11,112 research outputs found

    Critical Factors of the Buyer Decision Process Model in Business-to-Customer (B2C) E-Commerce in Taiwan

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    The purpose of this study is to identify the critical factors involved in each stage of the Buyer Decision Process model, developed by Kotler and Armstrong (1997), as this study relates to online retail shopping in the country of Taiwan. This study explored whether and to what extent these factors influence consumers making online purchase decisions. The Buyer Decision Process model consists of five stages: (a) need recognition; (b) information search; (c) alternatives evaluation; (d) purchase decision; and (e) post-purchase decision. This research study attempted to design a framework based on this model to explore the perceived consumer value of online purchase through the entire consumption process in a B2C e-commerce setting in the country of Taiwan. There are many problems in this research area, such as: (a) B2C e-commerce is very competitive; (b) online shoppers have different characteristics from traditional shoppers; (c) most B2C Websites are ignored by Internet users; and (d) online shoppers are unable to touch, feel, or see real products to evaluate quality. Therefore, how to attract worldwide potential customers to Websites is a challenge for global e-retailers; and how to analyze and understand consumer preferences is a challenge for global e-retailers in the fast-changing digital marketing as well. There are five research questions in this research study, based on the five stages of the Buyer Decision Process model to measure consumer online behavior in Taiwan. In order to answer the five research questions, the researcher identified 14 critical factors for consumer online purchase decisions based on the five stages. These critical factors include: Free Trials, Internet Advertisements, Search Engines, Online Shopping Malls, Auction Websites, Convenience, Price, Brand, Security, Promotion, Refund, Satisfaction, Customized Information, and Discount. In general, the study results supported the inference of relationships between the 14 critical factors and Internet users\u27 receptivity to online shopping, with Satisfaction ranking first, Online Shopping Malls ranking second, and Convenience ranking third

    Intelligent Agent Based Approach to Sales Operations at eStores

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    In our paper, we consider the application of Intelligent Agents in supporting the operations in an Internet-based store (estore). We consider and discuss different opportunities for employing Intelligent Agents to improve the performance of an estore\u27s operations, including sales, forecasting demand and supporting order fulfillment. We provide a framework for the application of such agents, show available sources of information, and discuss challenging issues in modeling learning and decision processes for agents

    Detecting Structure In Chaos: A Customer Process Analysis Method

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    Detecting typical patterns in customer processes is the precondition for gaining an understanding about customer issues and needs in the course of performing their processes. Such insights can be translated into customer-centric service offerings that provide added value by enabling customers to reach their process objectives more effectively and rapidly, and with less effort. However, customer processes performed in less restrictive environments are extremely heterogeneous, which makes them difficult to analyse. Current approaches deal with this issue by considering customer processes in large scope and low detail, or vice versa. However, both views are required to understand customer processes comprehensively. Therefore, we present a novel customer process analysis method capable of detecting the hidden activity-cluster structure of customer processes. Consequently, both the detailed level of process activities and the aggregated cluster level are available for customer process analysis, which increases the chances of detecting patterns in these heterogeneous processes. We apply the method to two datasets and evaluate the results’ validity and utility. Moreover, we demonstrate that the method outperforms alternative solution technologies. Finally, we provide new insights into customer process theory

    The Impact of Servicescape Perception on Perceived E-Commerce Value and Client Loyalty

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    While previous research on e-servicescapes has focused on the ordinary Internet user, several studies show that heavy Internet users are the target audience. To maximize firm profitability, it is vital to understand the nature of heavy user consumption; hence, this study examines the primary components of e-servicescapes and their relationship to buy intent using moderated data from heavy and light Internet users. Three hundred and forty-two genuine internet users with online purchasing experience answered an online questionnaire, and discrepancies were determined using structural equation modeling. For ordinary users, aesthetic appeal and interaction are significant factors in purchase intention; for heavy users, interactivity is the most important attribute, followed by aesthetic appeal, layout, and functionality; and for light users, aesthetic appeal is the sole consideration. Additionally, our data show that financial stability does not help heavy, regular, or light users. We demonstrate how heavy and light Internet users evaluate e-servicescapes to signal quality attributes and contribute to their cognitive responses and purchase intentions based on their consumption traits by integrating purchase intentions with e-service quality and segmentation theory in e-servicescapes. It is advised that online merchants identify heavy and light users, rethink their current e-servicescapes, and apply more tailored marketing methods to attract and retain heavy and light users, as well as increase their purchase intent. While this study concentrated on the most salient characteristics of heavy users, more research is required to explicate additional critical mediators. This poll makes no mention of the three kinds of websites or product qualities. Finally, demographic and psychological variables such as gender and personal characteristics may act as significant mediators in the link between the e-servicescape and purchase intention, but their relevance requires more research

