All posts by Richard Thripp

UCF student in the Education Ph.D. program. 25-year-old photographer, writer, and pianist.

Critique of “The buying impulse” by Rook (1987)

This is a critique of a qualitative empirical study by Rook (1987) that I wrote on 2017-10-18 for the class, EDF 7475: Qualitative Research in Education taught by David Boote, Ph.D. at University of Central Florida.

EDF 7475 Article Critique Two
Richard Thripp
University of Central Florida

Article Citation

Rook, D. W. (1987). The buying impulse. Journal of Consumer Research, 14, 189–199. https://doi.org/10.1086/209105

Summary

Rook (1987), working as a research associate for a large advertising agency, asked participants (n = 133; mainly college students) three retrospective, open-ended questions about the “sudden urge to buy something” (p. 192), which asked participants to articulate the origins of the impulses, recall in-the-moment feelings, and describe detrimental consequences of impulse shopping. Rook criticizes past literature for characterizing impulse shopping merely as “unplanned purchasing”; proposes a new definition recognizing the overwhelming urge to buy, hedonic complexity and “emotional conflict” (p. 191); and justifies this exploratory study as addressing a literature gap, a need for “thicker description” (Geertz, 1973 as cited by Rook, 1987), and as a counterpoint to “excessive reductivism in behavioral research” (p. 192). In analyzing responses, themes emerged such as the “BUY NOW!” urge (p. 193), excitement, feelings of synchronicity, hedonism and regret, and, notably, 80% of participants detailing some sort of negative consequences.

Contribution to the Field

This is a staple citation in literature reviews on purchasing decisions and consumer behavior, perhaps because it refined impulse buying, phenomenologically examined the nature of buying impulses, and argued, armed with thick descriptions, that such impulses are fundamentally different from other purchasing behaviors. In fact, Rook is audacious enough to impugn past research as having “suffered from a phenomenological failure to identify what a buying impulse actually is” (p. 196). Rook’s paper is part of the zeitgeist of the emerging prominence of behavioral economics as a field of inquiry, along with Prospect Theory (Kahneman & Tversky, 1979) and papers like Thaler and Shefrin’s (1981) “An economic theory of self-control.” However, its contribution is hindered by (a) lack of methodological innovation, with an emphasis on self-completed written questionnaires, and (b) reliance on retrospective self-report, which is in conflict with behavioral economics’ contention that people behave irrationally and may not be able to describe, reflect on, or even notice this irrationality. Nevertheless, it was a starting point for further research by Rook and others on impulse buying, and, with over 2000 citations (according to Google Scholar), also had a broader impact on consumer research.

Strengths and Weaknesses

This paper had several methodological strengths, such as using quota sampling to sample across genders and age groups evenly, a rigorous content-analysis procedure with thematic coding requiring 1862 separate judgements for 14 coding categories from two graduate-student judges, and a design that allowed the 133 participants to freely reflect on their impulse buying. Ideally, this design might have been augmented by following a small number of participants to a mall, in a protocol analysis where they verbalized their cognitive processes while shopping. With only reflective questionnaires, both response and memory biases may have affected results.

Rook notes that half of participants self-completed the instrument in writing while half were interviewed, but modality is not mentioned regarding quotes and inferences in the qualitative narrative. Quotes from participants and the surrounding narrative are vivid, riveting, and insightful. However, with only eight headings in this section, it is not clear what the 14 coding categories were. A table with categories and percentages would be helpful, both in this section and the negative consequences section, the latter also being hindered by its surprising brevity. How the included quotes were selected is also not clarified. Nonetheless, Rook’s discussion section is pointed, addressing memory bias, situational factors, and his study’s methodological limitations. Rook even suggests that credit cards and 24-hour retailing may enable impulse buying, foreshadowing the “dark nudges” Sunstein and Thaler would warn of two decades later in Nudge.

