AN EVALUATION OF CONSUMER BUYING CRITERIA AND ITS IMPACT ON THE PURCHASE OF COMMODITIZED LAPTOPS by Rachel V. McClary A Dissertation Presented in Partial Fulfillment Of the Requirements for the Degree Doctor of Philosophy Capella University December 2006 © Rachel McClary, 2006 AN EVAULATION OF CONSUMER BUYING CRITERIA AND ITS IMPACT ON THE PURCHASE OF COMMODITIZED LAPTOPS by Rachel V. McClary has been approved December 2006 APPROVED: JIM MIRABELLA, D. B. A, Faculty Mentor and Chair RICHARD MURPHY, Ph. D. , Committee Member ELAINE GUERRAZZI, Ph. D. , Committee Member ACCEPTED AND SIGNED: _________________________________________ JIM MIRABELLA, D. B. A. __________________________________________ Kurt Linberg, Ph. D. Executive Director, School of Business & Technology Abstract Laptop vendors are constantly looking for new ways to differentiate themselves. The commodization of this market precipitates a deeper view into what drives a consumer purchase of one brand over another. Do certain demographic profiles exist that are more likely to purchase a particular brand? Do certain product or brand attributes serve as the final decision criteria in the purchase process?
What is compelling between laptop brands to drive selection? Results support the premise that relationships exist and that consumers are more likely to purchase one brand over another based on age, education level, gender or technical competence. The likely selection of a laptop brand can also be associated with a particular product or brand attribute. A better understanding of the laptop consumer enhances a vendor’s ability to properly segment and market the message to the right audience, increasing the likelihood of purchase. Implications for laptop vendors and recommendations for them as well as future research are presented.
Dedication To Stephen, whose commitment and dedication to this journey was equal if not sometimes greater than my own. To Olivia and Elle, may this serve in later years as evidence that anything can be achieved if you work hard. iii Acknowledgments To committee members, Dr. Dick Murphy and Dr. Elaine Guerazzi, thank you for your careful guidance and direction from the first proposal to the final submission. Your contributions were appreciated and made this final product what it is. An unparalleled gratitude must be extended to my Mentor, Dr.
Jim Mirabella, whose tireless commitment, support and confidence given to me throughout the process cannot go unrecognized. I only hope that I can demonstrate the passion and dedication to students he so easily does. iv Table of Contents Acknowledgments iv List of Tables vii CHAPTER 1. INTRODUCTION 1 Introduction to the Problem 1 Background of the Study 1 Statement of the Problem 3 Purpose of the Study 4 Research Questions 4 Significance of the Study 6 Definition of Terms 6 Conceptual Framework 9 Organization of the Remainder of the Study CHAPTER 2. LITERATURE REVIEW 10 11 Introduction 11 Decision Making Theory 1 Consumer Choice Through Decision Making 22 Attributes as Influencers to Purchase 31 Brand Equity Definitions 41 Application to High Tech 43 CHAPTER 3. METHODOLOGY 46 Design of the Study 46 Methodology 47 v Study of the Population and Sample 48 Measurement Strategy 50 Variables 51 Research Questions and Hypotheses 51 Data Analysis Procedures 59 Assumptions and Limitations 59 CHAPTER 4. DATA COLLECTION AND ANALYSIS 60 Demographics 60 Brand 61 Respondent Characteristics 61 Results 64 CHAPTER 5. RESULTS, CONCLUSIONS, AND RECOMMENDATIONS 174 The Research Questions 174 The Hypotheses 175 Conclusions 91 Recommendations for Laptop Vendors 196 Recommendations for Future Research 199 REFERENCES 201 APPENDIX SURVEY 208 vi List of Tables Table 1. Brand’s Role in Consumer Choice 36 Table 2. Gender Descriptive Statistics 62 Table 3. Age Group Descriptive Statistics 62 Table 4. Education Level Descriptive Statistics 63 Table 5. Technical Competence Level Descriptive Statistics 64 Table 6. Crosstabulation for H1 65 Table 7. Chi Square Test for H1 66 Table 8. Crosstabulation for H2 67 Table 9. Chi Square Test for H2 68 Table 10. Crosstabulation for H3 69 Table 11. Chi Square Test for H3 69 Table 12.
Crosstabulation for H4 71 Table 13. Chi Square Test for H4 72 Table 14. Crosstabulation for H5 73 Table 15. Chi Square Test for H5 74 Table 16. Crosstabulation for H6 75 Table 17. Chi Square Test for H6 76 Table 18. Crosstabulation for H7 77 Table 19. Chi Square Test for H7 77 Table 20. Crosstabulation for H8 79 Table 21. Chi Square Test for H8 80 Table 22. Crosstabulation for H9 81 vii Table 23. Chi Square Test for H9 82 Table 24. Crosstabulation for H10 83 Table 25. Chi Square Test for H10 84 Table 26. Crosstabulation for H11 85 Table 27. Chi Square Test for H11 85 Table 28. Crosstabulation for H12 7 Table 29. Chi Square Test for H12 88 Table 30. Crosstabulation for H13 89 Table 31. Chi Square Test for H13 90 Table 32. Crosstabulation for H14 91 Table 33. Chi Square Test for H14 92 Table 34. Crosstabulation for H15 93 Table 35. Chi Square Test for H15 93 Table 36. Crosstabulation for H16 95 Table 37. Chi Square Test for H16 96 Table 38. Crosstabulation for H17 97 Table 39. Chi Square Test for H17 98 Table 40. Crosstabulation for H18 99 Table 41. Chi Square Test for H18 100 Table 42. Crosstabulation for H19 101 Table 43. Chi Square Test for H19 101 Table 44. Crosstabulation for H20 103 Table 45.
Chi Square Test for H20 104 viii Table 46. Crosstabulation for H21 105 Table 47. Chi Square Test for H21 106 Table 48. Crosstabulation for H22 107 Table 49. Chi Square Test for H22 108 Table 50. Crosstabulation for H23 109 Table 51. Chi Square Test for H23 109 Table 52. Crosstabulation for H24 111 Table 53. Chi Square Test for H24 112 Table 54. Crosstabulation for H25 113 Table 55. Chi Square Test for H25 114 Table 56. Crosstabulation for H26 115 Table 57. Chi Square Test for H26 116 Table 58. Crosstabulation for H27 117 Table 59. Chi Square Test for H27 117 Table 60. Crosstabulation for H28 119 Table 61.
