Bauer 2013

8/10/2019 Bauer 2013

1/20

ORIGINAL EMPIRICAL RESEARCH

Managerial decision making in customer management:adaptive, fast and frugal?

Johannes C. Bauer & Philipp Schmitt &

Vicki G. Morwitz & Russell S. Winer

Received: 30 May 2011 /Accepted: 15 October 2012 /Published online: 1 November 2012# Academy of Marketing Science 2012

Abstract While customer management has become a top priority for practitioners and academics, little is known about how managers actually make customer management decisions.Our study addresses this gap and uses the adaptive decisionmaker as well as the fast and frugal heuristics frameworks togain a better understanding of managerial decision making.Using the process-tracing tool MouselabWEB, we presentedsales managers in retail banking with three typical customer management prediction tasks. The results show that a majorityof managers in this study are adaptive in their decision makingand that some managers use fast and frugal heuristics. Usage of adaptive decision making seems to be mainly driven by lowobjective task difficulty, the use of fast and frugal heuristics byexperience. While adaptive decision making does not impact predictive accuracy, usage of fast and frugal heuristics is asso-ciated with proportionally greater use of high predictive qualitycues and a significant increase in accuracy. Hence, the existingskepticism concerning heuristics should be questioned.

Keywords Customer management . Adaptive decisionmaking . Fast and frugal heuristics . Process-tracing .Mouselab

Introduction

In the past decades, marketing academia and practice haveshifted their focus from a product- to a customer-centricview (Rust et al. 2004 ). Rather than maximizing brandequity, many firms have made customer management a top priority and aim to improve their performance by buildinglong term, profitable customer relationships (e.g., Bolton1998 ; Hanssens et al. 2008 ; Rust et al. 2010 ; Verhoef et al.2010 ). The basic premise of this trend is that customers areone of the firm s most important assets (Gupta et al. 2004 ).To assess the economic value of this asset, two key metricsare commonly used: customer lifetime value (CLV; the net present value of all earnings from a specific customer duringthe time of her/his relationship with the company minus thecosts of attracting, retaining, and servicing this client) andcustomer equity (CE; the sum of all individual lifetimevalues generated by the company s current and prospectivecustomers) (e.g., Berger and Nasr 1998 ; Blattberg andDeighton 1996 ; Rust et al. 2004 ). To increase CLV andmaximize CE, many firms strategically focus on customer acquisition, retention, and cross-selling (Blattberg et al.2001 ). These activities are considered to be the main driversof CLV (Gupta and Zeithaml 2006 ).

To that end, considerable research focuses on developingmodels to help firms make decisions about marketing re-source allocation, customer segmentation, and customer selection (Kumar et al. 2006 ; for a review, see Jain andSingh 2002 ). However, despite extensive research linkingfirms marketing actions to CLV and CE, little is knownabout the decision making processes of individual managerswhen they are faced with important questions concerning

J. C. Bauer ( * )Institute of Retail Management, University of St.Gallen,Dufourstrasse 40a,9000 St.Gallen, Switzerlande-mail: [email protected]

P. Schmitt School of Business and Economics, Goethe University Frankfurt,

Grueneburgplatz 1,60323 Frankfurt, Germanye-mail: [email protected]

V. G. Morwitz : R. S. Winer Stern School of Business, New York University,Tisch Hall, 40 West 4th Street, New York City, NY 10012, USA

V. G. Morwitze-mail: [email protected]

R. S. Winer e-mail: [email protected]

J. of the Acad. Mark. Sci. (2013) 41:436 455DOI 10.1007/s11747-012-0320-7

8/10/2019 Bauer 2013

2/20

how to acquire and retain customers and how to best cross-sell. This is surprising as managers today have access toextensive databases and are constantly challenged to processsubstantial amounts of customer information in order tomake accurate decisions that increase CLV. Wierenga et al.(1999 ) rightfully point out that the marketing literatureoffers countless recommendations concerning how manage-rial decisions should be made but, regrettably, less insight into how managerial decisions actually are made. Whilethere is a general shortage of studies investigating manag-ers decision making processes, this gap is particularly pro-found in the area of customer management (Van Bruggenand Wierenga 2010 ).

Therefore, the goal of our research is to gain a better understanding of managerial decision making in customer management by investigating the decision making process,the factors impacting it, and the factors impacting the pre-dictive accuracy of managers decisions. In order to do so,we employ two research frameworks which build onSimon s (1955 ) concept of bounded rationality: (1) theadaptive decision maker framework (Payne et al. 1993 ) and(2) the fast and frugal heuristics framework (Gigerenzer and Goldstein 1996 ). The former states that decision mak-ers are flexible and that their selection of decision strate-gies is contingent on a variety of factors; the latter assertsthat people use simplifying rules to come up with smart decisions quickly (fast) and with a limited amount of information (frugal). We empirically test these two frame-works in the context of customer management decisions.Specifically, we try to answer the following questions withregard to managers decisions concerning customer acqui-sition, retention, and cross-selling: (1) Are managers adap-tive in their decision making and/or do they use fast andfrugal heuristics? (2) Which characteristics of the task andthe decision maker drive the use of adaptive decisionmaking and of fast and frugal heuristics? (3) Which char-acteristics of the task and the decision maker impact the predict ive accuracy of managers decisions?

To answer these questions, we used a process-tracingapproach and investigated the decision making behavior of 49 sales managers of a leading German bank by employingMouselabWEB (MouselabWEB 2012 ). Managers partici- pated in three different tasks, one of each related to acqui-sition, retention, and cross-selling. In each task, they were provided with several pieces of customer information for a set of real bank clients and asked to select those customersthey predicted would display a certain behavior (1) obtaina new consumer loan in the cross-selling task, (2) canceltheir checking account in the retention task, and (3) refer a new customer to the bank in the acquisition task within a specified timeframe. Customer information was hidden onthe computer screen and had to be requested by clicking onthe respective box. This procedure allowed us to monitor (1)

what information managers used, (2) in which order theyaccessed the information, and (3) how long they took toreach a decision. We then examined managers decisionmaking processes and assessed their decision accuracy bycomparing their predictions to the actual behaviors of thecustomers.

Our research makes several managerial and theoreticcontributions. First, by explaining which factors drive gooddecisions in customer management, our results can improvemanagers decision making, which ultimately increases thelifetime value of the firm s customers and thus customer equity. Second, combining a process-tracing method with a prediction task not only offers a rich, descriptive account of decision making in customer management but also allows usto simultaneously assess decision makers accuracy. Third,our study is the first to empirically test the adaptive decisionmaker framework as well as the fast and frugal heuristicsframework in the context of customer management, an area of high strategic importance for many firms. In sum, by providing comprehensive insights into the way managersactually make decisions and the factors that determine deci-sion quality, this research contributes not only to the aca-demic literature on customer management but also todecision making research in the realm of managerial cogni-tion. From a theoretical perspective, our study adds to theunderstanding of the relationship between people s usage of fast and frugal heuristics and their decision accuracy. Due toinconsistent results in the judgment and decision makingliterature, the latter aspect is, to date, a subject of controver-sial debate.

The remainder of this article is organized as follows: Wefirst provide an overview of the existing literature on man-agerial decision making in the fields of managerial cognitionand customer management. Then, we provide the theoretical background by introducing the concepts of bounded ratio-nality, adaptive decision making, and fast and frugal heu-ristics. A description of the method and the results of our study follow. We conclude by discussing managerial impli-cations, limitations of our study, and directions for further research.

