Factors That Influence E-Commerce Purchase Intentions
An Abbreviated Report
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Submitted as partial fulfillment of the requirements for MKT 497: Marketing Research
Tuesday, December 9, 2008
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Factors Influencing E-Commerce Purchase Intentions: An Abbreviated Report - Prof. James L., Exams of Marketing Research
An abbreviated report on a study conducted at jacksonville state university to examine the factors that influence individuals' intentions to engage in electronic commerce (e-commerce). The study focused on constructs such as technophobia, mental intangibility, e-privacy concerns, trust, and intentions, and hypotheses were tested to determine the impact of demographic and psychographic factors on e-commerce intentions.
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Factors That Influence E-Commerce Purchase Intentions
An Abbreviated Report
INSERT YOUR NAME HERE
Submitted as partial fulfillment of the requirements for MKT 497: Marketing Research Tuesday, December 9, 2008
Introduction As part of the course requirements for MKT 497: Marketing Research at Jacksonville State University, a study was conducted with JSU students and individuals who were not JSU students in the Spring 2007 semester to examine factors that influence individuals’ intentions to engage in electronic commerce (e-commerce). Students in the class conducted face-to-face intercept interviews with potential respondents; as well as analyzing and reporting the results. This document constitutes an abbreviated report of the results of the described study. Purpose of the Study The management question being addressed by the study is, “what factors influence consumers in their decision to purchase e-commerce products?” This question may be addressed with the following research questions:
- What influence do demographic factors have upon e-commerce intentions?
- What influence do psychographic factors have upon e-commerce intentions? A theoretical framework is necessary in order to address these research questions. Based on a variety of previous studies that relate to the topic at-hand, the theoretical framework, or model, depicted in Figure 1 may be used as an approach to answering the research questions. One of the constructs in the model, Technophobia, relates to an individual’s unease with or reluctance to use technology and technologically-based products. Sinkovics, Stöttinger, Schlegelmilch, and Ram (2002), in their development of a scale to measure technophobia, found that people who suffer higher levels of technophobia are less likely to use technology or to purchase technologically-intensive products.
idea that privacy concerns are one of the most important factors limiting the growth of e- commerce. However, no systematic data exists that examines the extent to which consumers address these concerns by taking actions to protect themselves. Thus, a new measure, E-Privacy Concerns, will be used to examine how consumers actually do protect their online privacy. The final two constructs in the model are Trust and Intentions. Specifically, trust relates the degree to which the customer trusts e-commerce and intentions simply are whether or not the customer believes he/she will participate in an e-commerce transaction in the future. Gefen and Straub (2004) found that as customers’ trust in electronic products increases, their intentions to purchase the products in the future increase. Given this model and the discussion of the constructs, the following hypotheses will be tested in this study: # Hypothesis H1 Gender does not influence E-Privacy Concerns. H2 Gender does not influence Mental Intangibility. H3 Females are more trusting than Males. H4 Gender does not influence Trust in E-Commerce. H5 Gender does not influence Technophobia. H6 Females are more impulsive buyers than Males. H7 Non-Customers have greater E-Privacy Concerns than existing Customers. H8 Non-Customers do not have as clear a mental image of E-Commerce as Customers. H9 Customers Status does not influence Trusting Disposition. H10 Existing Customers Trust E-Commerce more than Non-Customers. H11 Non-Customers are more Technophobic than existing Customers. H12 Existing Customers are more impulsive buyers than Non-Customers. H13 Older consumers are less likely to buy on-line than younger consumers. H14 Consumers with more years in the workforce are less likely to buy on-line.
H15 E-Privacy Concerns negatively impact Intentions. H16 Trusting Disposition positively impacts Intentions. H17 E-Trust positively impacts Intentions. H18 Mental Intangibility negatively impact Intentions. H19 Technophobia negatively impact Intentions. H20 Impulse Buying Tendency positively impact Intentions. H21 Age negatively impacts E-Trust. H22 Technophobia negatively impacts E-Trust. H23 Mental Intangibility negatively impacts E-Trust. H24 Impulse Buying Tendency positively impacts E-Trust. H25 E-Privacy Concerns negatively impacts E-Trust. Methodology The Data Instrument In order to collect the data necessary to test the hypotheses, a questionnaire was developed that, in addition to a cover page that explained the purpose of the study, consisted of three major sections. The first section collected basic demographic information about the respondents. The second section of the questionnaire measured E-Privacy Concerns and the outcome of a serious problem the respondent had suffered with e-commerce transactions. The final section of the questionnaire measured the psychological variables Mental Intangibility, Trusting Disposition, E-Commerce Trust, Technophobia, Intentions, and Impulse Buying Tendency. Multi-item scales were employed since each of the psychological constructs was highly abstract in nature. An annotated version of the questionnaire may be seen in the Appendix to this report.