Chi Square Test for H28 120 Table 62. Crosstabulation for H29 121 Table 63. Chi Square Test for H29 122 Table 64. Crosstabulation for H30 123 Table 65. Chi Square Test for H30 124 Table 66. Crosstabulation for H31 125 Table 67. Chi Square Test for H31 125 Table 68. Crosstabulation for H32 127 ix Table 69. Chi Square Test for H32 128 Table 70. Crosstabulation for H33 129 Table 71. Chi Square Test for H33 130 Table 72. Crosstabulation for H34 131 Table 73. Chi Square Test for H34 132 Table 74. Crosstabulation for H35 133 Table 75. Chi Square Test for H35 133 Table 76. Crosstabulation for H36 135 Table 77.
Chi Square Test for H36 136 Table 78. Crosstabulation for H37 137 Table 79. Chi Square Test for H37 138 Table 80. Crosstabulation for H38 139 Table 81. Chi Square Test for H38 140 Table 82. Crosstabulation for H39 141 Table 83. Chi Square Test for H39 141 Table 84. Crosstabulation for H40 143 Table 85. Chi Square Test for H40 144 Table 86. Crosstabulation for H41 145 Table 87. Chi Square Test for H41 146 Table 88. Crosstabulation for H42 147 Table 89. Chi Square Test for H42 148 Table 90. Crosstabulation for H43 149 Table 91. Chi Square Test for H43 149 x Table 92. Crosstabulation for H44 151 Table 93.
Chi Square Test for H44 152 Table 94. Crosstabulation for H45 154 Table 95. Chi Square Test for H45 155 Table 96. Crosstabulation for H46 156 Table 97. Chi Square Test for H46 157 Table 98. Crosstabulation for H47 158 Table 99. Chi Square Test for H47 159 Table 100. Crosstabulation for H48 160 Table 101. Chi Square Test for H48 161 Table 102. Crosstabulation for H49 162 Table 103. Chi Square Test for H49 163 Table 104. Crosstabulation for H50 164 Table 105. Chi Square Test for H50 165 Table 106. Crosstabulation for H51 166 Table 107. Chi Square Test for H51 167 Table 108. Crosstabulation for H52 68 Table 109. Chi Square Test for H52 169 Table 110. Crosstabulation for H53 170 Table 111. Chi Square Test for H53 171 Table 112. Crosstabulation for H54 172 Table 113. Chi Square Test for H54 173 Table 114. Relationships Between Demographics and Information Sources and Attributes 193 xi Table 115. Relationship Between Demographics and Most Important Evaluative Criterion 194 Table 116. Relationships Between Brand and Information Sources and Attributes 195 xii CHAPTER 1. INTRODUCTION Introduction to the Problem The era of highly differentiated laptops in the consumer industry is over.
No longer does one vendor dominate the market, enjoying their product being seen as exceedingly superior to its competition. What once served to distinguish a laptop provider has now been equalized across the field; every vendor offers the same microprocessors, the same RAM capacity, the same graphics cards, the same networking and wireless functionality. The commoditization of the market has diminished a vendor’s ability to strongly differentiate themselves among consumers. With all things virtually equal within the box, what is it that makes a consumer choose one brand over another?
Is it still within the box or outside of it that drives the decision? While the evaluative buying criteria consumers use when purchasing these products may be known, what was not known was the impact each of them have in contributing to that decision, and whether combinations of these criteria aligned with a certain demographic profile of a customer segment. Background of the Study The introduction of computer technology to the consumer market brought with it an evolution of change within the household that is comparable to the likes of radios and televisions in the 20th century.
It served as a catalyst in jumpstarting not only how consumers obtain information but also the rapidity, quality and density with which they retrieve it. Computers serve as a source of entertainment in addition to its role as resource and productivity tool. 1 Over the years as the benefits of household computers exponentially increased while simultaneously being realized, the technology had equally improved at the same rate. What was once a massive box and monitor taking up an entire desktop was now a sleek, stylish addition to one’s decor, the size of a coffee table book.
What had piggybacked on this technology boom was the paradigm shift in the computer industry from highly proprietary, differentiated and premiumpriced hardware to industry-standard, commoditized components that were priced accordingly. Despite the loss of high margin goods, manufacturers continued to push the boundaries of the technology to deliver one more choice point to the consumer – mobile computing. The explosion of laptop/mobile notebooks on to the market further improved user productivity and introduced a sense of freedom otherwise unknown.
Similar to any market where a hot product enters, manufacturers were quick to replicate and develop their own under their brand. The Personal Computer was viewed by consumers in the United States as a valuable tool to enhance productivity and improve the entertainment experience. While the form factor of choice in households today remains the desktop, maintaining more than 50% of the ownership, laptop/mobile notebooks are improving their position, up to 17%. Price difference between the two remains the primary reason for the gap, although manufacturers are introducing lower priced laptops that directly challenge the price of many desktops.
The increase of wireless capabilities and the corresponding benefits were beginning to tip the scales toward the mobile computing direction (Daoud & Shim, 2005). Fast forward from the introduction of the computer to the laptop today, where the market has became saturated with well-known brands, each offering nearly indistinguishable products to a population of consumers that are now more educated, have easier access to more information to compare and contrast competitive products and ultimately make a much more fact-based, 2 informed decision.
While consumers enjoy the benefit of being more educated with public access to free information regarding laptops, manufacturers continue to conduct studies on consumer behaviors behind closed doors. Little to no market segmentation exist publicly that states who the laptop buyer really is. No public studies had been located at this point of this dissertation development, as market research studies are traditionally private. What has been studied is the decision making process itself, grounded in theory and tested in practice, specifically when consumers seek ought the information that is available to them.