Literature review

Managerial cognition and strategic decision making

To arrive at the optimal solution for a decision problem, themanagerial decision making literature proposes the follow-ing six steps: (1) definition of the problem, (2) identificationof the criteria, (3) weighting of the criteria, (4) generation of alternatives, (5) rating of each alternative on each criterion,and (6) computation of the optimal decision (Bazerman1998 ). In their daily decision making, however, managers

J. of the Acad. Mark. Sci. (2013) 41:436 455 437

8/10/2019 Bauer 2013

3/20

are faced with a variety of complex problems. Scarcity of time (e.g., Mintzberg 1973 ) and cognitive limitations (e.g.,Miller 1956 ) make it difficult for managers to process allavailable information and to make fully rational decisionsconcerning all possible alternatives (Simon 1955 , 1997 ).Instead, decision makers typically depart from rational de-cision making and use multiple strategies which depend onthe type of the problem (e.g., Anderson 1983 ; Hickson et al.1986 ; Nutt 1984 ). Rather than optimizing decision out-comes, managers make trade-offs between decision accura-cy and costs (i.e., time and cognitive effort) and choosestrategies that lead to satisfactory (versus optimal) outcomes(for a review, see Eisenhardt and Zbaracki 1992 ; Walsh1995 ).

Several studies have also examined how experienceaffects managerial decision making. For example, Day andLord ( 1992 ) show that experts (38 CEOs) categorize ill-structured problems faster than novices (30 MBA students).They explain that experienced managers expedite decisions by using well-developed heuristics ( rules of thumb ) in theearly stages of the decision making process. Similarly,Isenberg ( 1986 ) finds that experienced managers request less information and act sooner than undergraduate students.Other researchers have found that experienced decisionmakers use both reasoning and intuition simultaneously(Dane and Pratt 2007 ; Fredrickson 1985 ) which, in turn,leads to faster decision making (Wally and Baum 1994 ).One reason for this might be that experts have richer knowl-edge structures than novices (e.g., Fiske et al. 1983 ; Lurigioand Carroll 1985 ; Wagner 1987 ). However, even thoughthey are capable of making faster decisions, experiencedmanagers also tend to be prone to systematic biases resultingfrom overconfidence (e.g., Einhorn and Hogarth 1978 ;Mahajan 1992 ). The literature on managerial cognition andstrategic decision making provides considerable evidencethat overconfidence not only is a common phenomenonamong managers but also leads to lower accuracy (e.g., Neale and Bazerman 1985 ; Russo and Schoemaker 1992 ;Schwenk 1986 ). Overconfident managers overestimate their chances of success when making decisions about market entries (Camerer and Lovallo 1999 ), product introductions(Simon and Houghton 2003 ), and corporate investments(Malmendier and Tate 2005 ). Thus, more experience resultsin faster decisions but not necessarily in higher decisionaccuracy.

The literature on managerial cognition and strategic de-cision making has shown that managers use various decisionstrategies and accelerate decision making processes by re-lying on their experience and intuition. However, there is noempirical study which provides a comprehensive account of the task- and manager-related factors (and their interactions)that drive adaptive or fast and frugal decision making andtheir impact on decision accuracy.

Decision making in customer management

Researchers in customer management have developed nu-merous mathematical models to support managerial decisionmaking (for an overview see Reinartz and Venkatesan2008 ). Specifically, models have been developed to calcu-late CLV and CE (e.g., Berger and Nasr 1998 ; Dwyer 1997 ),to indentify the antecedents of CLV (e.g., Reinartz andKumar 2003 ; Rust et al. 2004 ), to determine the optimal balance between customer acquisition and retention spend-ing (e.g., Berger and Nasr Bechwati 2001 ; Blattberg andDeighton 1996 ; Reinartz et al. 2005 ), and to provide insightsinto a firm s customer base (e.g., Reinartz and Kumar 2000 ;Schmittlein and Peterson 1994 ; Schmittlein et al. 1987 ).However, there is a shortage of descriptive studies that investigate how individual managers make customer man-agement decisions. A notable exception is the work of Wbben and Wangenheim ( 2008 ), who show in a customer management context that if one were to apply certain heu-ristics (i.e., the hiatus and persistence heuristics), the deci-sions would be at least as accurate as results from the Pareto/ NBD and BG/NBD stat istical models. Yet they do not investigate if managers actually use these heuristics whenmaking decisions. In the area of customer acquisition,Morwitz and Schmittlein ( 1998 ) show in a direct marketingcontext that an interplay of managerial judgments and sta-tistical models lead to more accurate decisions than doesmanagerial judgment alone and thus ultimately lead to in-creased profits. However, Morwitz and Schmittlein ( 1998 )do not examine managers decision making processes. In a lab experiment, Hoch and Schkade ( 1996 ) come to a similar conclusion. Accordingly, they recommend providing man-agers with decision support systems.

Existing studies show the effectiveness of different deci-sion strategies but essentially treat the process leading to a decision as a black box. To address this gap, we investigateindividual decision making behavior and use a process-tracingmethod which allows us to examine the intervening steps between the informational input and the decision output.

Theoretical background

Bounded rationality

The concept of bounded rationality was first introduced in a seminal article by Herbert A. Simon ( 1955 ). It questions theunrealistic picture of human decision making that assumesfull rationality and complete knowledge. Instead, Simon(1955 ) stressed that (1) a focus on how decisions are madeis needed (instead of only looking at what decisions aremade), (2) a more realistic view of the decision maker hasto take into account their limited computational capacity

438 J. of the Acad. Mark. Sci. (2013) 41:436 455

8/10/2019 Bauer 2013

4/20

(instead of assuming global rationality), and (3) understand-ing decision processes has to account for the interaction of cognitive limitations and the environment in which thedecision is made (i.e., a variety of task- and context-relatedfactors). This implies that any analysis of human decisionmaking must be concerned not only with the content of decisions (substantive rationality) but also with the decisionmaking process (procedural rationality) as Simon ( 1981 )notes. Such an approach is needed to achieve the core goalof the concept of bounded rationality: a more realistic viewof human decision making.

Adaptive decision making

A well-known framework that builds on Simon s ideas of bounded rationality is adaptive decision making. It showsthat decision makers use different decision strategies con-tingent on a variety of task, context, and individual differ-ence factors (Payne et al. 1993 ), and that effort-accuracyconsiderations play a crucial role in deciding how to decide.In line with Simon ( 1955 , 1990 ), adaptive decision makingassumes that decision makers are highly flexible and adjust to their task environment. Not only do different decisionmakers use different strategies but the same individual willalso use different decision strategies for different problems.

Payne ( 1976 ) argues that human decision strategies mainlydiffer along two dimensions: (1) the amount of informationsearched as either constant or variable across alternatives and(2) the sequence of information searched as either inter-dimensional (i.e., successively evaluating alternatives on the basis of different evaluation criteria) or intra-dimensional (i.e.,comparing different alternatives on the basis of one evaluationcriterion). He explains that each combination of these twoinformation search dimensions characterizes a particular deci-sion strategy that people use to evaluate a set of alternatives before making a final choice. Figure 1 illustrates Payne s(1976 ) two-dimensional classification of decision strategies.

The four resulting decision strategies are characterized asfollows:

(1) The linear model is characterized by a constant andinter-dimensional information search: each alternativeis evaluated separately by additively combining thevalues of all evaluation criteria into an overall value.

(2) The conjunctive model is characterized by a variableand inter-dimensional information search: the first al-ternative that exceeds certain cutoff values on all eval-uation criteria will be chosen.

(3) The additive difference model is characterized by a constant and intra-dimensional information search:differences between alternatives for selected evaluationcriteria are summed together in order to make a choice(Tversky 1969 ).

(4) The elimination-by-aspects model is characterized by a variable and intra-dimensional information search: first the most important evaluation criterion is determined andthen all alternatives that do not meet a certain cutoff value for this criterion are eliminated. The process con-tinues with the second most important evaluation crite-rion, then the third, and so on (Tversky 1972 ).

