characteristics of show hosts were set as independent variables. Purchase intention was set as the dependent variable, and content flow was set as the mediating variable. As a result of the study, it was found that the characteristics of live commerce such as Interaction, economics, entertainment, and the characteristics of show hosts such as attractiveness, professionality, and awareness all had a positive (+) significant effect on purchase intention. The impact was shown in the following order: entertainment of live commerce, awareness, attractiveness, professionality of show hosts, economics, interaction of live commerce. In addition, the results of the mediating effect of content flow on purchase intention are as follows. Content flow was found to play a mediating role between interaction, entertainment, attractiveness, professionality, awareness and purchase intention. On the other hand, economics was analyzed to have no mediating effect. The implications of this study are as follows. Companies and show hosts that sell products in live commerce should sell products that can inspire consumers rather than simply sell products. In addition, it is considered that content that provides entertainment and attractions gives pleasure to consumers. If not only a well-recognized show host, but also people with high recognition in various fields such as influencers and creators, become show hosts, consumers' content flow and purchase intentions will increase. And vendors must offer interesting content development and reasonable prices. Show hosts need to focus on active communication with consumers.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1009-1029
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• 2023

Urban trees play a vital role in urban ecosystems,significantly reducing impervious surfaces and impacting carbon cycling within the city. Although previous research has demonstrated the efficacy of employing artificial intelligence in conjunction with airborne light detection and ranging (LiDAR) data to generate urban tree information, the availability and cost constraints associated with LiDAR data pose limitations. Consequently, this study employed freely accessible, high-resolution multispectral satellite imagery (i.e., Sentinel-2 data) to estimate fractional tree canopy cover (FTC) within the urban confines of Suwon, South Korea, employing machine learning techniques. This study leveraged a median composite image derived from a time series of Sentinel-2 images. In order to account for the diverse land cover found in urban areas, the model incorporated three types of input variables: average (mean) and standard deviation (std) values within a 30-meter grid from 10 m resolution of optical indices from Sentinel-2, and fractional coverage for distinct land cover classes within 30 m grids from the existing level 3 land cover map. Four schemes with different combinations of input variables were compared. Notably, when all three factors (i.e., mean, std, and fractional cover) were used to consider the variation of landcover in urban areas(Scheme 4, S4), the machine learning model exhibited improved performance compared to using only the mean of optical indices (Scheme 1). Of the various models proposed, the random forest (RF) model with S4 demonstrated the most remarkable performance, achieving R² of 0.8196, and mean absolute error (MAE) of 0.0749, and a root mean squared error (RMSE) of 0.1022. The std variable exhibited the highest impact on model outputs within the heterogeneous land covers based on the variable importance analysis. This trained RF model with S4 was then applied to the entire Suwon region, consistently delivering robust results with an R² of 0.8702, MAE of 0.0873, and RMSE of 0.1335. The FTC estimation method developed in this study is expected to offer advantages for application in various regions, providing fundamental data for a better understanding of carbon dynamics in urban ecosystems in the future.

• Journal of The Korean Association For Science Education
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• v.36 no.6
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• pp.959-968
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• 2016

In this study, the Delphi Method was conducted to extract variables as effects of integrated education. Forty-six experts engaged in both the integrated education and research fields participated in this study. The Delphi survey was conducted for three rounds. In the first round, an open questionnaire was given asking variables possibly considered as effects of integrated education. In the second round, variables induced from analysis of the first survey results were given and the degree of agreement for each variable was determined according to the Likert scale. In the third round of the survey, mean, standard deviation, and the first and third quartile calculated using the results of the second survey were given to experts to determine their degree of assent. In addition, categories for variables were suggested. The degree of agreement for appropriateness of categorization and relative importance were determined As a result, a total of 18 variables were chosen except for career awareness. They were categorized according to their definition and properties into five categories: 'creativity' (flexible thinking, associative thinking, intuitive thinking, creative thinking), 'problem solving' (meta-cognition, problem recognition and solving, critical thinking, decision making ability, ability of knowledge application, knowledge and information processing skills), 'integrative perception and sensitivity' (concern and interest in various disciplines, understanding and acceptance of difference, integrative thinking), 'interpersonal relations' (communication skills, cooperation), and 'disciplinary literacy' (humanistic imagination, basic knowledge and literacy of each discipline, academic motivation). The degree of agreement was high in variables included in 'creativity' and 'problem solving' categories and the frequency of choosing the importance was high in variables included in 'integrative perception and sensitivity'. The educational implication related to implementation and practice of integrated education were discussed on the basis of results.

