Is this investment right for me?
Excerpt 2: A Request for Competitive Benchmarks
report of last year’s performance and their plans for the next year to a board of directors composed of experienced top managers. The board members’ questions in bold and italics are requests for benchmark information.
The fact that the board members had to request benchmark information from the student team indicates the students were unaware of the crucial role benchmarks play in the audience’s decision-making process (note: the names of the students have been changed). In the fi rst excerpt, a board member requests benchmark information in order to evaluate the interest rate and type of loan the team secured. Given that interest rates rose the previous year, locking in the lower rate seemed to be a good decision for the team. In the second excerpt, the chairman of the board requests benchmark information about competitors’ fi nancial results. Initially the team’s fi nancial results sound outstanding. But when compared to their competitors’ results, the team’s results turn out to be about average.
TWO EXCERPTS FROM A PRESENTATION GIVEN BY MBA STUDENTS TO AN EXPERIENCED BOARD OF DIRECTORS Excerpt 1: A Request for Interest Rate Benchmarks
Student VP of Finance: Just give you a quick overview of the way we went about funding our expansion. Cost of our expansion was approximately $36.6 million. We had 15 million of long-term debt that we renewed at 14% fixed rate amortized for the next five years. We wanted to finance construction [of a plant expansion] with as much internal generated funds as possible. And we had $19.2 million in cash from market securities at the end of last year.
We negotiated a $20 million line of credit with the bank to help us finance the expansion and also to help with the operating expenses. As a result, using our cash and our line of credit we were able to fund the expansion, and since then the line of credit has now been reduced to
$12.5 million. And if we can, we hope to continue to reduce this as fast as possible.
Board Member: How does the 14% fixed rate compare to what benchmark rates or govern- ment rates were at the time for the same term?
Student VP of Finance: The prime rate at the time was 12% with a prime plus two. And talking to people in our bank this was an average and much more favorable rate in relation to our competitors.
Board Member: You have prime plus two fixed though for five years, not floating with the prime?
Student VP of Finance: Right, yes. Prime was at 12% at the time that we negotiated.
Student VP of Marketing: It is now at 19.
Board Member: It’s now 19. So the outlook for rates was that they were going to be rising.
Student VP of Finance: Right. Inflation was running at a fairly good clip, and we thought the best thing to do was to try to lock in a rate. If you don’t have any questions I would like to go over the results of the labor negotiations.
Excerpt 2: A Request for Competitive Benchmarks
Student CEO: Okay. Fine. Okay. Very quickly I want to just give you the highlights from last year. That is on July 1 we opened the new $38 million factory and warehouse. Also during the
year we submitted in our new advertising campaign, and we participated in the labor nego- tiations which Aditya just went through. Currently, we are producing two products for the commercial market inside the nation. A high sudser detergent, a medium sudser, and we’re producing one product in the industrial market.
Right now, we have overall a 31% market share. And on a financial note net income rose $6.9 million last year to $23.7 million this year. This was on an increase to retained earnings of $22.5 million versus last year at $5.7 million. Much of this increase is due as Bob mentioned to the additional capacity that we have with the factory, with the new factory. It allowed us to do two things. One was to produce more goods for sale within the commercial end of the business, and the other allows us to also get into the industrial market.
Stock price rose from last year roughly at this time from the low 50s to almost $90 per share right now. And earnings per share climbed from $6.96 last year to $23.68. Return on investment for the year was 23%, and return on equity was 42%. So with that in mind, what we would like to do is present to you our main concerns for next year.
Chairman of the Board: No. We would like to discuss that a little more. Do you have any comparative numbers for the competitors in our nation? What did the competitors’ stock prices do?
Student CEO: In our nation, Team 3, Yun Wang’s team is at $90 stock price and Brenda Kelley, the third competitor, is at $110. I think it is. She’s number one in the nation and in the world right now which is at $110. We are in at $90. We are sort of in the thick of things.