Past research has shown that people are adaptive in their decision making for a variety of tasks, such as choosing anapartment (Sundstrm 1987 ) or loan candidates (Biggs et al.1985 ). The following task and context factors have beenfound to determine people s selection of decision strategy:the number of alternatives in the choice set (e.g., Johnsonand Meyer 1984 ), the number of attributes (e.g., Sundstrm1987 ), time pressure (e.g., Payne et al. 1996 ), and the similar-ity of alternatives (e.g., Bettman et al. 1993 ). Studies investi-gating the relationships between decision strategies, cognitiveeffort, and decision accuracy have shown that (1) under cer-tain circumstances (e.g., time constraints), heuristic decisionstrategies that involve highly selective information processing(e.g., the elimination-by-aspects model) can be as accurate asmore deliberate decision strategies (e.g., Johnson and Payne1985 ; Payne et al. 1988 , 1996 ; Rieskamp and Hoffrage 2008 ),and (2) people will shift decision strategies in an adaptive wayto achieve reasonable performance in the two meta-goals of decision accuracy and cognitive effort (e.g., Bettman et al.1998 ; Creyer et al. 1990 ; Payne and Bettman 2004 ).

Fast and frugal heuristics

Another framework building on the concept of boundedrationality is the research program of fast and frugal

DIMENSION 1

Amount of information searched

Constant Variable

I n t e r -

d i m e n s i o n a l

(1) Linearmodel

(2) Conjunctivemodel

D I M E N S I O N 2

S e q u e n c e o f

i n f o r m a t

i o n s e a r c h e d

I n t r a -

d i m e n s i o n a l

(3) Additivedifference model

(4) Elimination-by-aspects model

Fig. 1 Two-dimensional classification of decision strategies


8/10/2019 Bauer 2013

5/20

heuristics (Gigerenzer and Goldstein 1996 ). In general, a heuristic is a simple decision rule. However, not every ruleis a heuristic. Contrary to heuristics, rules can also be complex, such as computational algori thms. According toGigerenzer ( 2004 ), heuristics have three specific qualities:(1) Heuristics exploit evolved or learned capacities and aresimple in relation to these capacities. (2) Heuristics exploit structures of the environment. A heuristic is not good or bad per se; it is only good or bad in a particular environment. (3)Heuristics are distinct from as-if optimization models. Thelatter do not give information about the actual process, while a heuristic is a problem-solving rule whose purpose is to de-scribe both process and outcome. These qualities are incorpo-rated in the concept of fast and frugal heuristics, which aredefined as employing a minimum of time, knowledge, andcomputation to make adaptive choices in real environments

(Gigerenzer et al. 1999 , p. 14). Fast and frugal decisionmaking deliberately ignores information through its embraceof stopping rules (Gigerenzer et al. 1999 ).

The idea that people make judgments and decisions in a fast and frugal way has become increasingly popular in recent years in part because the underlying heuristics are both elegant and straightforward (Oppenheimer 2003 ). Fast and frugalheuristics can be used in a vast variety of situations, rangingfrom classifying incoming heart attack patients (Breiman et al.1993 ) to assessing the authenticity of antique statues (Hoving1996 ). When making a decision, people are assumed to select a decision strategy from a repertoire of fast and frugal heu-ristics, the adaptive toolbox (Gigerenzer and Selten 2001 ).Past research has identified several of these relatively simpleheuristics and investigated their use and accuracy (for a review,see Gigerenzer and Gaissmaier 2011 ). However, the evidenceis mixed. On the one hand, there is evidence that these simpleheuristics work even better than more deliberate decision strat-egies despite requiring less information and computation (e.g.,Brder 2000 , 2003 ; Brder and Schiffer 2003 ; Dhami andAyton 2001 ; Gigerenzer and Goldstein 1996 ; Gigerenzer et al. 1999 ). On the other hand, other research questions the fast and frugal approach by showing that most people s behavior isinconsistent with its theoretical assumptions (e.g., Newell et al.2003 ; Newell and Shanks 2003 ) and that the decision accuracyof these simplifying heuristics is worse than predictions made by chance (e.g., Oppenheimer 2003 ). By investigating therelationship between people s usage of fast and frugal heuristicsand their decision accuracy, our research will add to our under-standing of heuristic decision making.

Method

To empirically test the adaptive decision making and the fast and frugal heuristics frameworks in the context of customer management, we needed a sample of practicing managers anda

method that allows us to capture their decision making pro-cesses when being faced with customer management decisions.We chose to run a MouselabWEB study with sales managersworking in the retail banking industry. This section providesdetails on the participants of our study, the data collection usingMouselabWEB, and the design of the experiment.

Participants

All participants were sales managers of the retail bankingdivision of a German bank. Hence, all sampled managerswere from one functional area, namely the retail bank s salesdepartment. In their daily work, sales managers in retail banking are primarily concerned with customer manage-ment activities, such as client care and advisory on a rangeof financial products. Thus, the participants of this studyroutinely make customer management decisions similar tothose used in the experiment. The bank itself has been a leading player in the German banking market for more than130 years. It is one of the larger European banks and offersall major banking services. In recent years, customer man-agement has become a critical success factor for the bank and the focus of top management. Given this, the senior management of the bank was interested and supportive of our research and allowed us to select participants from the bank s entire pool of sales managers. In order to limit external factors (different focus areas, regional differences,etc.), we decided to focus on the region that was most representative of the bank s overall population of sales man-agers and clients. From this region, 70 managers wererandomly selected from the firm s internal database andcontacted by the office of their regional sales director.They were told that participation in the study was voluntary but would help to improve the sales management process of the bank. Of 70 managers initially contacted, 49 mangersagreed to take the study (i.e., 70% participation rate).

Participants are nearly equally distributed among threedifferent age groups: 21 30 years (34.7%), 31 40 years(36.7%), and 41 50 years (28.6%). Twenty-eight partici- pa nt s we re fe ma le (5 7. 1% ); tw en ty -o ne we re ma le(42.9%). Participants varied in their years of work experi-ence: less than 1 year (4.1%), 1 to 3 years (4.1%), 4 to6 years (18.8%), 7 to 9 years (14.6%), 10 or more years(58.3%). While a majority of participants (58.3%) have 10or more years of work experience in the sales area, there isenough variation for us to examine the role of expertise inthe subsequent analyses. While confidentiality agreements preclude us from reporting actual numbers, senior manage-ment confirmed that our sample is representative of thefirm s overall population of sales managers. Specifically,there were no significant differences between participantsand non-participants regarding age, gender, and salesexperience.


8/10/2019 Bauer 2013

6/20

Data collection using process-tracing

Managers were directed via email to a self-programmedwebsite using the MouselabWEB technology. After a short explanation about the background of the study and their role, they were presented with three tasks, which appearedin the same order for all participants. Each task was first briefly described and then participants were shown a matrixsuch as the one in Fig. 2.

The matrix contained information about a set of custom-ers. All information was initially hidden behind the boxes.To reveal a piece of information, participants had to click onthe respective box. Once a box was opened, it remainedopen for the duration of that task. During the time managersworked on each of the three tasks, MouselabWEB recorded(1) the number of boxes participants opened to solve eachtask (i.e., the amount of information accessed per task), (2)the order in which the boxes were opened (i.e., the sequenceof accessed information), (3) the time participants spent oneach box they opened, (4) the time participants took tocategorize each customer, (5) the total time to completethe task, and (6) the chosen options. Thus, MouselabWEBtraced managers information search and problem solvingstrategies without interfering with the decision making processes.