- Switched from Variable view to Data view
- Entered the data
- Ran Frequency Distribution on all variables. Examined the distributions for any 2- digit entries. No 2-digit entries occurred.
- Used Microsoft Excel’s random number generator to generate a list of 41 numbers between 1 and 404. Questionnaires with these randomly generated numbers were checked on each data entry point for data entry accuracy. No data entry errors were discovered; therefore, the assumption was made that the accuracy of the data entry could be trusted.
- Reverse-scored questions as necessary.
- Using the Year questions (question # 2), created a new variable, “Age.” Then, deleted the original Year variable from the dataset.
- Deleted the “Respondent” variable. Sample Size and Description IDENTIFY THE ACTUAL SAMPLE SIZE ACHIEVED. THEN, MENTION 3 CHARACTERISTICS OF THE SAMPLE (SELECT WHICHEVER ONES YOU THINK ARE APPROPRIATE OR NEEDED), THEN REFER YOUR READER TO THE ANNOTATED QUESTIONNAIRE IN THE APPENDIX FOR A FULL DESCRIPTION OF THE SAMPLE. Results BRIEFLY DESCRIBE YOUR CONSTRUCTION OF THE ANNOTATED QUESTIONNAIRE. DESCRIBE HOW THE SCALES WERE CHECKED (I.E., THE RELIABILITY ASSESSMENT). DON’T FORGET TO IDENTIFY THE STANDARD USED. DESCRIBE THE DEVELOPMENT OF THE SUMMATED, STANDARDIZED VARIABLES.
DESCRIBE THE TESTING OF EACH OF THE HYPOTHESES. DO NOT INTERPRET THE
FINDINGS AT THIS POINT. AN EXAMPLE MAY BE SEEN BELOW.
ANOVA was used to test Hypotheses 1, which stated that, “Gender does not influence E- Privacy Concerns.” Churchill’s (1979) standard of a significance level less than or equal to 0. was used as a measure of whether a difference exists between the groups. Using this standard, ANOVA revealed no statistically significant differences between females and males on E- Privacy Concerns. Hence, the decision must be made to fail to reject Hypotheses 1. Conclusions BASED ON YOUR FINDINGS IN THE RESULT SECTION, DRAW CONCLUSIONS ABOUT WHAT THEY MEAN. AN EXAMPLE MAY BE SEEN BELOW. In the testing of the first hypothesis, no statistically significant difference was discovered between females and males on E-Privacy Concerns. This means that the decision to be customer of e-commerce products, at least as it relates to these this variable, is not influenced by a person’s gender. Recommendations BASED ON YOUR CONCLUSIONS, USE YOUR KNOWLEDGE OF MARKETING TO MAKE RECOMMENDATIONS TO M-COMMERCE MARKETING MANAGERS. IN OTHER WORDS, TELL THEM WHAT THEY SHOULD DO, GIVEN YOUR CONCLUSIONS. AN EXAMPLE MAY BE SEEN BELOW. It was found that females and males do not differ in terms of their concerns about E- Privacy. Thus, it would be inappropriate to develop marketing programs that are targeted
consumer trust,” Journal of Retailing, 82 (4), 331-338. Plank, Richard E., David A. Reid, and Ellen Bolman Pullins (1999), “Perceived Trust in Business-to-Business Sales: A New Measure,” Journal of Personal Selling & Sales Management ,” XIX(3), 61-71. Sinkovics, Rudolf R., Barbara Stöttinger, Bodo B. Schlegelmilch, and Sundaresan Ram (2002), “Reluctance to Use Technology-Related Products: Development of a Technophobia Scale,” Thunderbird International Business Review , 44(4), 477-494. Stone, Robert N. and Kjell Grønhaug (1993), “Perceived Risk: Further Considerations for the Marketing Discipline,” European Journal of Marketing , 27(3), 39-50. Weun, Seungood, Michael A. Jones, and Sharon E. Beatty (1997), “A Parsimonious Scale to Measure Impulse Buying Tendency,” In W.M. Pride and G.T. Hult (Eds.), AMA Educator’s Proceedings: Enhancing Knowledge Development in Marketing , Chicago: American Marketing Association, 306-307.