Within the normative model of decision making, the consumer collected information about alternatives, evaluated them based on their relevance and made a decision that will maximize the value of that decision (Lau, 1995; Abelson & Levi, 1985). How the consumer collected his information affected the choice strategy he selected. The more complex the decision task, the more likely strategies will be employed to simplify that task (Johnson & Payne, 1985; Thorngate, 1980). Statement of the Problem As industry standard components within a laptop became more prevalent, the ability to differentiate became more difficult.
The commoditization of this market created a challenge for manufacturers to identify the internal motivation among the consumer base that influenced their purchase of one brand over another. This commoditization had proven it difficult for any one vendor to considerably differentiate themselves in the consumer market. Laptop vendors needed to know if relationship existed between the profile of these consumers, the most important buying criteria they used when considering the purchase and the final brand that was selected at point of purchase. 3 Purpose of the Study
The purpose of this study was to determine if a relationship existed between the brand of laptop consumers selected and a variety of demographic and evaluative buying criteria considered in the process. The demographic variables examined included age, education level and the degree of technical competence. The result provided laptop vendors a unique perspective on the consideration and selection phase. The results further enabled useful segmentation of the population to better target messaging and promotions that will resonate with the appropriate audience.
There is tremendous business value in vendors gaining insight into the consumers’ minds around this topic as it can drive better marketing activity to influence awareness, consideration, preference and ultimately purchasing campaigns. Marketing the wrong product features to the wrong audience results in a low marketing Return on Investment (ROI). Customer insight is powerful and can properly navigate the vendor toward the right direction in developing message and value propositions that hit the mark, resulting in higher sales and higher returns on their investment.
Research Questions Humans are inquisitive. They seek to answer the many questions that are posed as a result of their observations and interpretations. Research acts a framework to help guide an individual through the process of producing high quality, reliable answers to those questions, enabling better decision making. All research begins with the simplest form of a question. While the process for development and refinement is built into the design of the research and its methodology, the spark of inquiry that fuels it is primal and basic This study strove to answer a series of nine research questions within two categories through the development of relevant hypotheses and use of statistical techniques to either prove or disprove them. Demographics 1. Is there a relationship between the demographics of a laptop user and the brand purchased? 2. Does a relationship exist between the demographics of a laptop user and the most important evaluative buying criteria identified by the consumer in contributing to the purchase decision? 3.
Is there a relationship between the relative importance of various information sources and the demographics of a laptop user? 4. Does a relationship exist between the between the demographics of a laptop user and the tangible, product-like attributes considered in the purchase decision? 5. Does a relationship exist between the between the demographics of a laptop user and the soft, intangible attributes considered in the purchase decision? Brand 1. Is there a relationship between the laptop brand purchased and the relative importance of various information sources used by the consumer? . Does a relationship exist between the tangible, product-like attributes considered in the purchase decision and the laptop brand selected? 3. Does a relationship exist between the soft, intangible attributes considered in the purchase decision and the laptop brand selected? 4. Is there a relationship between the laptop brand purchased and the most important evaluative buying criteria identified by the consumer in contributing to the purchase decision? 5 Significance of the Study Identifying if a consumer tendency existed toward the use of tangible product attributes, (i. e. speeds and feeds”) versus less tangible criteria (i. e. brand awareness, or “I like Dell’s commercials”) helped determine the appropriate course of action to influence them throughout their purchase journey. For instance, a 75 year-old female with a High School Diploma and no technical background would consider the purchase of one laptop over another for very different reasons than a 30 year-old Computer Technician who is heavily into gaming. Each individual develops his or her own collective set of attributes that is evaluated, assessed and weighed to enable a purchase decision.
By better understanding the relationships between the criteria, including their relative importance in relation to demographic variables, laptop vendors can more accurately target the appropriate value proposition that will resonate with the intended audience. This type of focused segmentation and targeted messaging can result in a higher Return on Marketing Investment (ROMI). The better equipped vendors are to send the right message to the right audience, the better the likelihood it will result in increased sales.
The number one function of Marketing is to grow the top line by filling the sales funnel with prospective buyers. Definition of Terms The definitions below were sourced from the online technical resource, whatis. com. Application program interface (API). An application program interface (API – and sometimes spelled application programming interface) is the specific method prescribed by a computer operating system or by an application program by which a programmer writing an application program can make requests of the operating system or another application.
An API can be contrasted with a graphical user interface or a command interface (both of which are direct user interfaces) as interfaces to an operating system or a program. ” 6 (Retrieved October 14, 2006 from http://searchexchange. techtarget. com/sDefinition/0,,sid43_gci213778,00. html) Commoditization. Commoditization is the existence of like attributes to a product or service. When a product becomes indistinguishable from others like it and consumers buy on price alone, it becomes a commodity. (Retrieved October 14, 2006 from http://www. investopedia. om/terms/c/commoditization. asp) Digital-to-analog conversion. Digital-to-analog conversion is a process in which signals having a few (usually two) defined levels or states (digital) are converted into signals having a theoretically infinite number of states (analog). A common example is the processing, by a modem, of computer data into audio-frequency (AF) tones that can be transmitted over a twisted pair telephone line. The circuit that performs this function is a digital-to-analog converter (DAC). (Retrieved October 14, 2006 from http://searchsmb. echtarget. com/sDefinition/0,,sid44_gci213875,00. html) Graphics card. A video adapter (alternate terms include graphics card, display adapter, video card, video board and almost any combination of the words in these terms) is an integrated circuit card in a computer or, in some cases, a monitor that provides digital-to-analog conversion, video RAM, and a video controller so that data can be sent to a computer’s display. Today, almost all displays and video adapters adhere to a common denominator de facto standard, Video Graphics Array (VGA).
VGA describes how data – essentially red, green, blue data streams – is passed between the computer and the display. It also describes the frame refresh rates in hertz. It also specifies the number and width of horizontal lines, which essentially amounts to specifying the resolution of the pixels that are created. VGA supports four different resolution settings and two related image refresh rates. (Retrieved October 14, 2006 from http://searchsmb. techtarget. com/sDefinition/0,290660,sid44_gci213290,00. html) Hard disk.
