Abstract
Marketers in firms that routinely produce high-tech innovations want rapid adoption of their products. Many believe the consumer segment that is targeted initially should consist of people who adopt innovative technology relatively early and are influential sources of information that others use as references for their own behavior. A set of adopters who might meet these requirements, but have not been the focus of prior scholarly research, are gadget lovers. This article provides insights into this segment, proposes a scale to measure its key characteristics, and reports the results of a group interview and four additional studies that support the validity of the scale (n 1 = 1,655, n 2 = 789, n 3 = 1,366, and n 4 = 188). The gadget lover scale explains adoption-related behaviors beyond the variance accounted for by technological innovativeness and key demographic variables.
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Notes
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We knew about the gadget-related interests and purchases of the students from conversations with them in and/or outside the classroom prior to the focus group session.
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For Studies 1, 2, and 3, we worked with a corporate partner to gather data. That partner in turn hired a well-known research firm to collect the data. The client agreed to pay for a target number of completed online questionnaires, after which point the data collection halted. Thus, we have no way to determine the actual response rate. With regard to the mail survey portion of Study 1, 1,600 surveys were mailed out, and 624 completed forms were returned, yielding a response rate of 41.6%.
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We determined groups on the basis of percentages derived from the literature. The best known split comes from Rogers’s work, in which he defines innovators as the top 2.5% of adopters (2003, p. 281). Using that exact figure, however, would have left us with very few respondents on whom to run tests and draw conclusions, especially for Study 2, because the total sample included only 789 respondents. Therefore, we use the slightly larger measure of the 95th percentile.
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For verification, we also used a seven-item GL scale and repeated all analyses in Studies 1 and 2. We obtained the same results as when we used the eight-item scale.
References
Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423.
Ballard, R., Crino, M. D., & Rubenfeld, S. (1988). Social desirability response bias and the Marlowe–Crowne social desirability scale. Psychological Reports, 63, 227–237.
Bentler, P. M. (1998). EQS for windows (version 5.7). Encino, CA: Multivariate Software.
Bloch, P. H., Sherrell, D. L., & Ridgway, N. M. (1986). Consumer search: An extended framework. Journal of Consumer Research, 13, 119–126.
Dabholkar, P. A., & Bagozzi, R. P. (2002). An attitudinal model of technology-based self-service: Moderating effects of consumer traits and situational factors. Journal of the Academy of Marketing Science, 30(3), 184–201.
Flynn, L. R., Goldsmith, R. E., & Eastman, J. K. (1996). Opinion leaders and opinion seekers: Two new measurement scales. Journal of the Academy of Marketing Science, 24, 137–147.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39–50.
Goldsmith, R. E., Freiden, J. B., & Eastman, J. K. (1995). The generality/specificity issue in consumer innovativeness research. Technovation, 15(10), 601–613.
Goldsmith, R. E., & Hofacker, C. F. (1991). Measuring consumer innovativeness. Journal of the Academy of Marketing Science, 19(3), 209–221.
Horrigan, J. B. (2003). Consumption of information goods and services in the United States. Washington, DC: Pew Internet & American Life Project.
McLuhan, M. (1964). Understanding media. New York: McGraw-Hill.
Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. Cambridge, MA: The MIT Press.
Midgley, D. F., & Dowling, G. R. (1993). A longitudinal study of product form innovation: The interaction between predispositions and social messages. Journal of Consumer Research, 19, 611–625.
Netemeyer, R. G., Bearden, W. O., & Sharma, S. (2003). Scaling procedures: Issues and applications. Newbury Park, CA: Sage Publications.
Parasuraman, A. (2000). Technology readiness index (TRI). Journal of Services Marketing, 2, 307–320.
Parasuraman, A., & Colby, C. L. (2001). Techno-ready marketing: How and why your customers adopt technology. New York: The Free Press.
Richins, M. L., & Dawson, S. (1992). A consumer values orientation for materialism and its measurement: Scale development and validation. Journal of Consumer Research, 19, 303–316.