Audience-Provided Benchmarks
Audiences sometimes compare recommended alternatives to personal or subjective standards or benchmarks. A study of recruiters screening résumés for a supervisory position fi nds recruiters use themselves as benchmarks: The more similar the applicant’s background is to the back- ground of the recruiter evaluating that applicant, the higher the probability the applicant will be hired. 84 Consumers often generate a subjective reference price for a product based on its quality, and then evaluate different brands by comparing their subjective reference price to the actual price. 85 A study of renters’ decision making gave renters descriptions of seven houses with each house described in a separate booklet. Of all the comparisons the renters made, 71%
were comparisons to a subjective standard, the remaining 29% were comparisons among the seven houses. 86
When comparative information about alternatives is missing from a document or presentation, expert audiences may supply it. Interestingly, the comparative information consumers retrieve from their memories has a stronger effect on their decisions than comparative information provided by external sources. 87 If consumers are unable to retrieve missing benchmark information from their memories, they may attempt to infer it, especially when the information has high subjective importance. 88
In a study comparing the effects of missing information on experts and novices, expert and novice bicycle buyers received written descriptions of bicycles in which important comparative information such as the weight of the bicycle was missing for some of the bicycles. Although novices were unconcerned that information was missing, experts who understood the impor- tance of the missing benchmark information tried to guess what it would be for each bicycle
before making their decisions. 89 Similar expert/novice differences regarding missing benchmark information have been reported among other types of consumers. 90 However, even experts may not take the time necessary to infer missing benchmark information if the amount of miss- ing information is large or if they are not confi dent in their ability to make the inferences accurately. 91
Decision Schemata of Expert Audiences
Decision Schemata: The Audience’s Decision-Making Framework
When experts’ decision criteria and benchmarks are plotted out, they form a grid or decision matrix . Table 1.1 shows an example of a decision matrix for deciding on a laptop computer in 2015. The fi rst column of the matrix lists possible decision criteria. The fi rst row indicates the recommended laptop and benchmark, or alternative, laptops. The cells of the matrix give the values for the recommended and benchmark laptops. But do such matrix representations have any psychological reality? Do they refl ect something about the underlying structure of decision-making expertise? Research suggests the answer is yes . Expert decision makers appear to have decision-specifi c knowledge structures, 92 or mental representations, stored in their long-term memories that correspond to decision matrices. It is these knowledge structures that lead experts to mentally represent each alternative they consider as values along a number of attributes or decision criteria. 93
Cognitive scientists call such knowledge structures schemata . 94 Schemata are mental frameworks that reside in an expert’s long-term memory and into which new information can be fi tted and made sense of. 95 Other terms for schemata include scripts , 96 frames , 97 mental models , 98 templates , 99 and knowledge representations . 100
Some scientists believe schemata are stored in the prefrontal cortex of the brain. 101 Other sci- entists fi nd evidence for schemata being distributed over different areas of the cortex. 102 More recently, neuroscientists have demonstrated that the medial prefrontal cortex, interacting with the hippocampus, is involved in assimilating new information into existing schemata and in retrieving it later as needed for decision making. 103
Schemata, like the decision matrix illustrated in Table 1.1 , consist of slots that indicate the expert’s knowledge relevant to a particular decision as well as empty slots that can be fi lled with situation-specifi c information called slot values . 104 In Table 1.1 the slot values for the laptop
TABLE 1.1 Decision Matrices Incorporate Both Decision Criteria and Benchmarks DECISION
CRITERIA
RECOMMENDATION Laptop under consideration
BENCHMARK 1 Comparable laptop
BENCHMARK 2 Current laptop Year, make, and
model
2015 13”
Apple MacBook Air
2015 13”
Apple MacBook Pro with Retina display
2014 11”
Apple MacBook Air
Processor speed 1.6GHz 2.9GHz 1.3GHz
Memory 4GB SDRAM 8GB SDRAM 4GB SDRAM
Storage 256GB Flash Storage 512GB Flash Storage 128GB Flash Storage
Display quality Very good Excellent Very good
Retail price $1,200 $1,800 $1,000 (new)
under consideration are 2015 13” Apple MacBook Air , 1.6GHz , 4GB SDRAM , 256GB Flash Storage , Very Good , and $1,200. Sometimes the slots in an expert’s schema may already be fi lled with default values (e.g., an expert’s default value for the retail price of the recommended laptop in Figure 1.1 might be more than $1,000 ). But when experts start to instantiate their sche- mata, they usually replace those default values with the actual values of the alternatives under consideration. 105
The audience’s long-term memory is largely composed of schemata, 106 and much of what we call expertise is schema driven. Expertise has been shown to be schema driven in accounting, 107 financial analysis, 108 physics, 109 algebra ,110 and medicine. 111 Expertise in making parole decisions has been shown to be schema driven. 112 Wall Street analysts’ ability to value firms and their acquisitions has been shown to depend on the analysts’ schemata. 113 In addition, the ability of business leaders and military officers to effectively lead their subordinates has been shown to depend on their possessing complex and highly organized schemata. 114 Similarly, the ability of cyber security analysts to effectively respond to cyber attacks has been shown to depend on the analysts possessing robust incident handling and cyber attack schemata. 115