• Journal of muscle and joint health
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• v.8 no.1
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• pp.122-140
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• 2001

The aims of this study were to understand and to predict the determinent factors affecting the exercise behaviors and physical fitness by testing the Pender's revised health promotion model, and to help the patients with rheumatoid arthritis and osteoarthritis perform the continous exercise program, and to help them maximize the physical effect such as muscle strength, endurance, and functional status and mental effects including self efficacy and quality of life, and improve the physical and mental well being, and to provide a basis for the nursing intervention strategies. Of the selected variables in this study, the endogenous variables included the physical fitness, exercise score, exercise participation, perceived benefits of action, perceived barriers of action to exercise, activity-related affect(depression) and perceived self-efficacy, interpersonal influences(family support), situational factors(duration of arthritis, fatigue) and the exogenous variables included personal sociocultural factor(education level), personal biologic factor(body mass index), personal psychologic factor(perceived health status) and prior related behavior factors(previous participation in exercise, life-style). We analyzed the clinical records of 208 patients with rheumatoid arthritis and degenerative arthritis who visited the outpatient clinics at H university hospital in Seoul. Data were composed of self reported qustionnaire and good of fitness score which were obtained by padalling the ergometer of bicycle for 9 minutes. SPSS Win 8.0 and Window LISREL 8.12a were used for statistical analysis. Of 75 hypothetical paths that influence on physical fitness, exercise participation, exercise score, perceived benefits of action, perceived barriers of action to exercise, activity-related affect(depression) and perceived self-efficacy, interpersonal influences(family support), situational factors(duration of arthritis, fatigue), 40 were supported. The physical fitness was directly influenced by life-style, perceived health status, education level, family support, fatigue, which explained 12% of physical fitness. The exercise participation were directly influenced by life-style, education level, past exercise behavior, perceived benefits of action, perceived barriers of action, depression and duration of arthritis, which explained 47% of exercise participation. Exercise score were directly affected by perceived self efficacy. BMI, life-style, past exercise behavior, perceived benefits of action, family support, perceived health status. perceived barriers of action, and fatigue, which explained 70%. Perceived benefits of action was directly influenced by BMI, life-style, which explained 39%. Perceived barriers of action were directly influeced by past exercise behavior, perceived health status, which explained 7%. Perceived self efficacy were directly influeced by level of education, perceived health status, life-style, which explained 57%. Depression were directly influeced by past exercise behavior, BMI, life-style, which explained 27%. Family support were directly influeced by life-style, perceived health status, which explained 29%. Fatigue were directly influeced by BMI, life-style, perceived health status. which explained 41%. Duration of arthritis were directly influeced by life-style, past exercise behavior, BMI, which explained 6%. In conclusion, important variables for physical fitness were life-style, and variable affecting exercise participation were life-style. Perceived self-efficacy of exercise was a significant predictor of exercise score. BMI, Life-style, perceived benefits of action, family support, past exercise behavior showed direct effects on perceived self-efficacy. Therefore, disease related factor should be minimized for physical performance and well being in nursing intervention for patients with rheumatoid arthritis, and plans to promote and continue exercise should be seeked to reduce disability. In addition, Exercise program should be planned and performed by the exact evaluation of exercise according to the ability of the patients and the contents to improve the importance of exercise and self efficacy in self control program, dedicated educational program should be involved. This study suggest that the methods to reduce the disease related factors, the importance of daily life-style, recognition of benefit of exercise, and educational program to promote self efficacy should be considered in the exercise behavior promotion and nursing intervention for continous performance. The significance of this study is also thought to provide patients with chronic arthritis the specific data for maximal physical and mental well being through exercise, chronic therapeutic procedure, daily adaptation and confrontation in nursing intervention.