After completing each task, managers responded to sev-eral questions on five-point semantic differential scalesabout how realistic (task authenticity: 1 0 not realistic at all/ 5 0 very realistic) and difficult they perceived the task (self-reported task difficulty: 1 0 very easy/5 0 very difficult).Managers next indicated how often they make decisionssimilar to those in this study (task familiarity: 1 0 daily; 2 0

weekly; 3 0 monthly; 4 0 yearly; 5 0 less than once per year).Then managers estimated the percentage of customers that they classified correctly (estimated hit rate in %). After having completed all three tasks, managers reported their

age (coded 1 0 21 30 years; 2 0 31 40 years; 3 0 41 50 years;4 0 51 years and above), gender (coded 0 0 female; 1 0 male),and sales experience (coded 1 0 less than 1 year; 2 0 one tothree years; 3 0 four to six years; 4 0 seven to nine years; 5 0

10 or more years).

Manipulation

In order to investigate context-related effects on managers

decision making processes, we manipulated task complexi-ty. Managers were randomly assigned to either a low ( n 0 25)or high complexity ( n 0 24) condition. In the low (high)complexity condition, managers were shown seven piecesof information for 10 customers (10 pieces of informationfor 16 customers) per task. The number of metrics andcustomers were chosen based on discussions with the bank smanagement to reflect realistic tasks at different levels of complexity. Furthermore, our experimental manipulation isin line with Miller s (1956 ) findings which show that seven pieces of information are a natural limit for the workingmemory capacity in human information processing.Managers in both the low and high complexity conditionssaw the same seven customer metrics. These metrics wereselected by the bank as ones with high predictive quality.The three additional customer metrics shown only tothose managers in the high complexity group were oneswith low predictive quality. Therefore, the low and highcomplexity group differed only in the number of cus-tomer metrics provided, not in the overall predictivequality of the available information. Based on this in-formation, participants in the low (high) complexitycondition had to select those five (eight) customers theyexpected to display a certain behavior within a specifiedtime frame. In our analyses, we account for the task complexity manipulation with a dummy variable (coded0 0 low complexity; 1 0 high complexity).

Fig. 2 MouselabWEB layout of the cross-selling task (low complexity condition)


8/10/2019 Bauer 2013

7/20

Customer sample

The bank s retail customers are all in the consumer market.In the experiment, managers had to make decisionsconcern ing the bank s r ea l cus tomers , and the i r corresponding customer information was retrieved fromthe bank s internal database. For each task, a pool of cus-tomers was selected using the following procedure: In thefirst step, a small subset of customers was randomly selectedfrom the retail bank s overall population of customers. The bank double-checked the selection and confirmed that thesecustomers were representative of the overall consumer client base. In the second step, 10 (or 16) customers were chosenfrom the random draw in the first step; however, selectedcustomers had to fulfill the following two requirements: (1)they had to show variation on the two customer metrics withthe highest predictive quality for the respective task, and (2)for each task, half of the customers (five in the low complexity condition and eight in the high complexity condi-tion) had to display the behavior in question.

Note that since managers were told beforehand to select either five (low complexity condition) or eight customers(high complexity condition), the pattern in the data is some-what stronger than in reality. While this might increasemanagers overall predictive accuracy, it should neither affect the use of adaptive decision making or fast and frugalheuristics nor should it affect task- and manager-relatedinfluences. A purely random draw of customers would not have been appropriate for our experimental design as it most likely would not show any meaningful pattern (e.g., cus-tomers might not differ on the most important metrics) andthus would have made it impossible for managers to reach a reasonable decision. We discuss the limitations associatedwith this procedure at the close of the article.

Tasks

The tasks used in the experiment were selected based onseveral criteria. They should (1) increase in difficulty, so that the first task is clearly the easiest and the third task is clearlythe most difficult, (2) deal with customer management deci-sions that sales managers in retail banking routinely face andare familiar with, (3) be solvable with information well-known to managers and available in the bank s internaldatabase, and (4) be as realistic as possible.

To ensure that these four requirements were met, a small pretest was conducted. Participants were seven managersfrom sales, marketing, and senior management. Based ontheir feedback, several changes were made to the predictiontasks. After this refinement, all seven managers agreed that the tasks met the four criteria. Thus, our pretest confirmedthat the three tasks were appropriate for our research goal of capturing real managerial decision making.

The specific information shown for each task was basedon the predictive quality of the respective customer metrics.For each task, the predictive quality of the incorporatedcustomer metrics was assessed by the bank using regressionanalyses. The seven customer metrics shown to both the lowand high complexity group were high in predictive qualityand explained a significant proportion of variance in therespective regression analysis. The three customer metricsshown only to the high complexity group were low in predictive quality and did not further contribute to the var-iance explained. Therefore, rather than arbitrarily selectingcustomer metrics, our procedure ensured (1) that we provid-ed the appropriate customer information for each task and(2) that the low and high complexity group only differed inthe amount of information and not in its predictive quality.

The first task the cross-selling task concerned obtain-ing a new consumer loan. The available metrics for eachcustomer for both complexity groups were the customers

number of fully repaid loans, number of current loans, grossmargins for the last four quarters, current usage of a con-sumer loan, average account balance, number of products,and average overdraft in the last month. The high complex-ity group additionally saw information on the customers

age, the number of children they have, and the number of credit cards they used in the last three months. Based onthese metrics managers had to predict which five (lowcomplexity condition) or eight (high complexity condition)customers actually obtained a new consumer loan in thenext three months. Figure 2 illustrates the actual layout of the task in MouselabWEB.

The second task the retention task concerned predict-ing which customers cancelled a checking account. All participants saw the customers number of checking account transactions per month, whether they had an overdraft facil-ity, the number of saving accounts they had with a cancel-lation period, the number of negative credit reports theyreceived, the sum of their positive account balance, thesum of their negative account balance, and their total number of products. Participants in the high complexity condi-tion were also provided with information about thecustomers age, whether they had a provision for deprecia-tion, and whether they had bought a featured product.Managers had to predict which five (low complexity condi-tion) or eight (high complexity condition) customers actual-ly cancelled their checking account within the next six months.

The third task the acquisition task concernedwhether or not the customer referred a new customer tothe bank. Participants in both conditions saw the custom-ers age, their household size, their account volume, their types of checking accounts, their total number of prod-ucts, whether they had bought a featured product, andthe number of months since their last transaction. Those


8/10/2019 Bauer 2013

8/20

in the high complexity condition also could view thenumber of children the customers have, their number of checking account transactions per month, and their number of remittances. Based on these metrics managers hadto predict which five (low complexity condition) or eight (high complexity condition) customers actually referred a new customer through the referral program of the bank within the next 18 months.

In order to investigate how task difficulty affects the useof adaptive decision making, fast and frugal heuristics, andmanagers predictive accuracy, we coded the tasks based ontheir objective task difficulty with an ordinal variable asfollows: 1 0 easy (cross-selling task), 2 0 difficult (retentiontask), 3 0 very difficult (acquisition task).

Measures

Usage of adaptive decision making is measured using a dummy variable to indicate if there was any change indecision strategy by the decision maker across the threetasks (coded 1 0 two or more different decision strategieswere used for the three tasks; 0 0 the same decision strategywas used for all three tasks). A change in decision strategyoccurred if participating managers switched between thelinear, conjunctive, additive difference, or elimination-by-aspects model (Payne 1976 ). Note that a change in decisionstrategy can happen for a number of reasons (e.g., learning,different information needs) but that the focus of our studyis to investigate if a change in decision strategy happened.Usage of adaptive decision making is measured as a dummyvariable which is constant for each manager over all threetasks and serves as an independent variable in a generalizedestimating equation (GEE) model investigating managers

predictive accuracy.Strategy changed is a dummy variable which captures

any change in decision strategy between the first and thesecond, and between the second and the third task (coded1 0 manager changed her/his decision strategy from the pre-vious task; 0 0 there was no change in strategy from the previous task). Hence, strategy changed produces only val-ues for the second (retention) and the third (acquisition) task because no change in decision strategy can occur for the first task, which serves as reference point for the transition to thesecond task. Strategy changed is the dependent variable in a GEE (binary logistic) model investigating the task- andmanager-related factors which drive adaptive decisionmaking.