A hard disk is part of a unit, often called a “disk drive,” “hard drive,” or “hard disk drive,” that stores and provides relatively quick access to large amounts of data on an electromagnetically charged surface or set of surfaces. Today’s computers typically come with a hard disk that contains several billion bytes (gigabytes) of storage. A hard disk is really a set of stacked “disks,” each of which, like phonograph records, has data recorded electromagnetically in concentric circles or “tracks” on the disk. A “head” (something like a phonograph arm but in a relatively fixed position) records (writes) or reads the information on the tracks.
Two heads, one on each side of a disk, read or write 7 the data as the disk spins. Each read or write operation requires that data be located, which is an operation called a “seek. ” (Data already in a disk cache, however, will be located more quickly. ) A hard disk/drive unit comes with a set rotation speed varying from 4500 to 7200 rpm. Disk access time is measured in milliseconds. Although the physical location can be identified with cylinder, track, and sector locations, these are actually mapped to a logical block address (LBA) that works with the larger address range on today’s hard disks. (Retrieved October 14, 2006 from http://searchstorage. techtarget. com/sDefinition/0,,sid5_gci212227,00. html) Laptop/mobile computer. A laptop computer, usually called a notebook computer by manufacturers, is a battery- or AC-powered personal computer generally smaller than a briefcase that can easily be transported and conveniently used in temporary spaces such as on airplanes, in libraries, temporary offices, and at meetings. A laptop typically weighs less than 5 pounds and is 3 inches or less in thickness. Retrieved October 14, 2006 from http://searchmobilecomputing. techtarget. com/sDefinition/0,,sid40_gci213610,00. html) Operating system. An operating system (sometimes abbreviated as “OS”) is the program that, after being initially loaded into the computer by a boot program, manages all the other programs in a computer. The other programs are called applications or application programs. The application programs make use of the operating system by making requests for services through a defined application program interface (API).
In addition, users can interact directly with the operating system through a user interface such as a command language or a graphical user interface (GUI). Retrieved October 14, 2006 from (http://searchsmb. techtarget. com/sDefinition/0,,sid44_gci212714,00. html) Processor. A processor is the logic circuitry that responds to and processes the basic instructions that drive a computer. The term processor has generally replaced the term central processing unit (CPU). The processor in a personal computer or embedded in small devices is often called a microprocessor. Retrieved October 14, 2006 from http://searchsmb. techtarget. com/sDefinition/0,,sid44_gci212833,00. html) RAM. RAM (random access memory) is the place in a computer where the operating system, application programs, and data in current use are kept so that they can be quickly reached by the computer’s processor. RAM is much faster to read from and write to than the other kinds of storage in a computer, the hard disk, floppy disk, and CD-ROM. However, the 8 data in RAM stays there only as long as your computer is running. When you turn the computer off, RAM loses its data.
When you turn your computer on again, your operating system and other files are once again loaded into RAM, usually from your hard disk. RAM can be compared to a person’s short-term memory and the hard disk to the longterm memory. The short-term memory focuses on work at hand, but can only keep so many facts in view at one time. If short-term memory fills up, your brain sometimes is able to refresh it from facts stored in long-term memory. A computer also works this way. If RAM fills up, the processor needs to continually go to the hard disk to overlay old data in RAM with new, slowing down the computer’s operation.
Unlike the hard disk which can become completely full of data so that it won’t accept any more, RAM never runs out of memory. It keeps operating, but much more slowly”. Retrieved October 14, 2006 from (http://searchmobilecomputing. techtarget. com/sDefinition/0,290660,sid40_gci214255,00. html) Conceptual Framework What is it that compels a consumer to purchase the Dell laptop instead of the HP when a consumer is comparing them side by side? Is it just the price? Has the consumer previously had a bad experience with HP?
Are the Dell commercials intriguing enough to make consumers think they look like a fun company so their products must be the best? Does someone from a younger generation with a higher degree of technical competency tell an older family member that Dell is the only thing to buy? What drives the decision, and is there any relationship between those drivers and the consumer profile making them? Howard-Sheth (1969) and Engel (1983) developed models that can explain and predict human behavior and how it related to decision making, focusing on the process, learning and perceptions and attitudes.
But did a key set of attributes exist that could influence that decision one way or the other? Specifically as it related to technology, the Technology Adoption Model (TAM) proposed five attributes that will be discussed in greater detail in Chapter 2. They 9 include: (a) perceived usefulness, (b) perceived ease of use, (c) relative advantage, (d) technology attitude, and (e) brand (Taylor & Todd, 1995). The first of several variables analyzed in this study was the brand of laptop selected in the purchase decision.
Additional variables included both tangible, product-related factors like price and features as well as intangible, brand-related attributes like brand image and outside recommendations. The demographic variables were age, education, gender and level of technical competency. What was tested is the existence of a relationship between these variables and the laptop brand purchased. For example, whether or not the competency level of the consumer influenced the purchasing decision was studied.
It is often conjectured that those consumers with a high level of technical competency may have a tendency to align more with the physical attributes versus with lower levels that choose to align emotionally. The age of the consumer is another indicator, as it is often speculated whether younger consumers make buying decisions based on intangible attributes such as brand image while older consumers depend more heavily on the more tangible attributes like reliability. Organization of the Remainder of the Study
Chapter Two reviews the relevant literature examining decision-making theory at its most basic level and then delves deeper into consumer choice as it relates within that theory and further reviews specific attributes that would affect that choice and the role that brand equity plays within. Chapter Three reviews the methodology of this secondary research study while Chapter Four presents the analysis of the data. The final Chapter provides a thorough review of the findings including recommendations to vendors and future research. 10
CHAPTER 2. LITERATURE REVIEW Introduction Fundamental to unlocking the secret of internal motivations surrounding consumer purchase is understanding three key areas: (a) decision making theory that serves as the foundation and the role information plays in this process and the acquisition strategy of the user, (b) what drives consumer choice and the attributes that act as influencers to ultimately enable purchase decisions, and (c) importance of brand and the resulting brand equity that contributes to a consumer’s choice to purchase.
Each of these three areas will be reviewed in this chapter. Decision Making Theory Data is data, but information is power. When data can be transformed into information, the user is equipped with better decision making tools. Different data can become information to different people, all based on its relevancy to the user in achieving the desired goal of making an informed decision. The stages a consumer experiences in working through this process are similar, and a certain sense of consistency has emerged as a result of continuous research around decision making.