• Asia pacific journal of information systems
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• v.17 no.2
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• pp.49-81
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• 2007

Research in the IS (Information Systems) outsourcing has focused on the importance of legal contracts and partnerships between vendors and clients. Without detailed legal contracts, there is no guarantee that an outsourcing vendor would not indulge in self-serving behavior. In addition, partnerships can supplement legal contracts in managing the relationship between clients and vendors legal contracts by itself cannot deal with all the complexity and ambiguity involved with IS outsourcing relationships. In this paper, we introduce a psychological contract (between client and vendor) as an important variable for IS outsourcing success. A psychological contract refers to individual's mental beliefs about his or her mutual obligations in a contractual relationship (Rousseau, 1995). A psychological contract emerges when one party believes that a promise of future returns has been made, a contribution has been given, and thus, an obligation has been created to provide future benefits (Rousseau, 1989). An employmentpsychological contract, which is a widespread concept in psychology, refers to employer and employee expectations of the employment relationship, i.e. mutual obligations, values, expectations and aspirations that operate over and above the formal contract of employment (Smithson and Lewis, 2003). Similar to the psychological contract between an employer and employee, IS outsourcing involves a contract and a set of mutual obligations between client and vendor (Ho et al., 2003). Given the lack of prior research on psychological contracts in the IS outsourcing context, we extend such studies and give insights through investigating the role of psychological contracts between client and vendor. Psychological contract theory offers highly relevant and sound theoretical lens for studying IS outsourcing management because of its six distinctive principles: (1) it focuses on mutual (rather than one-sided) obligations between contractual parties, (2) it's more comprehensive than the concept of legal contract, (3) it's an individual-level construct, (4) it changes over time, (5) it affects organizational behaviors, and (6) it's susceptible to organizational factors (Koh et al., 2004; Rousseau, 1996; Coyle-Shapiro, 2000). The aim of this paper is to put the concept, psychological contract commitment (PCC), under the spotlight, by finding out its mediating effects between legal contracts/partnerships and IS outsourcing success. Our interest is in the psychological contract commitment (PCC) or commitment to psychological contracts, which is the extent to which a partner consistently and deeply concerns with what the counter-party believes as obligations during the IS project. The basic premise for the hypothesized relationship between PCC and success is that for outsourcing success, client and vendor should continually commit to mutual obligations in which both parties believe, rather than to only explicit obligations. The psychological contract commitment playsa pivotal role in evaluating a counter-party because it reflects what one party really expects from the other. If one party consistently shows high commitment to psychological contracts, the other party would evaluate it positively. This will increase positive reciprocation efforts of the other party, thus leading to successful outsourcing outcomes (McNeeley and Meglino, 1994). We have used matched sample data for this research. We have collected three responses from each set of a client and a vendor firm: a project manager of the client firm, a project member from the vendor firm with whom the project manager cooperated, and an end-user of the client company who actually used the outsourced information systems. Special caution was given to the data collection process to avoid any bias in responses. We first sent three types of questionnaires (A, Band C) to each project manager of the client firm, asking him/her to answer the first type of questionnaires (A).