The knowledge experienced consumers have about evaluating brands in a product class may also be thought of as schemata. 116 A consumer’s schemata for particular brands “largely determine how the consumer reacts to advertising.” 117 Expert supervisors evaluate employee performance using highly differentiated performance schemata that include performance criteria relevant to specifi c jobs. 118 Because more experienced supervisors use more differentiated performance sche- mata, they are able to provide more accurate ratings of their employees than their less-experienced colleagues. 119
Many cognitive scientists agree that schemata are critical to decision making and high-level thought. As the authors of a study of 713 product decisions conclude, “Decisions refl ect the schemata employed in the decision-making process.” 120 Others argue that “a rich collection of schemata constitutes an essential engine for high-level thinking in a domain.” 121 John R. Ander- son, a leader in the fi eld of cognitive science, identifi es both reasoning and decision making as
“schema-based inference processes” that can approach the level of normative decision-making principles if people “lock into the right schema.” 122 Moreover, he has since confi rmed this view. 123
Perhaps in part because of the similarities between internal schemata and decision matrices, consumers spontaneously create alternative-by-attribute decision matrices when deciding among different products and sources of credit. 124 Figure 1.1 shows the decision matrix one consumer cre- ated while comparing fi ve different brands (labeled A through E) of do-it-yourself storage buildings along six different decision criteria (Warranty, Size, Materials, Assembly, Ease, and Price). 125 Not only did the consumer rearrange randomly presented information, she also made calculations to fi ll empty schema slots, re-scaled slot values that were hard to compare, and ranked each of the six attributes or decision criteria.
Decision Schemata as Guides to the Decision-Making Process
Schemata not only store important information in an organized way, they also guide the pro- cess of decision making. Ultimately, schemata guide behavior. 126 Social psychologists Susan Fiske and Shelley Taylor explain just how fundamental schemata are: “Once cued, schemas affect how quickly we perceive, what we notice, how we interpret what we notice, and what we perceive as similar and different.” 127 Schemata direct attention during information search, specify which
information is relevant and which is irrelevant, code information, organize it in memory, direct the retrieval of information from memory, and specify which important information is missing. 128 Voters’ schemata can affect their attention to, interpretation, and recall of political information. 129 The schemata of experienced voters lead them to give attention to and to seek information about political events that inexperienced voters tend to overlook. 130 The infl uence of schemata can even be retroactive. An audience member’s current schema can help them retrieve memories that were formed before the schema had been acquired. 131
Well-developed schemata provide many advantages for expert audience members. For example, voters with more highly developed political schemata are able to produce higher quality argu- ments about current issues than less sophisticated voters. 132 The ability of experienced physicians to recognize the signifi cance of secondary physiological measurements 133 can be credited to their more complete schemata. 134 Schemata help experienced consumers recall product information, make accurate inferences about the product information with which they are presented, and put new information into context quickly. 135 Internal knowledge structures, or schemata, allow expert business appraisers to quickly search for and assess the information they need to evaluate the worth of a company. 136
Limitations of Decision Schemata
The schemata of expert audiences sometimes have negative effects on the decisions they make. When activated, an expert’s schemata may lead the expert to distort new information that is inconsistent with their schemata. 137 An expert’s schemata may also lead them to discount schema-inconsistent information altogether. 138 In addition, an expert’s schemata can limit the types of problems they perceive. For example, business executives have been found to defi ne problems largely in terms of their functional expertise, such as marketing, fi nance, or operations, and not recognize
FIGURE 1.1 Decision Makers Spontaneously Create Decision Matrices Source: Coupey (1994, p. 90)
business problems outside their function area. 139 Sometimes experts try to apply their schemata to decisions outside their domain of expertise. But experts’ schemata are domain specifi c and cannot be used as the basis of expertise in a second domain even when that domain appears to be similar. 140
Such limitations can lead experts to make biased decisions, particularly if they try to use their schemata to make nonroutine decisions or to solve ill-structured problems. 141 In such cir- cumstances, expert audiences do not necessarily make better decisions than novices. 142 Experts’
schemata can even incline them to overlook information simply because it is not formatted in the typical way. For example, expert, but nonprofessional, investors have been found to use comprehensive income information only when it is presented in the format they have come to expect. 143
The Shared Decision Schemata of Groups
Shared schemata underpin the corporate cultures of diverse organizations 144 and guide the decision-making processes of effective groups and teams. 145 A study of 25 four- to six-person groups of business executives making hiring decisions reports that the executives in each group shared the same schema concerning the characteristics of suitable applicants, tended to search for the same information about each applicant, mentioned that same information in group discussions, and used that information to make group decisions. When group members repeated information during their discussions, they were more likely to repeat information that was schema relevant than schema irrelevant. Any information that was unshared, or known by only one group member, was more likely to be mentioned in group discussions if it were schema-relevant information than if it were schema irrelevant. Finally, the study reports that the most infl uential person in each group was the group member with the most complete knowledge of information relevant to the group’s shared schema. 146