Contribution to My Understanding

The data analysis section of this article showed me that a priori coding categories can justifiably be used, and that if I had 133 responses to analyze with an average of 181 words, which is as long as a dissertation, I would want to use software such as NVivo 11 to manage and graphically guide the coding procedure. Nonetheless, two graduate assistants, with enough time, can do this without software. Rook (1987) prominently features quotes and terms used by respondents, which lets their experiences speak for themselves, but is guided by the overall narrative of impulse buying being intense but bittersweet. Overall, before reading this I had discounted the value of reflective free-response interviews and questionnaires, but I learned they can be used to frame a problem, guide future research, gain insights, and argue a point, as Rook (1987) did with impulse buying. Therefore, I will consider this method as an alternative or supplement to a protocol analysis of individuals interacting with mobile banking interfaces. After reading this article, I learned more about behavioral economics from reading encyclopedia entries, book descriptions, and other studies, which contributes to my understanding of the psychological and irrational elements of consumer spending and financial decision-making. Methodologically, respect for allowing participants to respond uninterrupted—and to record and incorporate their writings or utterances verbatim—was evident throughout this paper and is something I need to work on, as I have a sort of compulsion to copy-edit and otherwise “clean up” others’ writing and speech.

High- vs. low-cognitive load work, environments, and mental fatigue

Some tasks simply require more cognitive load than others. Certain work environments are conducive to low-cognitive load (LCL) work while it would be foolish to attempt high-cognitive load (HCL) work in these environments. Further, one’s mental state and energy level (e.g., Staal, 2004) also matter. I am a person who best performs HCL work in complete silence, but might be more productive doing LCL work with music or a podcast playing. However, one’s energy level also matters. Pulling an all-nighter to complete an essay requiring HCL is generally a bad strategy, for instance.

High levels of mental fatigue increase the probability of errors and reduce efficiency when doing HCL work (Meijman, 1997). Lengthy work shifts with HCL work should be interrupted with frequent, short breaks (Boucsein & Thum, 1997). In fact, several short breaks are probably superior to one long break.

Applying this to the typical office worker’s workday and work-week, it becomes clear that HCL work should be scheduled early in the day, and perhaps, additionally, early in the week. For most people, this is when more cognitive load capacity is available. On the other hand, afternoons and Fridays should be geared toward LCL work because this is when many people are more fatigued, but, fortunately, LCL work is resilient toward mental fatigue.

For academics, this may also mean that office hours should be scheduled in the afternoons and, if possible, later in the week. Because there are many interruptions from students, phone calls, et cetera during office hours, one is not going to be getting much HCL work done anyway, but most student visits are LCL rather than HCL.

Further, this conceptual framework lends credence to the idea that email is a massive waste of time and probably shouldn’t even be looked at until late in the day (or, if you can get away with it, Friday afternoon). It also repudiates open-door office policies, at least with respect to getting any HCL work done.

We are at the stage where one’s physical environment is finally being recognized as important to cognitive load theory (Choi, van Merriënboer, & Paas, 2014). Beyond the cognitive load of the task at hand, one should think both about how one’s physical environment is organized and one’s level of mental fatigue. In fact, this should partly guide one’s schedule. For example, many people find that an early-morning workout leads to a more productive workday.

The consequences of one’s physical environment being important toward productivity are numerous and far-reaching. In fact, taking steps in advance to minimize distractions and temptations in one’s work environment is a piece of this puzzle. For instance, workers may be more productive if are militant about disabling or blocking smartphone notifications and installing browser add-ons such as Facebook News Feed Eradicator to impede viewing the Facebook news feed. More extreme solutions might be to physically sequester one’s smartphone and physically disconnect from the Internet for HCL work, in addition to doing this work with one’s door closed, early in the morning before the kids wake up, or late in the night for nightowls.

A full appreciation of cognitive load requires us to recognize the stark finiteness of our mental energy, and to appropriately limit our expectations and orient our lives toward what is most important. Being efficient at LCL tasks does not make one a leader nor innovator. HCL work is key, and it is facilitated by a streamlined work environment with respect for mental energy and cyclical rhythms. If I want to encourage myself to play piano, I don’t do this by leaving the piano lid closed or placing the piano bench on the opposite side of the room.