Decision Making Theory and Information Acquisition In order for a decision to be made, an individual must first identify a perceived need that must to be met. As mentioned, for this discussion, the individual will be identified as a consumer with the need for a product or service. Then the process begins. Within the normative 11 model of decision making, the consumer collects information about alternatives, evaluates them based on their relevancy and makes a decision that will maximize the value of that decision (Lau, 1995;Abelson & Levi, 1985).
Otherwise known as the value-maximization theory, the normative model has been criticized as too broad, ignoring human limitations (Moorthy, Ratchford & Talukdar, 1997; Thaler, 1985), and an evolutionary, bounded rationality model emerged to enhance it. Here consumers were assumed to have limited processing capability, selectively search alternatives and terminate the search when a suitable solution has been found (Simon, 1985). Further criticism emerged from this model as well. By selective selection, the consumer is compromising the random nature of the information search and may compromise the decision choice.
How a consumer collects his information affects the choice strategy he uses. For example, decision makers choose a certain strategy depending on the complexity of the task. The more complex the decision task, the more likely people employ strategies to simply that task (Johnson & Payne, 1985; Thorngate, 1980). While several theories exist, the valuemaximization/normative model has remained relatively intact and enhanced with the limitation of human processing capacity. Rationality: Substantive Versus Procedural
The first stage of defining relevancy as it relates to the consumer decision process within Abelson and Levi’s (1985) framework is grounded in the notion that consumers are rational and have the ability to apply a certain sense of logic to the determination and definition of relevant information to aid them in the decision making process. Consumers are considered rational decision-makers in the traditional economic theory of consumer behavior. They implement choice strategies that are the most advantageous to their outcome, based on their perception of the decision environment.
The use of cost/benefit analysis demonstrates optimal nature of the 12 consumer’s strategy (Moorthy, Ratchford & Talukdar, 1997; Payne, 1982). In addition Simon (1985) suggests that every consumer, when making a decision, has and uses a “utility function” that generates a ranking within the alternatives and enables the selection of the product with the highest utility. This process assumes a substantively rational solution. Procedural rationality as defined by Simon (1985) is the flexible nature of human ehavior that adapts and adjusts to the external factors facing and internal factors constraining the consumer. Because it was developed within psychology and the primary focus is on the process, procedural rationality concentrates on the process that generates a particular behavior rather than the outcome. The intent is to observe the individual and the process though which they work that will generate the rational thinking behind the decision. Compensatory Versus Noncompensatory Choice Rules The two major rules guiding choice strategies discussed in the literature are compensatory and noncompensatory.
They are differentiated based on three characteristics: the level of attractiveness, commensurability across attributes and form of processing (intradimensional versus interdimensional). The former describes a complex and sophisticated method for Abelson and Levi’s (1985) third element of decision making, information integration, while the latter equally descriptive to information integration deploys a simplistic approach. Each of these rules is also used in the second stage of information collection. Compensatory choice rules require commensurability, enabling trade-off of attribute value of one over another.
For example, when purchasing a home, the total square footage may be sacrificed for an ocean view. The level of attractiveness of each of these attributes could be high but trade-offs on initial ranking could occur. Generally compensatory choice mandates an 13 interdimensional form of processing, where the consumer assigns an overall rating to each attribute in the choice set (Abelson & Levi, 1985). Noncompensatory choice rules differ. Commensurability is not required, and attribute trade-offs are not allowed. Within this category of rules, there exist conjunctive and disjunctive rules.
Both require a set of cutoffs on the choice dimensions. The conjunctive rule assumes a minimum set and product rejection when it does not exceed all of them. The form of processing is interdimensional. Using the home search example above, the consumer using a conjunctive, noncompensatory rule would consider each home separately and reject either if it did not meet both the square footage and view requirements. A caveat to this rule is that if more than one product exceeds all of the requirements, the model will yield an equal number of acceptable alternatives.
At this point, the consumer would either develop more stringent cutoffs or use a different choice rule that would yield only one solution. Disjunctive rules also require those cutoffs, although the filter is different. “An alternative would be considered acceptable if it has at least one value greater than the corresponding cutoff” (Abelson & Levi, 1985, p. 260). With the home example, the homes to be considered acceptable would have at least the desired square footage or view. Both are not necessary.
The caveat to this rule is that a different set of cutoffs would generate a different set of alternatives, allowing for multiple choices. The same issue applies to the conjunctive rules. Information Search Strategies Once the relevancy is determined the surgical approach in searching for information can begin. The strategies are learned and deployed cumulatively as the consumer steers his way through the process. The search strategies enable the integration of the information and the eventual selection of the product, exploring all three stages of Abelson and Levi’s (1985) model: 14 elevance, assembly and integration. First the idea of rationality enables the definition of relevance. That breaks through to pave the way for assembling information which in turn enables the integration. An emergent belief exists among decision science researchers that consumer preferences are often times developed during the decision process rather than being pre-existing (Tversky, Sattath & Slovic, 1988; Bettman, 1979). “People often do not have well-defined preferences; instead, they may construct them on the spot when needed, such as when they must make a choice” (Bettman, Luce & Payne, 1988, p. 88). The concept of constructive preference enhances the ideas of Simon’s (1985) bounded rationality and limited processing capacity. It introduces the dynamic of human learning and adaptability, further refining the concepts to explain the intricate actions of consumer behavior and decision making. “One important property of this constructive viewpoint is that preference will often be highly context dependent. This implies that processing approaches may change as consumers learn more about problem structure during the course of making a decision” (Bettman, Luce & Payne, 1988, p. 88). Agility connotates a level of intelligence and rationality, bound together by reason and logic. Three search strategy models exist defined by the underlying choice rules (compensatory versus noncompensatory and interdimensional versus intradimensional): linear, additive difference, conjunctive and elimination-by-aspects (Payne, 1976). The additive model represents the consumer choosing between multi-attribute products by evaluating each product separately in a pre-determined choice set, an interdimensional form of processing.
