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Submitted as partial fulfillment of the requirements for MKT 497: Marketing Research
Wednesday, 30 November 2011

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:
1.
What influence do demographic factors have upon e-commerce intentions?
2.
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.

Figure 1
A Model of E-Commerce Client Behavior
Technophobia
-
Mental
Intangibility
- E-Privacy
Concerns
-
-
Trust Impulse Buying
Tendency
+
+
Intentions
Another model construct is Mental Intangibility, which represents the lack of clarity in one’s mind that an individual has of an item or concept. Laroche, Bergeron, and Goutaland
(2001), found that as the degree of mental intangibility associated with a product increases, customers are less likely to trust and ultimately purchase the product. A third model construct is
Impulse Buying Tendency. IBT has been defined as “the degree to which an individual is likely to make unintended, immediate, and unreflective purchases (i.e., impulse purchases)” (Weun,
Jones, and Beatty, 1997, p. 306). It is possible, given the relative lack of physical controls, such as tangible goods filling the shopping cart, that impulse buyers may be more likely to engage in e-commerce transactions than non-impulse buyers.
Pan and Zinkhan (2006) were successful in demonstrating that on-line consumers are more likely to trust e-tailers that have clearly stated privacy policies. This lends support to the
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idea that privacy concerns are one of the most important factors limiting the growth of ecommerce. 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:
H#
H1
H2
H3
Hypothesis
No Age difference exists between females and males.
No Workforce Years difference exists between females and males.
Gender does not influence any psychological variable.
H4
H5
No Age difference exists based on customer status.
No Workforce Years difference exists based on customer status.
H6 Workforce Years does not influence any psychological variable.
H6a Customer Status does not influence any psychological variable.
H7
H8
Younger customers are more impulsive buyers than older ones.
Workforce years negatively impacts impulse buying tendencies.
H9 Older customers have higher e-privacy concerns.
H10 Customers with more workforce years have more e-privacy concerns.
H11 Mental intangibility negatively impacts e-privacy concerns.
H12 Older customers have higher technophobia.
H13 Customers with more workforce years are more technophobic.
H14 Mental intangibility positively impacts technophobia.
H15 Younger customers are more likely to trust e-commerce.
H16 Customers with more workforce years less likely to trust e-commerce.
H17 Technophobia negatively impacts trust.
H18 Mental intangibility negatively impacts trust.
H19 Trusting disposition positively impacts trust.
H20 Impulse buying tendency positively impacts trust.
H21 Younger customers are more likely to buy online.
H22 Customers with more workforce years less likely to buy online.
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H23 Technophobia negatively impacts the likelihood of buying online.
H24 Mental intangibility negatively impacts the likelihood of buying online.
H25 Trusting disposition positively impacts the likelihood of buying online.
H26 Impulse buying tendency positively impacts the likelihood of buying online.
H27 Trust positively impacts the likelihood of buying online.
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.
Each of the scales used to measure the psychological constructs, or variables, had been previously developed, tested, and validated in prior studies, with the exception of E-Privacy
Concerns. A list of the scales employed, and their sources, may be seen below.
Scale
Technophobia
Mental Intangibility
Source
Sinkovics, Stöttinger, Schlegelmilch, and Ram (2002)
Laroche, Bergeron, and Goutaland (2001)
Impulse Buying Tendency Weun, Jones, and Beatty (1997)
Trust
Intentions
Plank, Reid, and Pullins (1999)
Gefen and Straub (2004)
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Collecting the Data, Checking the Data for Accuracy, and Data Entry
Data was collected on the JSU campus during the Spring 2007 semester by students in
Dr. Thomas’ MKT 497: Marketing Research class. Students in the class were required to recruit and interview 15 current JSU students and non-students (at least 5 non-students, but no more than 7). Therefore, the study relied upon self-reported data, though the use of face-to-face interviews did assist in reducing the item non-response problems inherent with such data. Once the data was collected, Dr. Thomas’ checked the questionnaires for accuracy and entered the data using the process delineated below.
1.
Numbered each questionnaire (total of 404 completed questionnaires)
2.
Checked each questionnaire for missing or contradictory data
3.
Coded missing data as “9999”
4.