Usage of fast and frugal heuristics is measured using a dummy variable (coded 1 0 fast and frugal heuristic wasused; 0 0 otherwise). A fast and frugal heuristic was assumedto be used if managers predictions in a specific task were(1) based on 20% or less of the available information (i.e.,14 cells in the low and 32 cells in the high complexity

group) and (2) made in 150 seconds or less for the low or 300 seconds or less for the high complexity group. 1 Thus,for each task there is one observation indicating whether or not a specific manager solved the decision making problemusing a fast and frugal heuristic. We cannot rule out the possibility that participants who were coded as not using a fast and frugal heuristic actually did so since we do not know if they based their decision on all the information theylooked at (Rieskamp and Hoffrage 1999 ). Specifically, man-agers who opened more than 14 (32) boxes in the low (high)complexity condition might not have used all of theseaccessed pieces of information for their predictions. Thus,they may still have used a fast and frugal heuristic without being detected by MouselabWEB and classified as fast andfrugal according to our measure. However, if participantsreached a decision within a short amount of time and onlylooked at a limited amount of information, they must haveused a fast and frugal heuristic. Therefore, we believe that our measure is conservative because it reflects the minimumnumber of incidents where fast and frugal heuristics wereused. Usage of fast and frugal heuristics serves as thedependent variable in a GEE (binary logistic) model exam-ining the task- and manager-related factors which drive fast and frugal decision making. Usage of fast and frugal heu-ristics is also an independent variable in a GEE (linear)model investigating the impact of fast and frugal decisionmaking on managers predictive accuracy.

Predictive accuracy is measured as the actual hit rate of each manager for the respective task (one observation per task). The hit rate is defined as the proportion of correctlyclassified customers. Predictive accuracy is the dependent variable in a GEE (linear) model which identifies the maindeterminants for managers decision quality.

The difference between each manager s actual and esti-mated hit rate serves as a measure of each manager s over-and underconfidence, respectively. Whereas positive differ-ences indicate that managers display underconfidence in

1 Using a cutoff value of 20% was based on the fact that (1) a lower cutoff was not possible since that would have not yielded enough participants to draw meaningful conclusions (only four participantsused 10% of the information), (2) a higher cutoff does not seemconsistent with the frugal heuristics framework, and (3) the number

of participants using 20% or less of the information formed exactly the10th percentile of all participants. Using the cutoff value of 150 or 300 seconds was based on discussion with the bank s management. Inorder to provide stronger support for our findings, we conducted a sensitivity analysis which examined the robustness of our results withrespect to different definitions of usage of fast and frugal heuristics .Across different definitions of usage of fast and frugal heuristics basedon a range from (1) 14% to 26% (in 2% intervals) for information use,(2) 120 to 180 seconds (in 10 second intervals) for decision time in thelow complexity condition, and (3) 270 to 330 seconds for decision timein the high complexity condition, all analyses reported in this articleyielded similar results. Thus, our findings are robust to changes in howwe operationalize usage of fast and frugal heuristics .


8/10/2019 Bauer 2013

9/20

their estimates, negative differences imply overconfidence.By using a metric variable, we detect not only whether or not managers over- or underestimate their predictive accu-racy but also the degree of their over-/underconfidence bias.

Results

Manipulation check

To test if our manipulation of task complexity was success-ful, we examined whether participants in the low ( n 0 25)and high complexity ( n 0 24) condition differed in their easeof processing the provided customer information (for a review, see Alter and Oppenheimer 2009 ). We used manag-ers reaction times for classifying the first customer (inseconds) in each task as a measure of processing ease(Winkielman et al. 2006 ). Participants in the high complex-ity condition (10 pieces of information per customer in eachtask) required more time to make their first prediction thanthose in the low complexity condition (seven pieces of information per customer in each task). We used a Mann

Whitney test in order to examine whether or not managers

reaction times in all three tasks (three decisions per partic-ipant; n 0 147) were equally distributed across the two con-ditions of our experimental manipulation. The analysisrevealed a significant effect ( U 0 3326.00, z 0 2.43, p.10) complexity conditions. Thus, most managers first gathered an equal amount of information for all alternativesin the choice set before making their decisions.

In the second step, we determined whether managers hada tendency to search for information by customer (i.e., inter-dimensional or line-by-line with respect to Fig. 2) or bymetric (i.e., intra-dimensional or column-by-column withrespect to Fig. 2). Based on these sequences recorded byMouselabWEB and the corresponding Payne-index (a mea-sure of participants tendency to either search within or across alternatives; for computational details, see Payne1976 ), we found that 77.6% (22.4%) of the managers gath-ered information in an intra-dimensional (inter-dimensional)way. There were significant differences in managers infor-mation acquisition sequences between the low (intra-dimensional 0 66.7%, inter-dimensional 0 33.3%) and high(intra-dimensional 0 88.9%, inter-dimensional 0 11.1%; 2 0

10.420, df 0 1, p

8/10/2019 Bauer 2013

10/20

information in a decision making problem seems to makemanagers more likely to engage in full information process-ing. In contrast, providing more information in a decisionmaking problem seems to foster the use of intra-dimensionaldecision strategies. Specifically, the additive difference (highcomplexity 0 63.9% vs. low complexity 0 48.0%) andelimination-by-aspects (high complexity 0 25.0% vs. lowcomplexity 0 18.7%) models have higher choice shares in thehigh complexity condition. Managers overall preference for the additive difference (55.8%) and elimination-by-aspects(21.8%) models may be because both decision strategiesrequire relatively little information to make accurate decisions.As confirmed by a one-way ANOVA, the overall hit rates of the four decision strategies did not significantly differ (linear 0

92.0% vs. conjunctive 0 91.5% vs. additive difference 0 84.3%vs. elimination-by-aspects 0 85.1%; F(3, 146) 0 1.38, p>.10),even though the corresponding amount of accessed informa-tion ranged from 44.7% to 100.0% (F(3, 146) 0 27.81, p.10).

Table 2 provides a similar descriptive overview of manag-ers decision strategies, accessed information, and decisionaccuracy by task. 2 A chi-square test provides evidence that managers use different decision strategies which depend onthe task to be solved ( 2 0 19.05, df 0 6, p

8/10/2019 Bauer 2013

11/20

the significance of model effects using a Wald Chi 2 test. In thenext step, we reduced the model by removing all insignificant predictors (e.g., West et al. 2007 ). The final model was thenselected based on the quasi-likelihood (QIC) and the correctedquasi-likelihood under the independence model criterion(QICC); both represent the standard criteria for modelselection in the GEE framework (Pan 2001 ). As mod-ifications of Akaike s Information Criterion, QIC andQICC also penalize the number of parameters in the

model in order to account for the opposing needs of parsimony and model fit. Table 3 summarizes the resultsof the model effects for the full and the reduced GEE(binary logistic) models.

By directly comparing the results of the two models inTable 3, we can see that objective task difficulty is the main predictor of adaptive decision making(full model: Wald Chi 2 0

5.495, p

8/10/2019 Bauer 2013

12/20

model (Wald Chi 2 0 2.821, p.10). Allother model effects (including the demographic control variables age and gender) were insignificant in the full model andwere not included in the reduced model. Based on the lower QIC and QICC values, we indentified the reduced modelreported in Table 3 as the final model. Therefore, we interpret significant parameters and their odds ratios on the basis of theresults derived from the reduced model (cf. Table 4).