Ridgway, N. M., & Price, L. L. (1994). Exploration in product usage: A model of use innovativeness. Psychology & Marketing, 11(1), 69–84.
Rogers, E. M. (2003). Diffusion of innovations. New York: The Free Press.
Scheier, M. F., Carver, C. S., & Bridges, M. W. (1994). Distinguishing optimism from neuroticism (and trait anxiety, self-mastery, and self-esteem): A reevaluation of the life orientation test. Journal of Personality and Social Psychology, 67(6), 1063–1078.
Valente, T. W., & Davis, R. L. (1999). Accelerating the diffusion of innovations using opinion leaders. Annals of the American Academy of Political and Social Science, 566, 55–67.
Acknowledgement
The authors express their appreciation to Clyde Heppner and Sprint for their generous support of studies 1–3. The second author also thanks Gary Gebhardt and participants of the USF Marketing Department Research Seminar Series for their feedback. A longer version of this article, containing scale norms as well as additional validation and discussion, is available by contacting either author.
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Appendix
Appendix
Samples for studies 1, 2, and 3 (National samples)
Demographic variable |
Study 1 (n = 1655) (%) |
Study 2 (n = 789) (%) |
Study 3 (n = 1366) (%) |
|---|---|---|---|
Age |
|||
<30 years |
11.4 |
9.3 |
27.2 |
30–44 years |
40.0 |
41.2 |
42.8 |
45–59 years |
38.7 |
40.3 |
24.2 |
≥60 years |
10.0 |
9.3 |
5.9 |
Gender |
|||
Male |
50.0 |
50.6 |
71.6 |
Female |
50.0 |
49.4 |
28.4 |
Marital status |
|||
Married |
76.3 |
80.0 |
55.8 |
Single |
13.3 |
10.7 |
34.9 |
Other |
10.3 |
9.2 |
9.3 |
Education |
|||
High school or less |
15.9 |
15.9 |
9.4 |
Some college |
31.6 |
31.2 |
27.6 |
College degree |
27.0 |
29.3 |
31.5 |
Postgraduate |
20.1 |
18.2 |
27.6 |
Tech/trade school |
5.4 |
5.4 |
4.0 |
Employment |
|||
Full-time |
66.0 |
63.2 |
81.9 |
Part-time |
11.8 |
13.6 |
8.4 |
Not employed |
22.1 |
23.1 |
9.8 |
Household incomea |
|||
<$20,000 |
9.2 |
10.5 |
– |
$20,000–$34,999 |
14.6 |
12.8 |
– |
$35,000–$54,999 |
20.3 |
15.1 |
– |
$55,000–$84,999 |
26.2 |
25.2 |
– |
≥$85,000 |
29.7 |
36.4 |
– |
Ethnicity |
|||
White (non-Hispanic) |
92.9 |
91.4 |
76.1 |
African-American |
2.2 |
1.9 |
9.3 |
Hispanic |
1.6 |
2.5 |
5.4 |
Asian |
1.7 |
3.3 |
6.1 |
Other |
1.4 |
0.9 |
3.1 |
Samples for studies 4a and 4b (student samples)
Demographic variable |
Study 4a (n = 260) (%) |
Study 4b (n = 188) (%) |
|---|---|---|
Age |
||
≤25 years |
88.2 |
91.4 |
26–35 years |
7.8 |
5.9 |
≥36 years |
3.9 |
2.7 |
Gender |
||
Male |
47.4 |
57.0 |
Female |
52.6 |
43.0 |
Marital status |
||
Married |
94.3 |
96.2 |
Single |
5.7 |
3.8 |
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Bruner, G.C., Kumar, A. Gadget lovers. J. of the Acad. Mark. Sci. 35, 329–339 (2007). https://doi.org/10.1007/s11747-007-0051-3
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DOI: https://doi.org/10.1007/s11747-007-0051-3