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.361-381
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• 2023

Research on corporate bankruptcy prediction has been focused on financial information. Since the company's financial information is updated quarterly, there is a problem that timeliness is insufficient in predicting the possibility of a company's business closure in real time. Evaluated companies that want to improve this need a method of judging the soundness of a company that uses information other than financial information to judge the soundness of a target company. To this end, as information technology has made it easier to collect non-financial information about companies, research has been conducted to apply additional variables and various methodologies other than financial information to predict corporate bankruptcy. It has become an important research task to determine whether it has an effect. In this study, we examined the impact of electronic payment-related information, which constitutes non-financial information, when predicting the closure of business operators using electronic payment service and examined the difference in closure prediction accuracy according to the combination of financial and non-financial information. Specifically, three research models consisting of a financial information model, a non-financial information model, and a combined model were designed, and the closure prediction accuracy was confirmed with six algorithms including the Multi Layer Perceptron (MLP) algorithm. The model combining financial and non-financial information showed the highest prediction accuracy, followed by the non-financial information model and the financial information model in order. As for the prediction accuracy of business closure by algorithm, XGBoost showed the highest prediction accuracy among the six algorithms. As a result of examining the relative importance of a total of 87 variables used to predict business closure, it was confirmed that more than 70% of the top 20 variables that had a significant impact on the prediction of business closure were non-financial information. Through this, it was confirmed that electronic payment-related information of non-financial information is an important variable in predicting business closure, and the possibility of using non-financial information as an alternative to financial information was also examined. Based on this study, the importance of collecting and utilizing non-financial information as information that can predict business closure is recognized, and a plan to utilize it for corporate decision-making is also proposed.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.89-97
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• 2010

This study researches the effects of common features on a no-choice option with respect to regulatory focus theory. The primary interest is in three factors and their interrelationship: common features, no-choice option, and regulatory focus. Prior studies have compiled vast body of research in these areas. First, the "common features effect" has been observed bymany noted marketing researchers. Tversky (1972) proposed the seminal theory, the EBA model: elimination by aspect. According to this theory, consumers are prone to focus only on unique features during comparison processing, thereby dismissing any common features as redundant information. Recently, however, more provocative ideas have attacked the EBA model by asserting that common features really do affect consumer judgment. Chernev (1997) first reported that adding common features mitigates the choice gap because of the increasing perception of similarity among alternatives. Later, however, Chernev (2001) published a critically developed study against his prior perspective with the proposition that common features may be a cognitive load to consumers, and thus consumers are possible that they are prone to prefer the heuristic processing to the systematic processing. This tends to bring one question to the forefront: Do "common features" affect consumer choice? If so, what are the concrete effects? This study tries to answer the question with respect to the "no-choice" option and regulatory focus. Second, some researchers hold that the no-choice option is another best alternative of consumers, who are likely to avoid having to choose in the context of knotty trade-off settings or mental conflicts. Hope for the future also may increase the no-choice option in the context of optimism or the expectancy of a more satisfactory alternative appearing later. Other issues reported in this domain are time pressure, consumer confidence, and alternative numbers (Dhar and Nowlis 1999; Lin and Wu 2005; Zakay