Based on the results of the reduced model, we can concludethat the more difficult the task, the less likely managers wereto change their decision strategies. Specifically, as the oddsratio (Exp b) of objective task difficulty suggests, managers

likelihood to switch decision strategy decreases on average by68% ( 0 .320 1.000) as tasks become more difficult. This result is consistent with previous descriptive findings (cf. Table 2),which showed that percentage changes in decision strategies between the first (cross-selling) and the second (retention) task were considerably higher than between the second (retention)and third (acquisition) task.

Task- and manager-related drivers of fast and frugal heuristics

We observed 18 instances of fast and frugal heuristics beingused (only 12% of all observations). Interestingly, of the 18times fast and frugal heuristics were used, 17 times were bymanagers who had at least 7 years of sales experience. Intotal, 11 managers used fast and frugal heuristics; seven of them twice and four of them once.

To see which task- and manager-related characteristicsimpact the use of fast and frugal heuristics, we estimatedanother GEE (binary logistic) model with usage of fast and frugal heuristics as the dependent variable. Again, the GEEapproach was preferred over a binary logistic regression

because it can account for the repeated measurements whichmanagers made across the three tasks. We included the previ-ously used task- and manager-related factors as independent variables and followed the same top-down strategy for modelselection. Table 5 summarizes the results of the model effectsfor the full and the reduced GEE (binary logistic) models.

As Table 5 indicates, both models identified self-reportedtask difficulty (full model: Wald Chi 2 0 18.336, p

8/10/2019 Bauer 2013

13/20

Determinants of managers predictive accuracy

We estimated a GEE (linear) model with predictive accuracy,measured by managers hit rate in each task, as the dependent variable. We again accounted for repeated observations with a working correlation matrix and included task- and manager-related characteristics as independent variables. Additionally,usage of adaptive decision making and usage of fast and frugalheuristics served as independent variables in order to investi-gate their influence on managers decision quality. Table 7summarizes the results of the model effects for the full and thereduced GEE (linear) models.

Both models consistently revealed significant effects for objective task difficulty (full model: Wald Chi 2 0 10.691, p

8/10/2019 Bauer 2013

14/20

task difficulty has the expected negative association with predictive accuracy. Thus, the more difficult managers per-ceived the tasks to be, the less accurate were their predictions.Another interesting finding is that the difference between theactual and estimated hit rate not only has a positive impact on predictive accuracy but also its effect size is the largest of allvariables. Hence, managers who underestimated the quality of their decisions were the most accurate. Also, the more prudent managers were in their estimated hit rate (i.e., the larger theunderconfidence bias was), the more accurate they were intheir predictions. Whereas usage of adaptive decision makingwas insignificant in this analysis, usage of fast and frugalheuristics was significant and associated with an increase in

predictive accuracy. All other things being equal, making fast decisions and perhaps ignoring seemingly irrelevant pieces of information increased managers predictive accuracy by anaverage of 9.14 percentage points. Based on the GEE results,on average, managers have a predictive accuracy of 84.5% if they did not use a fast and frugal heuristic and have a predic-tive accuracy of 93.7% if they did.

The role of low and high predictive quality metrics in fast and frugal decision making

In order to examine why usage of fast and frugal heuristicswas associated with increased decision accuracy, we further

Table 6 Task- and manager-related drivers of fast and frugal heuristics (parameter estimates)

Dependent variable: usage of fast and frugal heuristics

Full model Reduced model

Parameter b (s.e.) Wald Chi2

Exp b b (s.e.) Wald Chi2

Exp b

Intercept 4.048** (1.998) 4.103 .017 3.014** (1.536) 3.851 .049Acquisition task 1.567* (.816) 3.686 4.793

Retention task 1.024 (.816) 1.576 2.784Cross-selling task (reference category) 0 1

Self-reported task difficulty 1.330*** (.311) 18.336 .265 .904** (.362) 6.256 .405High complexity 1.223 (.848) 2.076 3.396

Low complexity (reference category) 0 1

Age .595 (.393) 2.294 .552Male .854 (.708) 1.457 2.350

Female (reference category) 0 1

Sales experience .898** (.434) 4.287 2.454 .681** (.326) 4.359 1.977

Difference between actual and estimated hit rate .011 (.018) .388 1.011

Standard errors (s.e.) in parentheses; significance levels: *** p .01, ** p .05, * p .10

Table 7 Determinants of managers predictive accuracy (test of model effects)

Dependent variable: predictive accuracy Full model Reduced model

Test of model effects Wald Chi2

df p-value Wald Chi2

df p-value

Intercept 166.129*** 1 .000 446.291*** 1 .000

Objective task difficulty 10.691*** 2 .005 11.706*** 2 .003

Self-reported task difficulty 5.453** 1 .020 5.536** 1 .019

Complexity 2.137 1 .144

Age .139 1 .709

Gender .731 1 .392Sales experience .247 1 .619

Difference between actual and estimated hit rate 53.385*** 1 .000 54.863*** 1 .000

Usage of adaptive decision making .518 1 .472

Usage of fast and frugal heuristics 12.965*** 1 .000 10.225*** 1 .001

Model summary ( n 0 140): Model summary ( n 0 143):

QIC 0 21015.404 QIC 0 22021.039

QICC 0 21008.606 QICC 0 22018.806

Standard errors (s.e.) in parentheses; significance levels: *** p .01, ** p .05, * p .10


8/10/2019 Bauer 2013

15/20

explored which pieces of customer information managersaccessed in the high complexity condition. It is possible that managers who use a fast and frugal heuristic are moreaccurate because they focus more on the high predictivequality metrics and less on the low predictive quality met-rics. To test this, we examined what percent of the totalmetrics each manager looked at for each task was low versushigh predictive quality. A one-way ANOVA analysis withthe percentage of low predictive quality metrics as thedependent variable and usage of fast and frugal heuristicsas the independent variable revealed that managers whoused a fast and frugal heuristic accessed a smaller percent-age of low predictive quality metrics (M Usage of fast and frugalheuristics

0 4.2%) than managers who used other decisionstrategies (M Otherwise 0 22.2%; F(1, 70) 0 15.27, p

8/10/2019 Bauer 2013

16/20

the use of fast and frugal heuristics was largely driven bymanagers experience. Thus, our findings support Gigerenzer s assumption that people learn to choose theappropriate heuristic from their adaptive toolbox in order to make fast, frugal, and accurate decisions (e.g., Gigerenzer and Gaissmaier 2011 ).

In the classic view, the use of heuristics is associated with biases in judgment and decision making (e.g., Tversky andKahneman 1974 ). Therefore, many scholars consider heu-ristics as inaccurate decision rules which are best avoided(e.g., Hutchinson et al. 2010 ). Contrary to this, our resultsshow that (1) adaptive decision making does not have a negative impact on decision quality, and (2) fast and frugalheuristics can lead to a significant increase in decisionaccuracy. In particular, we found that the four decisionstrategies under investigation performed equally well so that switching strategies did not impact predictive accuracy.Even more surprisingly, the use of fast and frugal heuristicswas associated with an increase in predictive accuracy of over nine percentage points. This may be because managersfocus more on the important and less on the unimportant pieces of information. This result is in line with the theoret-ical assumption that fast and frugal heuristics exploit evolved capacities (e.g., Gigerenzer 2004 ). In other words,experienced managers have learned over time to deliberatelyignore irrelevant pieces of information and to focus on therelevant metrics when making customer management deci-sions. Overall, our study showed that simple decision strat-egies performed well in reducing cognitive effort without jeopardizing decision quality. Thus, the existing skepticismconcerning heuristics should be questioned.

Managerial implications

Having investigated the factors that drive good decisions,the results of this study provide some practical implicationsabout how individual managers can improve their decisionmaking in order to make valuable customer acquisition,retention, and cross-selling decisions.