When I criticized University of Central Florida’s College of Education and Human Performance for not allowing student nor instructor PCs to display labels nor ungroup items on the Windows 10 taskbar (in June 2017 on my blog and again in September 2017 on Twitter), it was with an appreciation that if you want students and faculty to work better and get more done, you don’t force Microsoft’s stupid default taskbar window management settings on them. When doing HCL work, having your taskbar icons identified only by the programs’ icons—and grouped together so that one must hover and then choose the correct window—is inarguably an unwise imposition.

One might posit that some sort of invisible hand of market competitiveness might drive institutions and organizations toward providing workplace and learning environments conducive to HCL work. Neither in the “free” market nor the contrived worlds of academia, churches, NGOs, or governments do I see evidence of this. I suppose it might be true for small, hypercompetitive startup companies, but firms with a modicum of largess are inclined toward systemic dysfunction. For the gainfully employed, optimizing one’s schedule and work environment for HCL is, regretfully, frequently an exercise in futility.

Research Focus Statement for Verbal Protocol Analysis of Individuals Accessing Their Banking and Credit Accounts

This is an assignment I completed writing on 2017-10-03 for the class, EDF 7475: Qualitative Research in Education taught by David Boote, Ph.D. at University of Central Florida. During the remainder of the semester I will be further developing this proposal and think this is one I will actually conduct in some form.

EDF 7475 Research Focus Statement
Richard Thripp
University of Central Florida

My proposed study is primarily a verbal protocol analysis of how people interact with their bank and credit accounts from their mobile devices and/or home computers. The focus of my research will be on American adults who have web-accessible banking and/or credit accounts. The main purpose is to discover and explore how they interact with the web or mobile interfaces of their accounts when reviewing account activity, imagining future account activity, paying bills, and making transfers. This will be conducted via protocol analysis, a qualitative “think-aloud” methodology (Ericsson, 2006) that I will leverage to identify and document their thought processes as they engage or pretend to engage in these tasks. A second purpose is to elucidate the strategies individuals use for managing their day-to-day finances, including provoking them to reflect on recent spending. This will be situated within the literature on financial behaviors among low- and middle-income Americans.

I intend to use maximal variation sampling to focus, in particular, on at least one individual who is lackluster at managing his/her finances, one who is accomplished, and perhaps one who is average. Because participants will be sitting down with me to access their actual personal accounts, I anticipate the protocol analysis will have to be augmented with prompts to imagine conditions or scenarios that are not presently occurring for them (e.g., “imagine you just received your paycheck,” “imagine you are considering making a $500 purchase,” etc.). Further, they may be asked to verbally reflect on their recent financial behaviors, to reveal their financial strategies, or lack thereof.

Research Questions

My main research question is: What are individuals’ thought processes related to accessing, reviewing, anticipating, and initiating activity on their bank and credit accounts? Potentially, this will be augmented with the following sub-questions, although the data collected may not address all of them. All of these sub-questions are intended as advance organizers to guide my qualitative data collection and analysis with my purpose and main research question in mind.

1. How do they plan around periodic bills and income sources?
1a. Are their approaches different for fixed versus unpredictable amounts?
2. How do they avoid or cope with overdraft fees and other bank fees?
3. Do they approach debit and unsecured-credit spending differently?
3a. Do they use one payment method religiously, or a mix depending on the situation (e.g., cash, debit, credit, checks)?
4. How and why do they access account activity and periodic statements?
5. How do they account for nonperiodic debits that do not yet appear in posted or pending account activity (e.g., written checks not yet cashed, fuel purchases that place only a $1.00 hold and take several days to post)? This may differ for debit versus credit users.
6. For bills, do they prefer automatic or manually scheduled payments, and why?
7. What do they find frustrating about their financial institution(s) and banking interface(s)?
8. Do they treat some income differently than others (e.g., windfall bias)?
9. Do they segregate liquid monies into multiple deposit accounts and/or different types of deposit accounts (e.g., checking, savings, money market), and why?
9a. How do they handle moving money between accounts?
10. Are they satisfied with their spending decisions?