Each product attribute is first analyzed and then combined with other attributes that are perceived by the consumer to deliver the most value thereby creating the choice set (Lau, 1995). 15 In contrast, an intradimensional rule is employed within the additive difference model. Products are compared at the individual attribute level, differentiation is identified and the sum of the results is used to identify the best product. With both the linear and additive difference models, the strategies use a compensatory strategy (Lau, 1995). A non-compensatory strategy is used for the elimination-by-aspects (EBA) model.
In opposition to the linear and additive difference models, EBA does not support commensurability (i. e. value tradeoffs). Product attributes are weighted based on perceived importance of the consumer. The attribute is then selected with probability proportional to its weight. Those products that do not meet the proportional values for the selected attributes are eliminated. The consumer considers only one product attribute at a time, an intradimensional form of processing (Tversky, 1972). Information Processing Theory of Consumer Choice
The theoretical framework of Bettman’s (1979) Information Processing Theory of Consumer Choice (IPTCC) consists of six key elements that represent the hypothetical value chain, each chronologically and cumulatively dependent on the other, with four key summary points: (a) the choice process is iterative and goal-directed, (b) rather than strictly sequential, the process is cyclical, (c) in certain circumstances consumers abandon the conscious decision process in placement of “learned rules and procedures,” and (d) selection or what is termed “choice decisions” can be made at several different levels within the process.
Considerable research has proven that individuals possess a limited capacity to process information, and when required to consider multiple attributes simultaneously the ability decreases, further limiting the processing capability (Bettman, 1979; Dawes, 1976; Lindsay & Norman, 1972; Norman & Bobrow, 1975; Simon, 1969). The first of six elements, processing 16 capacity, contributes to the theory that with limited capability, the use of heuristics (simple decision strategies) and previous experience plays a significant role in decision making.
Braunstein (1976) defines heuristics as uncomplicated problem-solving methods that generate acceptable results to often complicated problems. The outcome is achieved by limiting the search to only possible solutions. Lau and Rediawsk (2001) define them as “problem-solving strategies (often employed automatically or unconsciously) which serve to keep the information processing demands of the task within bounds” (p. 252). There is no argument that heuristics are used in place of capacity and processing capability.
Primitive in nature, they compensate for these gaps and enable more accurate choices with minimal cognitive effort (Abelson & Levi, 1985). Internal motivation dictates the amount of the limited processing capacity that is dedicated to a particular decision making activity. It also affects the choice of one behavior rather than a different one, as it prescribes a certain action that drives the consumer to a particular outcome (Bettman, 1979). A caveat to be considered regarding motivation is the control issue that motivational or emotional forces present.
They tend to produce a sense of irrationality that may lead to judgmental biases (Abelson & Levi, 1985). Internal motivation is personal and drives unique behaviors in each consumer, yet the end result is the same. A purchase decision has been made. The drivers that triggered the process are likely different as is the path taken. The third element, attention and perceptual coding, breaks attention into two different categories: voluntary and involuntary. Voluntary attention occurs when a consumer consciously allocates his processing capacity toward an intended action while pursuing a pre-determined 17 goal.
Involuntary attention on the other hand occurs as “an allocation of effort to stimuli based more upon automatic mechanisms than upon current goals” (Bettman, 1979, p. 25). As Bettman (1979) and Abelson and Levi (1985) posit, consumers acquire information they deem relevant to aid in achieving the goal of making decision. In addition the information must be evaluated for relevancy. Information acquisition and evaluation, the fourth element of the IPTCC, suggests that a conscious information processing effort is present only in a complex choice scenario. Consumers tend not to seek out new information when making a habitual choice.
For situations where information is sought, two sources exist: internal memory and external. Information from one’s memory is what Bettman (1979) refers to as strongly associated, proposing that little processing effort is necessary. For example, when a consumer frequently purchases their favorite brand of toothpaste, any type of information processing is absent. The decision is made without thought. Information stored in memory, prior knowledge, does affect the information processing model and has been studied extensively (Brucks, 1985; Johnson & Russo, 1984; Bettman & Park, 1980).
Different measures within the prior knowledge concept have been studied including frequency of purchase (Bettman & Park, 1980), formal training (Sujan, 1985;Hutchinson, 1983) and self-reporting (Johnson & Russo, 1984; Alba, 1983). For situations when the information in memory is either non-existent or insufficient, it will be sought externally from a variety of resources. Bettman and Kakkar (1977) support the series of studies that have been conducted to show that how a consumer collects information is heavily dependent on the format in which that information is presented (Capon & Burke, 1977; Payne 1976; Tversky, 1969) .
The search patterns differ as the display format does. The strategies employed by a consumer in selecting a 18 particular product over another have been boiled down to two emerging patterns: Choice by Processing Brands (CPB) and Choice by Processing Attributes (CPA). Information is gathered on several attributes of one brand first and then collected on a second, a third, and the process continues with CPB. CPA strategy is used by consumers who first look at one attribute across several brands and then proceed to the second attribute. These could be referred to as vertical (CPB) versus horizontal (CPA) approaches to brand products.
The use of these strategies by consumers to assembly relevant information to enable their decision is strongly affected by the structure of that information being presented. The consumer’s use of cost/benefit analysis demonstrating rationality was discussed earlier as it related to the determination of relevancy. This is also applicable to discuss as it relates to the information search of that relevant content. Within the context of information search, the same principles apply. A consumer’s search is optimized when the perceived benefit and cost of that search are considered.
Experience increases expertise and drives the demand for more information, while product knowledge decreases the demand (Moorthy, Ratchford & Talukdar, 1997). The degree of pre-existing knowledge versus the perceived cost of acquiring new knowledge in an effort to decide which product is the best fit for the need is weighed. When a consumer searches on a brand and retrieves all the attribute information desired, “the uncertainty of that brand is removed, and its true utility revealed “ (p. 265), thus producing a high benefit relative to a lower perceived cost of information acquisition.
If the consumer brings existing brand knowledge, the perceived cost is even lower. Moorthy, Ratchford and Talukdar’s (1997) study was able to show that these factors affect the search behavior of the consumer and highlight the effect prior brand knowledge has on the search process. 19 Svenson (1979) summarized several studies in this area, documenting that an increase in the number of product attributes to be considered had a greater effect on the information search than a comparable increase in products. The limited processing capacity of consumers is clearly demonstrated here.