and Tsal 1993). This study casts the no-choice option in yet another perspective: the interactive effects between common features and regulatory focus. Third, "regulatory focus theory" is a very popular theme in recent marketing research. It suggests that consumers have two focal goals facing each other: promotion vs. prevention. A promotion focus deals with the concepts of hope, inspiration, achievement, or gain, whereas prevention focus involves duty, responsibility, safety, or loss-aversion. Thus, while consumers with a promotion focus tend to take risks for gain, the same does not hold true for a prevention focus. Regulatory focus theory predicts consumers' emotions, creativity, attitudes, memory, performance, and judgment, as documented in a vast field of marketing and psychology articles. The perspective of the current study in exploring consumer choice and common features is a somewhat creative viewpoint in the area of regulatory focus. These reviews inspire this study of the interaction possibility between regulatory focus and common features with a no-choice option. Specifically, adding common features rather than omitting them may increase the no-choice option ratio in the choice setting only to prevention-focused consumers, but vice versa to promotion-focused consumers. The reasoning is that when prevention-focused consumers come in contact with common features, they may perceive higher similarity among the alternatives. This conflict among similar options would increase the no-choice ratio. Promotion-focused consumers, however, are possible that they perceive common features as a cue of confirmation bias. And thus their confirmation processing would make their prior preference more robust, then the no-choice ratio may shrink. This logic is verified in two experiments. The first is a $2{\times}2$ between-subject design (whether common features or not X regulatory focus) using a digital cameras as the relevant stimulus-a product very familiar to young subjects. Specifically, the regulatory focus variable is median split through a measure of eleven items. Common features included zoom, weight, memory, and battery, whereas the other two attributes (pixel and price) were unique features. Results supported our hypothesis that adding common features enhanced the no-choice ratio only to prevention-focus consumers, not to those with a promotion focus. These results confirm our hypothesis - the interactive effects between a regulatory focus and the common features. Prior research had suggested that including common features had a effect on consumer choice, but this study shows that common features affect choice by consumer segmentation. The second experiment was used to replicate the results of the first experiment. This experimental study is equal to the prior except only two - priming manipulation and another stimulus. For the promotion focus condition, subjects had to write an essay using words such as profit, inspiration, pleasure, achievement, development, hedonic, change, pursuit, etc. For prevention, however, they had to use the words persistence, safety, protection, aversion, loss, responsibility, stability etc. The room for rent had common features (sunshine, facility, ventilation) and unique features (distance time and building state). These attributes implied various levels and valence for replication of the prior experiment. Our hypothesis was supported repeatedly in the results, and the interaction effects were significant between regulatory focus and common features. Thus, these studies showed the dual effects of common features on consumer choice for a no-choice option. Adding common features may enhance or mitigate no-choice, contradictory as it may sound. Under a prevention focus, adding common features is likely to enhance the no-choice ratio because of increasing mental conflict; under the promotion focus, it is prone to shrink the ratio perhaps because of a "confirmation bias." The research has practical and theoretical implications for marketers, who may need to consider common features carefully in a practical display context according to consumer segmentation (i.e., promotion vs. prevention focus.) Theoretically, the results suggest some meaningful moderator variable between common features and no-choice in that the effect on no-choice option is partly dependent on a regulatory focus. This variable corresponds not only to a chronic perspective but also a situational perspective in our hypothesis domain. Finally, in light of some shortcomings in the research, such as overlooked attribute importance, low ratio of no-choice, or the external validity issue, we hope it influences future studies to explore the little-known world of the "no-choice option."