Significance

This study will contribute to research on financial literacy and behaviors, particularly with respect to consumer inattention (e.g., Grubb, 2015), payment methods and spending behavior (e.g., Soman, 2001), and checking overdraft fees (e.g., Stango & Zinman, 2009, 2014). There have been hundreds of attempts at financial education interventions, predicated on a wealth of survey data showing a lack of financial literacy in the United States, Europe, and beyond (Lusardi & Mitchell, 2014). Critically, educators and policymakers presume educational programs are efficacious, when in fact, they appear to yield negligible long-term effects. For a rigorous meta-analysis, see Frenandes, Lynch, and Netemeyer (2014), which found only 0.1% explanatory power across 201 studies.

The proposed verbal protocol analysis will qualitatively explore how individuals interact with and think about their bank and credit accounts. This approach is fundamentally different from typical questionnaire-based research, such as the Jump$tart Coalition’s Survey of Personal Financial Literacy Among Students or the FINRA Investor Education Foundation’s National Financial Capability Study. Consequently, it may be of both methodological and practice-oriented significant. Moreover, using maximal variation sampling will show how different types of people interact with bank accounts and other financial products—potentially, key differences between financial experts and novices may be revealed that could be leveraged to improve financial education and credit counseling practices.

Preliminary Source List

Campbell, J. Y. (2006). Household finance. The Journal of Finance, 61, 1553–1604. https://doi.org/10.1111/j.1540-6261.2006.00883.x

Carlin, B. I., & Robinson, D. T. (2012). What does financial literacy training teach us? Journal of Economic Education, 43, 235–247. https://doi.org/10.1080/00220485.2012.686385

Ericsson, K. A. (2006). Chapter 13: Protocol analysis and expert thought: Concurrent verbalizations of thinking during experts’ performance on representative tasks. In K. A. Ericsson, N. Charness, P. J. Feltovich, & R. R. Hoffman, (Eds.), The Cambridge handbook of expertise and expert performance (pp. 223–242). https://doi.org/10.1017/CBO9780511816796.013

Fernandes, D., Lynch, J. G., Jr., & Netemeyer, R. G. (2014). Financial literacy, financial education, and downstream financial behaviors. Management Science, 60, 1861–1883. https://doi.org/10.1287/mnsc.2013.1849

Grubb, M. D. (2015). Consumer inattention and bill-shock regulation. Review of Economic Studies, 82, 219–257. https://doi.org/10.1093/restud/rdu024

Hershey, D. A., Walsh, D. A., Read, S. J., & Chulef, A. S. (1990). The effects of expertise on financial problem solving: Evidence for goal-directed, problem-solving scripts. Organizational Behavior and Human Decision Processes, 46, 77–101. https://doi.org/10.1016/0749-5978(90)90023-3

Hilgert, M. A., Hogarth, J. M., & Beverly, S. G. (2003). Household financial management: The connection between knowledge and behavior. Federal Reserve Bulletin, 89, 309–322.

Karger, H. (2015). Curbing the financial exploitation of the poor: Financial literacy and social work education. Journal of Social Work Education, 51, 425–438. https://doi.org/10.1080/10437797.2015.1043194

Lee, J., Marlowe, J. (2003). How consumers choose a financial institution: Decision-making criteria and heuristics. International Journal of Bank Marketing, 22, 2, 53–71. https://doi.org/10.1108/02652320310461447

Lusardi, A., & Mitchell, O. S. (2014). The economic importance of financial literacy: Theory and evidence. Journal of Economic Literature, 52, 5–44. https://doi.org/10.1257/jel.52.1.5

Morgan, D. P., Strain, M. R., & Seblani, I. (2012). How payday credit access affects overdrafts and other outcomes. Journal of Money, Credit, and Banking, 44, 519–531. https://doi.org/10.1111/j.1538-4616.2011.00499.x