In particular, our findings suggest that being too confident can negatively affect decision quality and thus harm customer relationships. Identifying and successfully managing overcon-fidence seems promising for increasing the decision quality of a firm s managers. As suggested by Russo and Schoemaker (1992 ), timely and precise feedback can be an effective tool toreduce overconfidence. Counterargumentation can be another effective debiasing technique (Russo and Schoemaker 1992 ).Overconfidence is often the result of managers beingmotivated to support and successfully defend their initialopinion. In this process, contradicting evidence is large-ly neglected. Asking managers to think of reasons whytheir opinions or estimates might be wrong can reduceoverconfidence (Koriat et al. 1980 ). Thus, reminding

managers to account for both supporting and contradict-ing reasons in their decision making processes willincrease decision quality.

We showed not only that managers use fast and frugalheuristics but that they do so successfully. The beneficialimpact of fast and frugal heuristics may be because theyreduce information overload. The latter is a common phe-nomenon in today s managerial decision making (Reuters1996 ). By focusing only on important decisions inputs,usage of fast and frugal heuristics avoids (1) the overweight-ing of irrelevant pieces of information and (2) data-rich but poor decisions made on the basis of an unmanageableamount of information. Therefore, rather than just accumu-lating data, managers should be encouraged to trust their intuition about which information is important when makingdecisions.

While we focused on customer management decisions,our results have implications more generally for managerialdecision making. Consider a senior manager of a corporategroup who has to make an investment decision about wheth-er to acquire a company in order diversify the group s business portfolio. Such complex investment decisions usu-ally demand the processing of a substantial amount of information from financial statements (e.g., informationabout financial ratios, earnings, expenses, cash flows), an-nual reports (e.g., information about the diversification strat-egy, product portfolio), and a range of other sources (e.g.,rating reports) in order to judge the risk and return of theinvestment. However, there is also some evidence whichsuggests that fast and frugal heuristics can even improvedecision quality for such complex investment decisions. Inan interview that we conducted, a very successful senior manager of a leading financial institution explained that heonly uses four criteria to judge the health and prospects of a company before making the investment decision: (1) market share gain to see if the company is growing faster than thecompetition, (2) revenue growth to assess the company stop-line health, (3) cost growth to see if costs are growingfaster than revenues, and (4) flexibility of the cost base to judge whether the business is able to quickly adapt tochanging circumstances (e.g., sudden drop in demand).Thus, rather than processing all available financial informa-tion, the manager implicitly accounts for risk and returnconsiderations by assessing more global decision criteria.This anecdote is in line with research on venture capitaldecision making. For example, Hall and Hofer ( 1993 ) findthat venture capitalists make go/no-go investment decisionsin an average of less than 6 minutes on initial proposalscreening and less than 21 minutes on business proposalassessment. They also conclude that finances are implicitlyassessed by venture capitalists judgments about the natureof the proposed business, its strategy, and its economicenvironment (e.g., the industry s risk and rate of return).


8/10/2019 Bauer 2013

17/20

Despite relying on relatively few pieces of information, thesuccess rate of venture capital-backed businesses is signifi-cantly higher than the success rate of other new ventures(Hall and Hofer 1993 ).

Limitations and directions for further research

Even though our research contributes to the understandingof managerial decision making, we want to point out some potential limitations of our study. First, all three tasks in our experiment were predictive in nature. That is, managers inthe low (high) complexity condition knew beforehand that they had to predict a certain behavior for five (eight) cus-tomers in each task. Although this study design allowed usto assess managers decision accuracy, it is associated withless uncertainty in decision making compared to (1) a task where the number of objects to be classified is unknown or (2) a task that does not have a direct predictive dimension(e.g., investment decisions). Whereas this reduction in un-certainty might have positively affected managers hit rates,we do not think it should have influenced the way in whichmanagers searched for information (e.g., the number andsequence of accessed information, decision time). Thus, wefeel confident that the conclusions we drew for the use of adaptive decision making and fast and frugal heuristicsshould also hold for other types of decisions. Also, we think that the determinants of managers decision accuracy should be unaffected by a possible overestimation of hit rates whichwould only be reflected in a higher value for the intercept of the GEE (linear) model. Therefore, the tasks we usedaccounted not only for a normative evaluation criterion of managers decision quality but also for a predictive compo-nent which is involved in most managerial decisions.Consider the following example: if a retailer wants to decidewhether or not to enter into a new market, this investment decision will be driven to a large extent by managers

predictions about the anticipated sales volume, expansioncosts, operating costs, etc. As the example illustrates, most managerial decisions are either directly or indirectly charac-terized by making predictions. Nevertheless, since it remains possible that the nature of the tasks we used influ-enced the outcome, future research should examine whether the nature of the task affects managerial decision making byutilizing a more diverse set of tasks, including ones that donot involve an explicit prediction.

Because we held the order of the tasks constant, we didnot directly test or control for learning. Since we invited realmanagers to participate in our research, we did not have theopportunity to administer several experimental sessionswhere we could alter the order of the tasks. Therefore, futureresearch should test whether the usage of adaptive decisionmaking and fast and frugal heuristics increases withlearning.

Although we were fortunate to have access to a sample of real managers, we would like to note that all participantswere from the same functional area (i.e., sales department)of one bank. Thus, rather than drawing general conclusionsabout the entire population of managers, our findings should be considered as initial evidence for managers use of adap-tive decision making and fast and frugal heuristics. Futureresearch should therefore investigate the two frameworks indifferent contexts (e.g., different industries, different func-tional areas) in order to further enhance our understandingof managerial decision making.

Finally, as we noted, it is possible that managers whotook more time and opened more information boxes alsoused fast and frugal heuristics. Future research could ma-nipulate the use of specific fast and frugal heuristics, rather than infer their use, to help validate whether use of fast andfrugal heuristics can increase decision accuracy.

In addition to the extensions implied by these limitations,there are several other directions for further research that wewould like to highlight. Using MouselabWEB, future studiescould investigate howdemographic (e.g., age, gender, education-al background) andpersonality-related differences between man-agers (e.g., sensing-thinking vs. intuition-thinking vs. sensing-feeling vs. intuition feeling; Myers and McCaulley 1985) affect their choice of decision strategy and decision accuracy. Another interesting avenue for future research would be to manipulate participants level of confidence and to employ MouselabWEBfor identifying differences in the decision making processes andaccuracy of under- and overconfident managers.

Acknowledgments The authors thank Walter Herzog, Jan R.

Landwehr, Bernd Skiera, and the four anonymous reviewers for their helpful comments.

References

Alter, A. L., & Oppenheimer, D. M. (2009). Uniting the tribes of fluency to form a metacognitive nation. Personality and Social Psychology Review, 13 (3), 219 235.

Anderson, P. A. (1983). Decision making by objection and the Cubanmissile crisis. Administrative Science Quarterly, 28 (2), 201 222.

Ballinger, G. A. (2004). Using generalized estimating equations for

longitudinal data analysis. Organizational Research Methods, 7 (2), 127 150.Bazerman, M. H. (1998). Judgment in managerial decision making .

New York: Wiley.Berger, P. D., & Nasr, N. I. (1998). Customer lifetime value: marketing

models and applications. Journal of Interactive Marketing, 12 (1),17 30.

Berger, P. D., & Nasr Bechwati, N. I. (2001). The allocation of promotion budget to maximize customer equity. Omega, 29 (1),49 61.

Bettman, J. R., Johnson, E. J., Luce, M. F., & Payne, J. W. (1993).Correlation, conflict, and choice. Journal of Experimental Psychology: Learning, Memory, and Cognition, 19 (4), 931 951.


