Emergency usage intention and behavior are crucial to business service success for m-Health providers and patient healthcare service. This study aimed to identify the factors that influence m-Health acceptance and the effect of emergency use intentions on usage behavior among Taiwanese m-Health users by adopting and extending the Unified Theory of Acceptance and Use of Technology (UTAUT). This study also examines the moderating role of gender and age in the effects of the independent variables on satisfaction with m-Health services. An online questionnaire was used to collect data from 371 participants. The results revealed that performance expectancy, facilitating conditions, and trust had positive effects on user satisfaction. Additionally, m-Health knowledge and user satisfaction had positive effects on emergency use intentions. However, social influence and effort expectancy did not have a significant effect on satisfaction. Moreover, age and gender significantly moderated the effects of some predictors.

Purpose -To propose and test an augmented collaborative planning, forecasting, and replenishment (A-CPFR) model in a retailer-supplier context with a view to improving forecasting accuracy and then reducing the "bullwhip effect" in the supply chain. Design/methodology/approach -After a literature review, the paper presents a real case in which the present authors provided assistance. The description of the case includes: case company background; an "as-is" model analysis; a "to-be" (CPFR) model analysis; and a description of the results and potential benefits. The paper then proposes an A-CPFR model for the case and performs a simulation of the new model for comparison with the existing CPFR model. Findings -The results show that the mean absolute deviation of forecasting and the inventory variance are both better in the proposed model than in the existing CPFR model. The proposed model can thus improve the accuracy of sales forecasting, reduce inventory levels, and reduce the "bullwhip effect". Practical implications -In addition to information provided by the retailer, a logistics supplier should also obtain competitors' promotional information from the market as another factor for forecasting -thus enabling timely responses to demand fluctuations. Originality/value -The proposed model is an original and useful development on the existing CPFR model. It could become a reference model for the retail industry in implementing CPFR in the future.

Since collaborative, planning, forecasting, and replenishment (CPFR) was first proposed in 1998, numerous studies have focused on exploring its implementation in retailing contexts. While a considerable body of research has emphasized reduced costs, increased sales and improved forecasting ability, there has been a lack of research on the importance of each of the various factors which affect such implementations. In order to find out the critical success factors affecting CPFR implementation, this paper first collected related influence factors regarding adopting CPFR or business to business (B2B) information systems, and further constructed a factor table with a three-layer hierarchical structure. A pair wise analytic hierarchy process (AHP) questionnaire was designed and distributed to experts who were familiar with implementing CPFR in the retailing industry. After questionnaires were returned, we found out the weights of each impact factor by using a fuzzy analytic hierarchy process (fuzzy AHP) approach. The importance of each critical impact factor was investigated, and the paths of the critical success factors were also analyzed. The results of this study can provide more precise information with regard to allocating optimal resources for retailers implementing CPFR.

PurposeTo present a three‐layer hierarchical structure of the factors involved in adopting an electronic marketplace (EM) model and to examine the relative weightings given to various strategic factors by the securities industry (SI) and the heavy electric machinery industry in Taiwan.Design/methodology/approachA literature review and a review of nine Taiwanese industries allow the formulation of a three‐layer hierarchical structure of adoption factors. A fuzzy analytic hierarchical process (AHP) is then undertaken to ascertain the relative weightings of factors that affect entry to an EM in two of these industries which are studied in more detail.FindingsThe weights of “proactive” factors are found to be greater than those of “defensive” factors. For example, contrary to previous findings in this area, the “risk of adopting new technology” is not found to be the major factor influencing decision making. Various factors are found to have different routes of influence in determining decision making in different industries.Practical implicationsEnterprises that appreciate the weightings of factors to be considered will be able to facilitate the adoption of an EM model with lower costs and greater efficiency.Originality/valueThe study provides novel and reliable information about strategic factors that are involved in corporate decisions about entering an EM.

No abstract