Nye, P., & Hillyard, C. (2013). Personal financial behavior: The influence of quantitative literacy and material values. Numeracy, 6(1), 1–24. https://doi.org/10.5038/1936-4660.6.1.3

Soman, D. (2001). Effects of payment mechanism on spending behavior: The role of rehearsal and immediacy of payments. Journal of Consumer Research, 27, 460–474. https://doi.org/10.1086/319621

Stango, V., & Zinman, J. (2009). What do consumers really pay on their checking and credit card accounts? Explicit, implicit, and avoidable costs. The American Economic Review, 99, 424–429. https://doi.org/10.1257/aer.99.2.424

Stango, V., & Zinman, J. (2014). Limited and varying consumer attention: Evidence from shocks to the salience of bank overdraft fees. The Review of Financial Studies, 27, 990–1030. https://doi.org/10.1093/rfs/hhu008

Note: On 10/04/2017 I changed “critical case” to “maximal variation” sampling.

Critique of “Perceiving and managing business risks: Differences between entrepreneurs and bankers” by Sarasvathy, Simon, & Lave (1998)

This is a critique of a qualitative, protocol-analysis empirical study by Sarasvathy, Simon, and Lave (1998) that I wrote on 2017-09-22 for the class, EDF 7475: Qualitative Research in Education taught by David Boote, Ph.D. at University of Central Florida.

EDF 7475 Article Critique One
Richard Thripp
University of Central Florida

Article Citation

Sarasvathy, D., Simon, H. A., & Lave, L. (1998). Perceiving and managing business risks: Differences between entrepreneurs and bankers. Journal of Economic Behavior & Organization33, 207–225. https://doi.org/10.1016/S0167-2681(97)00092-9

Summary

The authors wrote a series of business problems, some involving risk of financial loss, and others involving uncertainty of workers dying or getting cancer. They administered these written problems to successful entrepreneurs (n = 4) and seasoned bankers (n = 4) who were recruited from continuing education participants and alumni of Carnegie Mellon University. The authors used verbal think-aloud protocols to analyze participants’ responses and thought processes to the written problems. Through cluster and protocol analyses, the authors concluded that entrepreneurs and bankers conceptualize risk and uncertainty differently. Bankers tend to hold returns fixed and try to decrease risk, while entrepreneurs accept the given levels of risk and focus on increasing returns. When confronting potential cancer or death due to carcinogens and other hazards in the workplace, bankers spoke in the third person and restricted their problem space to financial, legal, and ethical issues, often refusing to make a decision; contrastingly, entrepreneurs put their personal values first and looked for external solutions (e.g., being acquired by a larger company) to provide the millions of dollars required to improve safety.

Contribution to the Field

Methodologically, this article contributed by using a qualitative approach, which is unusual in finance and economics. The stark conceptual differences that emerged from protocol analysis of the bankers as compared to the entrepreneurs contributes to future studies of different financial perspectives, and more broadly, to management and leadership studies. Moreover, the business problems the authors developed are of value, although unfortunately from a web search it appears no one else has ever used these problems in the 21 years since this article’s acceptance.

Strengths and Weaknesses

Ericsson (2002) says a protocol analysis should ideally include other indicators like response times and brain activity. While the authors verbal coding and analysis was parsimonious yet sufficiently detailed, they did not include any information on how long respondents took on each question nor the entire instrument. One strength was that many utterances along each cluster dimension were included in Table 1, although the amount of time respondents spent pondering the problems could also have been included. However, the inclusion of quantitative participant-level statistics in Tables 3–4 is laudable and should be followed by other qualitative researchers, albeit the value here is dubious due to the small amount of data.