• Journal of Global Scholars of Marketing Science
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• v.18 no.3
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• pp.115-132
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• 2008

Metal industry is one of the most representative heavy industries and the median sales volume of steel and nonferrous metal companies is over one billion dollars in the case America [Forbes 2006]. As seen in the recent business market situation, an increasing number of industrial manufacturers and suppliers are moving from adversarial to cooperative exchange attitudes that support the long-term relationships with their customers. This article presents the results of an empirical study of the antecedent factors of business relationships in metal industry of the United States. Commitment has been reviewed as a significant and critical variable in research on inter-organizational relationships (Hong et al. 2007, Kim et al. 2007). The future stability of any buyer-seller relationship depends upon the commitment made by the interactants to their relationship. Commitment, according to Dwyer et al. [1987], refers to "an implicit or explicit pledge of relational continuity between exchange partners" and they consider commitment to be the most advanced phase of buyer-seller exchange relationship. Bonds are made because the members need their partners in order to do something and this integration on a task basis can be either symbiotic or cooperative (Svensson 2008). To the extent that members seek the same or mutually supporting ends, there will be strong bonds among them. In other words, the principle that affects the strength of bonds is 'economy of decision making' [Turner 1970]. These bonds provide an important idea to study the causes of business long-term relationships in a sense that organizations can be mutually bonded by a common interest in the economic matters. Recently, the framework of structural bonding has been used to study the buyer-seller relationships in industrial marketing [Han and Sung 2008, Williams et al. 1998, Wilson 1995] in that this structural bonding is a crucial part of the theoretical justification for distinguishing discrete transactions from ongoing long-term relationships. The major antecedent factors of buyer commitment such as technology, CLalt, transaction-specific assets, and importance were identified and explored from the perspective of structural bonding. Research hypotheses were developed and tested by using survey data from the middle managers in the metal industry. H1: Level of technology of the relationship partner is positively related to the level of structural bonding between the buyer and the seller. H2: Comparison level of alternatives is negatively related to the level of structural bonding between the buyer and the seller. H3: Amount of the transaction-specific assets is positively related to the level of structural bonding between the buyer and the seller. H4: Importance of the relationship partner is positively related to the level of structural bonding between the buyer and the seller. H5: Level of structural bonding is positively related to the level of commitment to the relationship. To examine the major antecedent factors of industrial buyer's structural bonding and long-term relationship, questionnaire was prepared, mailed out to the sample of 400 purchasing managers of the US metal industry (SIC codes 33 and 34). After a follow-up request, 139 informants returnedthe questionnaires, resulting in a response rate of 35 percent. 134 responses were used in the final analysis after dropping 5 incomplete questionnaires. All measures were analyzed for reliability and validity following the guidelines offered by Churchill [1979] and Anderson and Gerbing [1988]., the results of fitting the model to the data indicated that the hypothesized model provides a good fit to the data. Goodness-of-fit index (GFI = 0.94) and other indices ( chi-square = 78.02 with p-value = 0.13, Adjusted GFI = 0.90, Normed Fit Index = 0.92) indicated that a major proportion of variances and covariances in the data was accounted for by the model as a whole, and all the parameter estimates showed statistical significance as evidenced by large t-values. All the factor loadings were significantly different from zero. On these grounds we judged the hypothesized model to be a reasonable representation of the data. The results from the present study suggest several implications for buyer-seller relationships. Theoretically, we attempted to conceptualize the antecedent factors of buyer-seller long-term relationships from the perspective of structural bondingin metal industry. The four underlying determinants (i.e. technology, CLalt, transaction-specific assets, and importance) of structural bonding are very critical variables of buyer-seller long-term business relationships. Our model of structural bonding makes an attempt to systematically examine the relationship between the antecedent factors of structural bonding and long-term commitment. Managerially, this research provides industrial purchasing managers with a good framework to assess the interaction processes with their partners and, ability to position their business relationships from the perspective of structural bonding. In other words, based on those underlying variables, industrial purchasing managers can determine the strength of the company's relationships with the key suppliers and its state of preparation to be a successful partner with those suppliers. Both the supplying and customer companies can also benefit by using the concept of 'structural bonding' and evaluating their relationships with key business partners from the structural point of view. In general, the results indicate that structural bonding gives a critical impact on the level of relationship commitment. Managerial implications and limitations of the study are also discussed.

• Asia pacific journal of information systems
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• v.24 no.4
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• pp.443-472
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• 2014