Defined SPSS database (please refer to attached Database Key)
A.
Created a new variable (Respondent Number)
B.
Assigned a name (SPSS limits the variable name to no more than 8 characters) and label for each question (variable)
C.
Reset decimal places for all variables from 2 to 0
D.
Coded the one Nominal variable as Gender. Coded the Ordinal variables as
Year of Birth, Student Status, Full-Time Work Status, Have Ever Worked
Status, Primary Work Role, Most Current Work Industry, Ethnicity, and
Greatest Amount Ever Spent E-tailing.
E.
All other variables coded as Scale (the default setting)
F.
For each Nominal/Ordinal variable, defined the value labels as appropriate for the particular question
G.
Reset “Missing” column for each variable from None to 9999
5.
Switched from Variable view to Data view
6.
Entered the data
7.
Ran Frequency Distribution on all variables. Examined the distributions for any 2digit entries. No 2-digit entries occurred.
8.
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.
9.
Reverse-scored questions as necessary.
10.
Using the Year questions (question # 2), created a new variable, “Age.” Then, deleted the original Year variable from the dataset.
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11.
Deleted the “Respondent” variable.
Sample Size and Description
IDENTIFY THE ACTUAL SAMPLE SIZE ACHIEVED.
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 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 Hypothesis 1, which stated that, “no age difference exists between females and males.” Churchill’s (1979) standard of a significance level less than or equal to 0.05 was used as a measure of whether a difference exists between the groups. Using this standard, ANOVA revealed no statistically significant difference between females and males on age. Hence, the decision must be made to fail to reject Hypothesis 1.
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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 age. This means that females in the sample were no older, nor no younger than the males in the sample.
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 in the sample were statistically the same in terms of age. Most prior research reveals that the decision to be an e-commerce customer is generally influenced by age. Given the findings of the current study combined with prior research findings, an internet marketer very likely should segment the market based on age. However, it would be inappropriate further segment the age groups based upon gender. In other words, internet marketers should not segment the market into older males, older females, younger males, younger females, etc. An age-based segmentation approach should be sufficient.
Limitations
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IDENTIFY AND DESCRIBE AT LEAST 3 LIMITATIONS ASSOCIATED WITH THIS
STUDY. BE SURE TO EXPLAIN WHY EACH ONE IS ACTUALLY A LIMITATION. AN
EXAMPLE MAY BE SEEN BELOW.
One limitation of this study is that the data was collected by college students. While the students had been trained in how to conduct the interviews, their level of experience in doing so was low. This may have unintentionally introduced interviewer error into the results.
References
Churchill, Gilbert A. (1979), "A Paradigm for Developing Better Measures of Marketing
Constructs,”
Journal of Marketing Research , 16 (February), 64-73.
Cronbach, L.J. (1951), “Coefficient Alpha and Internal Structure of Tests,” Psychometrika,
16 (September), 297-334.
Gefen, David and Detmar W. Straub (2004), “Consumer Trust in B2C e-Commerce and the importance of social presence: experiments in e-Products and e-Services,” Omega: The
International Journal of Management Science , 32, 407-424.
Laroche, Michel, Jasmin Bergeron, and Christine Goutaland (2001), “A three-dimensional scale of intangibility,”
Journal of Service Research , 4(1), 26-38.
Laroche, Michel, Jasmin Bergeron, and Christine Goutaland (2003), “How intangibility affects perceived risk: the moderating role of knowledge and involvement,”
Journal of Services
Marketing , 17(2), 122-140.
Nunnally, Jum C. (1978), Psychometric Theory , New York: McGraw-Hill.
Pan, Yue and George M. Zinkhan (2006), “Exploring the impact of online privacy disclosures on 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,”
Management
,” XIX(3), 61-71.
Journal of Personal Selling & Sales
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.
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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.
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APPENDIX
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INSERT YOUR ANNOTATED QUESTIONNAIRE HERE. IF YOU DID NOT ADD YOUR
DATA TABLES TO THE END OF YOUR ANNOTATED QUESTIONNAIRE, INSERT
THEM INTO THIS DOCUMENT IMMEDIATELY AFTER THE ANNOTATED
QUESTIONNAIRE.
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