Shopping plans,buying motivations,and return policies: impacts on product returns and purchase likelihoods

Product return behavior and factors that contribute to product returns are an under-researched area, despite significant importance to manufacturers and retailers. The current research attempts to fill the gap by focusing on two factors that shape consumer purchase behavior: (1) whether purchases are planned or unplanned, and (2) whether hedonic or utilitarian motivations drive purchases. The findings show that purchase plans and buying motivations have distinctive and interactive impacts on pre-purchase concerns, self-estimated likelihood of returning purchases, and purchase intentions with or without return policies. When hedonic motivation drives purchases, unplanned (vs. planned) purchases lead to higher return concerns, higher return likelihood, and lower buying intentions. When utilitarian motivation drives purchases, planned and unplanned purchases have comparable return concerns, return likelihood, and buying intentions. This interaction effect on buying intentions dissipates when a lenient return policy is offered.     

Leadership competency inventory: A systematic process of developing and validating a leadership competency scale

This article reports psychometric evaluation of the Penn State Leadership Competency Inventory (LCI). The 32‐item LCI was validated on a sample of 323 managers in the health care industry. Preliminary validity and reliability evidence of the LCI was established through exploratory factor analysis (EFA), item‐total correlations, Cronbach's alpha coefficients, and confirmatory factor analysis (CFA). The four‐factor leadership competency scale, comprising supervisory and managerial competencies, organizational leadership, personal mastery, and resource leadership, accounted for 58% of variance. According to CFA results, the model fit of the four latent factors of the LCI was confirmed to be appropriate. Cross‐validation with other populations is needed to confirm the factor structure. Limitations and further research recommendations are discussed.     

中韩新能源产业合作的经济效应实证研究

本文主要探讨中韩新能源领域的合作,尤其是对两国新能源领域中合作的经济效应进行评估。为了评价合作的经济效应,本文建立新能源合作的相对收益指数,并利用向量自回归模型(VAR)探寻能源合作指数(ECI)和贸易、GDP之间的关系,从而论证中韩新能源产业合作的重要性。     

Financing preferences: evidence from the Korean market

We investigate the financing decisions of Korean firms during the period of 1996–2015. Korean firms follow a matching strategy for funding their cash needs. Cash inflows from investing activities are the primary source of funding for capital expenditures, and cash inflows from financing activities are the major means of covering cash outflows from financing activities. We also find that Korean firms’ financing practice of handling cash deficits can be described by the pecking order model modified and augmented by cash flow variables. Cash inflows from investing activities account for the major portion of financing to make up for cash deficits, followed by short- and long-term debt, and then equity financing.     

Parametric links for binary choice models: A Fisherian–Bayesian colloquy

The familiar logit and probit models provide convenient settings for many binary response applications, but a larger class of link functions may be occasionally desirable. Two parametric families of link functions are investigated: the Gosset link based on the Student t latent variable model with the degrees of freedom parameter controlling the tail behavior, and the Pregibon link based on the (generalized) Tukey λ family, with two shape parameters controlling skewness and tail behavior. Both Bayesian and maximum likelihood methods for estimation and inference are explored, compared and contrasted. In applications, like the propensity score matching problem discussed below, where it is critical to have accurate estimates of the conditional probabilities, we find that misspecification of the link function can create serious bias. Bayesian point estimation via MCMC performs quite competitively with MLE methods; however nominal coverage of Bayes credible regions is somewhat more problematic.     

Estimating the Economic Impact of Convention and Exhibition Businesses,Using a Regional Input–Output Model: A Case Study of the Daejeon Convention Center in South Korea

Past research on the economic impact of convention and exhibition businesses has neglected disaggregated expenditures of these consumer groups. The primary reason for this neglect is that estimating the economic impacts of these businesses is complicated and requires comprehensive surveys of various consumer groups. Thus, this study estimated expenditures based on surveys to different consumers such as convention and exhibition visitors, convention and exhibition organizers, and exhibitors. This study also employed a regional input–output (I–O) model and then matched the types of expenditure patterns with the I–O table. Survey results show that exhibition visitors spent the largest amount (US$153 million), followed by convention attendees (US$48 million), convention organizers (US$47 million), exhibition organizers (US$3.3 million), and exhibitors (US$2.8 million). The results of the I–O model indicate that the Daejeon Convention Center generated US$488 million of output impact, US$102 million of income impact, US$233 million of value added impact, and 10,211 jobs.     

Interdependence in the technology adoption decision among municipalities

This study examines the interdependence among municipalities in the discrete decision to adopt internet technology. Results from probit specifications with interaction effects suggest that cities are influenced by nearby neighbours with similar population size in developing a website. Evidence is also presented indicating interaction among municipalities in the decision to use internet technology to facilitate financial transactions.     

Structuring technological information for technology roadmapping: data mining approach

Most research on technology roadmapping has focused on its practical applications and the development of methods to enhance its operational process. Thus, despite a demand for well-supported, systematic information, little attention has been paid to how/which information can be utilised in technology roadmapping. Therefore, this paper aims at proposing a methodology to structure technological information in order to facilitate the process. To this end, eight methods are suggested to provide useful information for technology roadmapping: summary, information extraction, clustering, mapping, navigation, linking, indicators and comparison. This research identifies the characteristics of significant data that can potentially be used in roadmapping, and presents an approach to extracting important information from such raw data through various data mining techniques including text mining, multi-dimensional scaling and K-means clustering. In addition, this paper explains how this approach can be applied in each step of roadmapping. The proposed approach is applied to develop a roadmap of radio-frequency identification (RFID) technology to illustrate the process practically.     

Strategic visualisation tools for managing technological information

In an information economy, the effective management of a massive amount of technological information is inevitable when attempting to facilitate the decision-making process. It is an important part of selecting R&D projects and allocating budgets to promising technology development. Information visualisation is thus regarded as a critical tool in technological information management because it can provide decisive implications that the intelligence of humans cannot produce. This paper proposes six graphical forms, including a technology tree, a technology map, a technology network, a technology matrix, a technology curve, and a technology dictionary, presenting the generic form and concept of each method. Moreover, the principles and applications of such visualisation tools are suggested in order to enhance their use in practical circumstances. A case study of TFT-LCD (thin film transistor-liquid crystal display) technology will help to explain how to apply visualisation tools to strategic technology management.     

Developing the Causal Model of Online Store Success

This article introduces an Online Store Success Model (OSM) that reflects the characteristics of information systems, marketing, and e-commerce, and then empirically test the model. The OSM has five dimensions: system quality, information quality, service quality, trust, and customer loyalty. The model posits the causal relationships between the dimensions. In order to empirically test the model, a research model is developed and empirically analyzed by structural equation modeling using data from 244 customers on 69 online store websites. The results show that system quality significantly influences service quality and trust; information quality significantly influences service quality and customer loyalty; service quality also significantly influences trust and customer loyalty; and trust has a significant influence on customer loyalty.