An interesting point to consider is the difference in effect of information collection between the change of product attributes versus number of products. The more attributes, the less information consumers sought. Multi-attribute products, while warranting more information yet resulting in the collection and assembly of less, would lead one to conclude that these types of products and the choices presented to the consumer yield less than desirable results for both the consumer and product vendor. Vendors should integrate these learnings into the development of their products and corresponding attributes.
In referring back to the third stage of Abelson and Levi’s (1985) decision making theory, integrating information to make decisions, Bettman’s (1979) concept of perceptual coding supports it. Perceptual coding describes the process through which a consumer navigates by interpreting the meaning of information to which he has directed attention. Several theories propose that the interpretation of that information is developed by using both “information from memory” and “the perceptual input itself” (Bettman, 1979, p. 25; Lindsay & Norman, 1972).
In addition to perceptual coding, the amount of information the consumer collects in the assembly stage can contribute to the success of a quality decision or the failure of a low quality decision. Bettman, Luce, and Payne (1998) found the following: Decisions become more difficult as the amount of information increases, as the time resources available for processing the information decrease, as the degree of conflict among attributes increases, as the amount of missing information increases, as the information display format becomes less organized or more complex. (p. 199) 20
Information load can be defined as the independent number of informational items. When asked to choose between two products, consumers search equally on both alternatives demonstrating the use of a compensatory decision rule. When asked to review and choose between several products with more attributes to consider, the search concentrates on only a few attributes within the choice set, utilizing a noncompensatory strategy. When faced with too many options, consumers reduce the amount of information collected by artificially reducing the number of alternative product combinations to achieve the objective of choosing one product (Payne, 1976).
Less information is sought and noncompensatory strategies used to simplify the task. While time pressure may contribute (Wallsten, 1980; Wright, 1974), findings of these studies conclude that the use of simpler, less optimal rules enable the otherwise complex task to be completed (Abelson & Levi, 1985). Information load and decision quality are inversely related. High levels of information can considerably reduce decision quality.
In research conducted by Malhotra (1982), the effects of a wide range of content and information on decision quality was studied with a varied set of measures including a self-determination of overload. The results of the study support the theory and existence of relationship between the amount of information a consumer sees and the quality of the decision made in support of that information. Consumers who are faced with too many attributes are cognitively unable to make the number of necessary comparisons to thoroughly rank them. As a result, they resort to simple choice rules and heuristics to achieve the objective.
Further studies by Scammom (1977) suggest that when confronted with increasing amounts of information, consumers will likely split their time between all of the informational objects causing a dilution of the content consumption and eventual overload, causing low decision quality and dissatisfaction among the consumer over their product choice. 21 The final element of the Process, consumption and learning, refers to the consumer’s progression through the stages to arrive at a final purchase decision and ultimately consume the product. The experience as a result of the purchase and consumption can be recycled and used as information for uture purchase decisions. In a world of endless data, the skill to convert it into useful information to enable an educated, high quality decision is greatly coveted. The three stages of relevancy, assembly and integration are equally important and equally deserving of further observation as they relate to consumer decision making. The more data, the less likely the consumer is able to wade through it and result in a quality decision. A paradox exists. Consumers crave data. They covet information. Yet when presented with a limitless supply, they are overloaded and forced to ignore the abundance.
The human condition creates an environment that sustains the individual and supports them in their decision making process. With too much, we get less. With too little, we get less. The careful, delicate balance between starvation and overload is the utopia vendors need to obtain to better enable more satisfied, higher quality decisions consumers can enjoy. Consumer Choice Through Decision Making This section will introduce to the reader the models that support the underlying drivers to consumer choice and the attributes that act as influencers to enable purchase decisions.
It will answer the questions: what drives consumer choice and what attributes from those drivers influence purchase? The reader will understand how the consumer approaches the concept of making a decision and the internal, processes and tools he uses to arrive at that decision. For the purposes of this discussion, the scope of attributes influencing purchase as they relate to consumer choice will be bound to the area of technology adoption. The concepts of consumer 22 choice and decision making are described in the general context. Discussion relation to them focus in on the technology adoption component.
Choice can be a double-edged sword. When not faced with it, one feels mandated. When faces with its entirety, one feels overwhelmed. In between exists a delicate balance, once where the decision-maker believes enough in the way of resources has been allocated to enable him to generate a high quality decision. In the context of consumer choice, the process an individual assumes to ensure the quality is driven by the individual, similar in methodology to all but unique in deployment. Drivers to Choice What drives a consumer to choose one product over another?
What combination of variables, alternatives, external or internal factors compels the decision? The answer, intricate in its delivery yet simple in its response is fundamentally human behavior. How humans process information and make choices around the selection and consumption of products is fundamentally to answer the question of what drives the actions. Swift and continuous technological change in conjunction with the explosion of information sources like the web and television have given consumers too much choice within a time-pressured environment. How can consumers adapt and cope with the decisions they make?
Bettman, Luce and Payne (1998) suggest the process is adaptive and present a conceptual framework of five components that helps unlock the secret of understanding the process consumers undergo to form their purchase decisions. Howard and Sheth (1969) focus on four stages of attitudes, perceptions and learning, while Engel (1983) focuses on decision making as problem solving. This section of the paper will guide the reader through a series of theoretical and applied behavior models that provide the foundation, structure and eventual answer to the question: what drives consumer choice? 3 Constructive Consumer Choice Processes in Summary Is the consumer choice process adaptive? Are consumers agile enough to recognize at a moment in time through reflection that a different approach might yield a more acceptable outcome? Bettman, Luce and Payne (1998) say yes, and support it with five summary concepts that will be presented here. Consumers are goal oriented and develop their process for making a choice to achieve their goal. Driving factors include motivation, like increasing decision quality, reducing effort level or decreasing negative emotions.