    A Survey of Sequential Pattern Based E-Commerce Recommendation Systems

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    E-commerce recommendation systems usually deal with massive customer sequential databases, such as historical purchase or click stream sequences. Recommendation systems’ accuracy can be improved if complex sequential patterns of user purchase behavior are learned by integrating sequential patterns of customer clicks and/or purchases into the user–item rating matrix input of collaborative filtering. This review focuses on algorithms of existing E-commerce recommendation systems that are sequential pattern-based. It provides a comprehensive and comparative performance analysis of these systems, exposing their methodologies, achievements, limitations, and potential for solving more important problems in this domain. The review shows that integrating sequential pattern mining of historical purchase and/or click sequences into a user–item matrix for collaborative filtering can (i) improve recommendation accuracy, (ii) reduce user–item rating data sparsity, (iii) increase the novelty rate of recommendations, and (iv) improve the scalability of recommendation systems

    Making and Evaluating Participant Choice in Experimental Research on Information Technology: A Framework and Assessment

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    Evaluations of participant samples for experiments in information systems research often appear to be informal and intuitive. Appropriate participant choice becomes a more salient issue as the population of information technology professionals and users grows increasingly diverse, and the distribution of relevant characteristics in participant samples such as age, gender, nationality, and experience can often be unrepresentative of the characteristics’ distribution in target populations. In this paper, we present a framework based on widely accepted standards for evaluating participant choice and providing rationale that the choice is appropriate. Using a step-by-step approach, we compare current practice in experimental studies from top information systems journals to this framework. Based on this comparison, we recommend how to improve the treatment of participant choice when evaluating the validity of study inferences and how to discuss the tradeoffs involved in choosing participant samples

    Incorporating Profit Margins into Recommender Systems: A Randomized Field Experiment of Purchasing Behavior and Consumer Trust

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    A number of recent studies have proposed new recommender designs that incorporate firm-centric measures (e.g., the profit margins of products) along with consumer-centric measures (e.g., relevance of recommended products). These designs seek to maximize the long-term profits from recommender deployment without compromising customer trust. However, very little is known about how consumers might respond to recommender algorithms that account for product profitability. We tested the impact of deploying a profit-based recommender on its precision and usage, as well as customer purchasing and trust, with data from an online randomized field experiment. We found that the profit-based algorithm, despite potential concerns about its negative impact on consumers, is effective in retaining consumers’ usage and purchase levels at the same rate as a content-based recommender. We also found that the profit-based algorithm generated higher profits for the firm. Further, to measure trust, we issued a post-experiment survey to participants in the experiment; we found there were no significant differences in trust across treatment. We related the survey results to the accuracy and diversity of recommendations and found that accuracy and diversity were both positively and significantly related to trust. The study has broader implications for firms using recommenders as a marketing tool, in that the approach successfully addresses the relevance-profit tradeoff in a real-world context

    An Examination of a Multidimensional Model of Customer Satisfaction with Internet Purchasing

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    The World Wide Web and Internet have transformed the competitive business environment and altered the customer-firm relationship by creating a new retailing format and service enterprise. It is rapidly growing as a competitive distribution medium in which customer satisfaction will be a major success factor in the development and maintenance of this new retailing format. Despite its growing importance as a new shopping medium, little empirical research has been conducted that examines the relationship between Internet shopping, customer satisfaction, company image, and future online purchasing. Research is needed to develop theoretical models that will systematically explain and predict behavior related to Internet shopping. The purpose of this dissertation research was to examine how consumers become satisfied with an Internet purchasing experience, how company image is impacted by the shopping experience, and how satisfaction and company image affect future purchase behavior. Specifically, the constructs of information quality, ease of use, value, and expectation congruency were examined to determine their influence on satisfaction and company image in the context of shopping over the Internet. In order to assess the various relationships that exist in the proposed model of customer satisfaction with Internet purchasing, a structural modeling approach was employed. In addition, analysis of variance test of significance was conducted to determine if there were any differences in the mean ratings of satisfaction with an Internet purchase among different groups of consumers. Overall, the results of testing the model in this study support the assertion that a positive and direct relationship exists between customer satisfaction and the intention to continue shopping at a firm\u27s Web site. The results also provide evidence for the factors that significantly influence satisfaction with online shopping. Economic value and ease of use were found to have a positive and direct effect on consumer satisfaction with an Internet purchasing experience. These findings may be important for marketing managers because they can provide guidelines for planning Internet strategies to develop customer satisfaction and maintain customer loyalty. A positive and direct effect between company image and consumers\u27 desires to continue shopping on the firm\u27s Web site was also statistically supported by the data. The factors found to influence a positive company image after shopping at a firm\u27s Web site are ease of use and economic value. The results of the study also revealed that expectations and frequency of Internet shopping affected consumer\u27s ratings of satisfaction. The findings from this study may provide future researchers with evidence to expand their understanding of how the electronic retail medium of the Internet impacts the customer-firm relationship. In summary, this study provides empirical support for the factors that influence satisfaction with an Internet shopping experience, company image, and future purchasing behavior from a firm\u27s Web site

    Federal Practice and Procedure

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