In delivering the instrument, procedural rigor was lacking. This is abundantly clear in Appendix A, where Banker 2 mistook an amount in Problem 5 to be $1 million instead of $5 million, because “apparently he was given the copy of the problems used by the previous subject who, after completing the protocol, had discussed the possibility of making it one million and had changed the number 5 to number 1” (pp. 224–225). Another oversight is that Appendix B: Results of Cluster Analysis only refers readers to statistics in Tables 3–4 which were included in Appendix A, but these tables should have been moved to Appendix B. Finally, participants may be identifiable—we know the entrepreneurs founded companies with $5–30 million annual revenues—but social desirability bias is never mentioned, despite the nature of the problems. Given the prestige of the journal and institution, these shortcomings are surprising.

There is no getting around the fact this was a convenience sample. Entrepreneurs who were participating in the authors’ continuing education program were solicited to participate, and bankers were alumni “selected based on geographical accessibility and convenience of scheduling” (p. 208). Moreover, the design of the study suggests the authors may have held a priori assumptions about the differences between bankers and entrepreneurs. Further, the writing and design of the problems, including rationale for offering only di- or trichotomous choices, is absent. It is not even clear the authors wrote the problems— “five problems were used” (p. 208) is what they state, although I surmise they wrote the problems based on an absence of web results when searching portions of the exact text of the problems, besides this article itself.

Nevertheless, this was a concise, enjoyable read that resulted in surprising findings and insights. For example, in Problem 1, I find bankers’ preference for Project 2 stunning given the high probability of low returns contrasted with only a 0.5–0.75% increase in summed returns, based on cumulative probability. Even with discretionary monies, accepting enormous uncertainty for such marginal gains seems overly rational—almost inhuman. Finally, it was smart of the authors to note that causality could flow counterintuitively—perhaps bankers self-select due to their pre-existing aversion to risk, rather than developing the aversion on the job?

Contribution to My Understanding

This article showed me that conducting a verbal protocol analysis is not an insurmountable challenge. The authors’ coding scheme was concise and readily accessible, while their consistent emphasis on participants’ quotes gave me a clear window into several perspectives and lines of reasoning. Moreover, the article made it starkly clear that qualitative research involves many value judgments—there were certainly hundreds of utterances the authors did not see fit to include, and a detailed copy of their encoding protocols is only available upon request. However, trusting in their judgments and techniques, I feel reading their selections and analyses is preferable to having the raw recordings or transcripts. Finally, I now understand that protocol analysis can be used not just for comparing experts and novices, but also different kinds of experts, and that many inferences can be inductively drawn from the results.

Personal Intro to EDF 7474 Peers

Here is an introduction discussion post I wrote to my peers in Dr. Hahs-Vaughn’s EDF 7474: Multilevel Data Analysis course at University of Central Florida this semester (Fall 2017). Here, I recap my first year in the Ph.D. program, discuss my plans for the fall semester, and share an anecdote about the time I sold a photograph to NBC.

Hello, my name is Richard Thripp and I’m a 2nd-year Ed. Ph.D. Instructional Design & Technology student. I like math and statistics, and am in the Advanced Quantitative Methodologies certificate program.

The area I am most passionate about is financial literacy education and research, although in the past year I have been all over the place, working on an engineering testing center (a manuscript on which I am Author # 4 of 4 is under review), threshold concepts for doctoral students (abandoned this), Florida 7th grade civics test data (a manuscript on which I am Author # 3 of 5 is under review), and National Financial Capability Study (NFCS) data. Tentatively, this semester I want to further analyze NFCS data in this course and in Dr. Witta’s Quantitative Methods II course, leading to a publication, while an unrelated project will be observing consumer behavior at Starbucks for Dr. Boote’s qualitative methods class.

This semester is my first one teaching. I have 70 students in two sections (35 each) of EME 2040: Introduction to Technology for Educators. So far I have enjoyed it a lot.

An interesting fact about me is that in 2011, I sold this framed copy of this image of a grasshopper, a photograph I took in 2006 and removed the background, on Etsy to NBC, for $20. It appeared in Max Burkholder’s room in Season 3 of Parenthood. I still haven’t watched the show, and could not find it while skimming through it, but was told by one other person they saw it.

Message from NBC on Etsy