Because consumers are rational in nature, they also recognize that limited cognitive processing capability requires them to selectively process the most relevant information (Bettman, Luce & Payne, 1998). Continuing with the theme of information, consumers do differ in the rules and strategies they employ when collecting and analyzing it. Several argue that increased knowledge and expertise better enable the consumer to assess the information and select more effective decision strategies (Alba & Hutchinson, 1987; Russo & LeClerc, 1994; West, Brown & Hoch, 1996).
Even further down the discussion with information, Bettman, Payne and Luce (1998) state that how the information is displayed and presented can also affect/influence the consumer’s decision. Using Slovic’s (1972) principle of concreteness as the basis for their argument, they demonstrate that consumers are more likely to use information “that is explicitly displayed and will use it in the form it is displayed, without transforming it” (p. 202). Consumers will also vary their process when product categories are comparable and noncomparable.
Comparable choices are product alternatives in choice sets that have similar attributes, like a BMW versus a Mercedes. Noncomparable categories involve no similar attributes, like comparing cellular phone to a Mercedes. In those kinds of situations consumers 24 tend to “develop more abstract attribute or compare overall evaluations” (Bettman, Payne & Luce, 1998, p. 203) to process the information. Time constraint is the fifth and final contributing element to an adaptive decision process. Time dictates availability to process, compare and choose.
Consumers will limit each phase as appropriate to accommodate the constraints (Betmman, Payne & Luce, 1998). Howard-Sheth Model Four stages exist within the Howard-Sheth (1969) model, all to occur sequentially, building cumulative momentum to aid the consumer in his choice: (a) inputs, (b) perceptual constructs, (c) learning constructs, and (d) outputs. The inputs a consumer receives are a series of informational objects around the brand or product that can be categorized in three ways, significative, symbolic or social. Information around the physical attributes of a product, like features and functionality are significative.
Verbal and visual information in the form of advertising is symbolic, and social content is received through the consumer’s social environment by means of product opinions and recommendations (Warner, 1997). Perceptual constructs are built as a result of the informational inputs. While the inputs serve as the foundation on which to develop a purchase decision, the perceptual construct further refines the base to filter those inputs and frame them in a manner that is comprehensible for the consumer. Two different actions occur here to achieve that objective, contributing to the goal: stimulus ambiguity and overt search.
Stimulus ambiguity is not an action, rather an experience; however, the phenomenon describes a state of confusion and lack of clarity around the messages attempting to be received by consumer that thwarts the progress. While many might consider an obstacle like this to detract from the goal, it contributes strongly by leading the consumer to an overt search, concentrating on collecting intelligence/information about the subject of the 25 message. Not every consumer experiences ambiguity and not every consumer will conduct an overt search.
These two actions result in a stronger, more vetted set of perceptual constructs that prepare the consumer to learn (Warner, 1997). Learning constructs are strongly influenced by the preceding perceptual constructs. Four learning constructs exist, each driving different reactions, although each equally driving choice: (a) motivation, (b) brand comprehension, (c) confidence, and (d) attitude. Consumers are motivated to satisfy a perceived need, and it is this internal motivation that influences the evaluative criteria used to select the appropriate product to purchase (Warner, 1997).
Howard and Sheth (1969) argue that perceptions can be influenced. Brand comprehension simply defined is a consumer’s overall perception of a product. Targeted messaging, previous experience with the brand and external recommendations from trusted sources are three primary factors that influence and drive product choice over another. Brand comprehension, Howard and Sheth (1969) argue, has an equally powerful capability of influencing consumer attitudes toward particular products (Warner, 1997).
The work and navigation through a series of stages up to this point all contributes to the level of confidence the consumer experiences toward the capability of a particular product to satisfy his initial, perceived need. Confidence determines the next step. Does the consumer feel confident that he is on the right path, that enough information has been collected and properly filtered to aid in his decision? Does he feel as though he has missed something, or has the work up to this point secured his position allowing him to develop an attitude about his selection?
Attitude and confidence drive the intention to purchase, which leads to the actual purchase or output. Attitude is developed as a result of the confidence created by consumer wile 26 forming hi opinion through collecting information by way of inputs, developing perceptions as a result of learning from those perceptions. The output is the purchase. Engel Model The Howard-Sheth Model (1969) places greater emphasis on perception, attitudes and learning, while the Engel Model (1983) concentrates on decision-making processes.
The Engel Model (1983) views consumer decision-making as a problem-solving exercise, assuming the purchase of a particular product will resolve the initial problem. The most common sequence within a decision-making framework introduces six stages of the consumer experience: (a) define the problem, (b) generate alternative solutions, (c) evaluate alternatives, (d) decide on the solution, (e) implement decision, and (f) monitor results. Engel (1983) enhances the sequence by overlaying the driving human factors behind the sequence, preserving the process.
Motivation drives the recognition of a need to define the problem in the first stage. To generate alternative solutions in the second, the consumer must conduct an information search. The evaluation stage is where consumers employ a series of decision rules and strategies, dependent on the amount of information and the limitations of their processing capacity to eventually arrive at a decision (Warner, 1997). Theory of Reasoned Action (TRA) Fishbein and Ajzen’s (1975) Theory of Reasoned Action (TRA) stems from social psychology and the focus on the determinants of consciously intended behavior.
In its simples form, the theory suggest that an individual’s actions are a direct result of his intentions that are based on personal attitudes and social norms toward a particular behavior. Attitudes related to the evaluation of personal beliefs that a behavior will generate a certain outcome and 27 consequence. Intentions to engage in particular behavior are additionally affective by subjective norms, “the person’s perception that most people who are important to him or her think that he or she would or should not perform the behavior in question” (Fishbein & Ajzen, 1975, p. 302).
It is a social filter of sorts, a conscience to play back the potential outcome before it occurs to allow the individual to assess the risks and rewards. Theory of Planned Behavior (TPB) Recognizing that TRA as a predictor of actual behavior was solid in its fundamental assumptions, was at the same time limited with respect to analyzing only those behaviors that were under an individual’s control, Ajzen (1991) introduced the Theory of Planned Behavior (TPB). TPB supplements TRA by appending the control factor. TPB adds the perceived behavioral control component as a determinant of intentions to perform a behavior.
Perceived behavioral control refers to an individual’s assessment of “the presence or absence of requisite resources and opportun