To survive in the global competitive environment, enterprise should be able to solve various problems and find the optimal solution effectively. The big-data is being perceived as a tool for solving enterprise problems effectively and improve competitiveness with its' various problem solving and advanced predictive capabilities. Due to its remarkable performance, the implementation of big data systems has been increased through many enterprises around the world. Currently the big-data is called the 'crude oil' of the 21st century and is expected to provide competitive superiority. The reason why the big data is in the limelight is because while the conventional IT technology has been falling behind much in its possibility level, the big data has gone beyond the technological possibility and has the advantage of being utilized to create new values such as business optimization and new business creation through analysis of big data. Since the big data has been introduced too hastily without considering the strategic value deduction and achievement obtained through the big data, however, there are difficulties in the strategic value deduction and data utilization that can be gained through big data. According to the survey result of 1,800 IT professionals from 18 countries world wide, the percentage of the corporation where the big data is being utilized well was only 28%, and many of them responded that they are having difficulties in strategic value deduction and operation through big data. The strategic value should be deducted and environment phases like corporate internal and external related regulations and systems should be considered in order to introduce big data, but these factors were not well being reflected. The cause of the failure turned out to be that the big data was introduced by way of the IT trend and surrounding environment, but it was introduced hastily in the situation where the introduction condition was not well arranged. The strategic value which can be obtained through big data should be clearly comprehended and systematic environment analysis is very important about applicability in order to introduce successful big data, but since the corporations are considering only partial achievements and technological phases that can be obtained through big data, the successful introduction is not being made. Previous study shows that most of big data researches are focused on big data concept, cases, and practical suggestions without empirical study. The purpose of this study is provide the theoretically and practically useful implementation framework and strategies of big data systems with conducting comprehensive literature review, finding influencing factors for successful big data systems implementation, and analysing empirical models. To do this, the elements which can affect the introduction intention of big data were deducted by reviewing the information system's successful factors, strategic value perception factors, considering factors for the information system introduction environment and big data related literature in order to comprehend the effect factors when the corporations introduce big data and structured questionnaire was developed. After that, the questionnaire and the statistical analysis were performed with the people in charge of the big data inside the corporations as objects. According to the statistical analysis, it was shown that the strategic value perception factor and the inside-industry environmental factors affected positively the introduction intention of big data. The theoretical, practical and political implications deducted from the study result is as follows. The frist theoretical implication is that this study has proposed theoretically effect factors which affect the introduction intention of big data by reviewing the strategic value perception and environmental factors and big data related precedent studies and proposed the variables and measurement items which were analyzed empirically and verified. This study has meaning in that it has measured the influence of each variable on the introduction intention by verifying the relationship between the independent variables and the dependent variables through structural equation model. Second, this study has defined the independent variable(strategic value perception, environment), dependent variable(introduction intention) and regulatory variable(type of business and corporate size) about big data introduction intention and has arranged theoretical base in studying big data related field empirically afterwards by developing measurement items which has obtained credibility and validity. Third, by verifying the strategic value perception factors and the significance about environmental factors proposed in the conventional precedent studies, this study will be able to give aid to the afterwards empirical study about effect factors on big data introduction. The operational implications are as follows. First, this study has arranged the empirical study base about big data field by investigating the cause and effect relationship about the influence of the strategic value perception factor and environmental factor on the introduction intention and proposing the measurement items which has obtained the justice, credibility and validity etc. Second, this study has proposed the study result that the strategic value perception factor affects positively the big data introduction intention and it has meaning in that the importance of the strategic value perception has been presented. Third, the study has proposed that the corporation which introduces big data should consider the big data introduction through precise analysis about industry's internal environment. Fourth, this study has proposed the point that the size and type of business of the corresponding corporation should be considered in introducing the big data by presenting the difference of the effect factors of big data introduction depending on the size and type of business of the corporation. The political implications are as follows. First, variety of utilization of big data is needed. The strategic value that big data has can be accessed in various ways in the product, service field, productivity field, decision making field etc and can be utilized in all the business fields based on that, but the parts that main domestic corporations are considering are limited to some parts of the products and service fields. Accordingly, in introducing big data, reviewing the phase about utilization in detail and design the big data system in a form which can maximize the utilization rate will be necessary. Second, the study is proposing the burden of the cost of the system introduction, difficulty in utilization in the system and lack of credibility in the supply corporations etc in the big data introduction phase by corporations. Since the world IT corporations are predominating the big data market, the big data introduction of domestic corporations can not but to be dependent on the foreign corporations. When considering that fact, that our country does not have global IT corporations even though it is world powerful IT country, the big data can be thought to be the chance to rear world level corporations. Accordingly, the government shall need to rear star corporations through active political support. Third, the corporations' internal and external professional manpower for the big data introduction and operation lacks. Big data is a system where how valuable data can be deducted utilizing data is more important than the system construction itself. For this, talent who are equipped with academic knowledge and experience in various fields like IT, statistics, strategy and management etc and manpower training should be implemented through systematic education for these talents. This study has arranged theoretical base for empirical studies about big data related fields by comprehending the main variables which affect the big data introduction intention and verifying them and is expected to be able to propose useful guidelines for the corporations and policy developers who are considering big data implementationby analyzing empirically that theoretical base.