Factions within a group who are initially in a minority can successfully advocate their preferred alternative by appealing to shared knowledge structures or schemata upon which all members agree. 147 Any alternative consistent with the group’s shared schema is easier for the minority to defend. That alternative is also more likely to be chosen by the group as a whole, although usually after some delay. 148 The group is more likely to choose that alternative even when it is not demon- strably correct, as is often the case with jury decisions. 149 However, if group members do not share a schema, or if they possess multiple and confl icting schemata, the group will tend to choose an alternative on the basis of majority/plurality rule. 150
Shared mental models, a type of shared schema, predict both team processes and team perfor- mance. 151 They are associated with higher team satisfaction, 152 increased within-team helping behavior, 153 reduced absenteeism, 154 and greater team effectiveness. 155 They have an even greater effect on team performance than demographic similarities among team members. 156
The benefi ts of shared schemata or shared mental models arise because they enable team members to anticipate each other’s information needs, communicate effi ciently, and work in sync. 157 In many cases, a shared mental model about the task is necessary if a team is to complete its task successfully. 158 Furthermore, as the differences among the team members’
mental models decrease, team performance increases. High-performing teams tend to have not only more widely shared mental models than low-performing teams, but also more elaborate ones. 159 Teams that fail to develop shared mental models tend to be uncoordinated and to perform poorly. 160
Decision Schemata of Novice Audiences
Novices’ Less Well-Developed Decision Schemata
Important differences have been discovered between expert and novice consumers and the way they make decisions. 161 Important differences have been discovered between many other expert and novice audiences and how they make decisions as well. 162 Interestingly, expert and novice decision makers differ not only at the cognitive level, but also at the neurological level. Neuroscientists fi nd evidence that expertise leads to a functional reorganization of the brain. 163 When making a deci- sion that falls within their domain of expertise, expert audience members activate different brain systems than novices activate. 164
At the cognitive level, the most fundamental difference between expert and novice audiences is that, in contrast to experts, novice audiences come to documents, presentations, and group meetings without well-developed or appropriate schemata in mind. 165 In fact, most expert/novice differ- ences can be attributed to “quantitative and qualitative differences in their respective stores of relevant problem schemas.” 166
Thus, novice audience members may lack the well-formed and matrix-like decision schemata they need to make a good decision. Instead of a matrix-like schema, a novice’s schema may consist of a simple list of reasons for the alternative they prefer and against the alternative they dislike.
If the novice has more experience, her schema may consist of a random list of pros and cons for several alternatives. 167 Unlike experts who attempt to estimate each alternative’s overall value along a specifi c set of dimensions or decision criteria, novices try to fi nd unique reasons for and against each alternative they consider. 168
Not surprisingly, novice audiences employ fewer decision criteria when making a decision than experts. 169 For example, novice consumers of insurance evoke fewer decision criteria than experts when trying to choose the best insurance policy. 170 Novice consumers of newspapers search for information about fewer attributes, or decision criteria, than more knowledgeable consumers when choosing among newspapers. 171 Even when information pertinent to an expert’s decision criteria is provided to them in ads (e.g., the fact that a computer has 8GB SDRAM), novice consumers may neglect to process it. 172 Unless given incentives, novices will process only the benefi t information ads contain (e.g., the computer is “Great for gamers”).
Even when novice audiences apply the decision criteria experts possess, they may not weight those criteria appropriately. For example, novice investors weight the decision criteria provided by fi nancial analysts’ forecasts less appropriately than expert investors. 173 Novice consumers give more weight to nonfunctional dimensions of products such as the brand name and packaging than expert consumers. 174 Because novices lack the prestored decision criteria that experts possess, they sometimes try, without much success, to create them. 175
Novice audiences also use fewer benchmarks and make fewer comparisons than experts when making decisions. Novice consumers of insurance are less likely to make pair-wise comparisons of one policy to another than experts. 176 Novice consumers in general make fewer comparisons because they have less knowledge about alternative products, an important category of benchmarks, than expert consumers. 177 Novice fi nancial analysts are less likely to compare a fi rm’s fi nancials to internal norms or benchmarks than expert analysts. 178 Even when novice audiences have access to the benchmarks that experts rely on, they may not use them. A study of consumers reading comparative and noncomparative ads in order to choose among brands fi nds that, unlike the expert consumers, novice consumers fail to use the comparative product information comparative ads provide. 179