8/10/2019 Bauer 2013

18/20

8/10/2019 Bauer 2013

19/20

Miller, G. A. (1956). The magical number seven, plus or minus two:some limits on our capacity for processing information. Psychological Review, 63 (2), 81 97.

Mintzberg, H. (1973). The nature of managerial work . New York:Harper and Row.

Moore, D. A., & Cain, D. M. (2007). Overconfidence and underconfi-dence: when and why people underestimate (and overestimate)the competition. Organizational Behavior and Human Decision Processes, 103 (2), 197 213.

Morwitz, V. G., & Schmittlein, D. C. (1998). Testing new direct marketing offerings: the interplay of management judgment andstatistical models. Management Science, 44 (5), 610 628.

MouselabWEB (2012). Monitoring information acquisition processeson the Web, (accessed August 31, 2012), [available at http:// www.mouselabweb.org/ ].

Myers, I. B., & McCaulley, M. H. (1985). Manual: a guide to thedevelopment and use of the Myers-Briggs type indicator . PaloAlto: Consulting Psychologists Press.

Neale, M. A., & Bazerman, M. H. (1985). The effects of framing andnegotiator overconfidence on bargaining behaviors and outcomes. Academy of Management Journal, 28 (1), 34 49.

Newell, B. R., & Shanks, D. R. (2003). Take the best or look at therest? Factors influencing one-reason decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition,29(1), 53 65.

Newell, B. R., Weston, N. J., & Shanks, D. R. (2003). Empirical testsof the fast-and-frugal heuristic: not everyone takes-the-best .Organizational Behavior and Human Decision Processes, 91 (1),82 96.

Nut t, P. C. (198 4). Types of organizational decision processes. Administrative Science Quarterly, 29 (3), 414 450.

Oppenheimer, D. M. (2003). Not so fast! (and not so frugal!): rethink-ing the recognition heuristic. Cognition, 90 (1), B1 B9.

Pan, W. (2001). Akaike s information criterion in generalized estimat-ing equations. Biometrics, 57 (1), 120 125.

Payne, J. W. (1976). Task complexity and contingent processing indecision making: an information search and protocol analysis.Organizational Behavior and Human Performance, 16 (2), 366

387.Payne, J. W., & Bettman, J. R. (2004). Walking with the scarecrow: the

information-processing approach to decision research. In D. J.Koehler & N. Harvey (Eds.), Blackwell handbook of judgment and decision making (pp. 110 132). Oxford: Blackwell.

Payne, J. W., Bettman, J. R., & Johnson, E. J. (1988). Adaptivestrategy selection in decision making. Journal of Experimental Psychology: Learning, Memory, and Cognition, 14 (3), 534 552.

Payne, J. W., Bettman, J. R., & Johnson, E. J. (1993). The adaptivedecision maker . New York: Cambridge University Press.

Payne, J. W., Bettman, J. R., & Luce, M. F. (1996). When time ismoney: decision behavior under opportunity-cost time pressure.Organizational Behavior and Human Decision Processes, 66 (2),131 152.

Reinartz, W. J., & Kumar, V. (2000). On the profitability of long-lifecustomers in a noncontractual setting: an empirical investigation andimplications for marketing. Journal of Marketing, 64 (4), 17 35.

Reinartz, W. J., & Kumar, V. (2003). The impact of customer relation-ship characteristics on profitable lifetime duration. Journal of Marketing, 67 (1), 77 99.

Reinartz, W. J., & Venkatesan, R. (2008). Models for customer rela-tionship management (CRM). In B. Wierenga (Ed.), Handbook of marketing decision models (pp. 291 326). New York: Springer Science + Business Media.

Reinartz, W. J., Thomas, J. S., & Kumar, V. (2005). Balancing acqui-sition and retention resources to maximize customer profitability. Journal of Marketing, 69 (1), 63 79.

Reuters (1996). Dying for information? An investigation into theeffects of information overload in the UK and worldwide .London: Reuters Business Information.

Rieskamp, J., & Hoffrage, U. (1999). When do people use simpleheuristics, and how can we tell. In G. Gigerenzer, P. M.Todd, & the ABC Research Group (Eds.), Simple heuristicsthat make us smart (pp. 141 167). New York: OxfordUniversity Press.

Rieskamp, J., & Hoffrage, U. (2008). Inferences under time pressure:how opportuni ty costs affect s t ra tegy select ion. Acta Psychologica, 127 (2), 258 276.

Ronchetto, J. R., Hutt, M. D., & Reingen, P. H. (1989). Embeddedinfluence pattern in organizational buying systems. Journal of Marketing, 53 (4), 51 62.

Ruekert, R. W., & Walker, O. C., Jr. (1987). Marketing s interactionwith other functional units: a conceptual framework and empiricalevidence. Journal of Marketing, 51 (1), 1 19.

Russo, J. E., & Schoemaker, P. J. H. (1992). Managing overconfidence.Sloan Management Review, 33 (2), 7 18.

Rust, R. T., Lemon, K. N., & Zeithaml, V. A. (2004). Return onmarketing: using customer equity to focus marketing strategy. Journal of Marketing, 68 (1), 109 127.

Rust, R. T., Moorman, C., & Bhalla, G. (2010). Rethinking marketing. Harvard Business Review, 88 (1/2), 94 101.

Schmittlein, D. C., & Peterson, R. A. (1994). Customer base analysis:an industrial purchase process application. Marketing Science, 13(1), 41 67.

Schmittlein, D. C., Morrison, D. G., & Colombo, R. (1987). Countingyour customers: who are they and what will they do next? Management Science, 33 (1), 1 24.

Schwenk, C. R. (1986). Information, cognitive biases, and commit-ment to a course of action. Academy of Management Review, 11(2), 298 310.

Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69 (1), 99 118.

Simon, H. A. (1981). The sciences of the artificial (2nd ed.).Cambridge: MIT Press.

Simon, H. A. (1990). Invariants of human behavior. Annual Review of Psychology, 41 (February), 1 19.

Simon, H. A. (1997). Administrative behavior: a study of decision-making processes in administrative organizations (4th ed.). NewYork: Free Press.

Simon, M., & Houghton, S. M. (2003). The relationship betweenoverconfidence and the introduction of risky products: evidencefrom a field study. Academy of Management Journal, 46 (2), 139

149.Sundstrm, G. A. (1987). Information search and decision making: the

effects of information displays. Acta Psychologica, 65 (2), 165 179.Tversky, A. (1969). Intransitivity of preferences. Psychological

Review, 76 (1), 31 48.Tversky, A. (1972). Elimination by aspects: a theory of choice.

Psychological Review, 79 (4), 281 299.Tversky, A., & Kahneman, D. (1974). Judgments under uncertainty:

heuristics and biases. Science, 185 (4157), 1124 1131.Van Bruggen, G. H., & Wierenga, B. (2010). Marketing decision

making and decision support: challenges and perspectives for successful marketing management support systems. Foundationsand Trends in Marketing, 4 (4), 209 331.

Verbeke, G., & Molenberghs, G. (2000). Linear mixed models for longitudinal data (2nd ed.). New York: Springer.

Verhoef, P. C., Reinartz, W. J., & Krafft, M. (2010). Customer engage-ment as a new perspective in customer management. Journal of Service Research, 13 (3), 247 252.

Wagner, R. K. (1987). Tacit knowledge in everyday intelligent behavior. Journal of Personality and Social Psychology, 52 (6), 1236 1247.

http://www.mouselabweb.org/http://www.mouselabweb.org/http://www.mouselabweb.org/http://www.mouselabweb.org/

8/10/2019 Bauer 2013

20/20

Wally, S., & Baum, J. R. (1994). Personal and structural determinantsof the pace of strategic decision making. Academy of Management Journal, 37 (4), 932 956.

Walsh, J. P. (1995). Managerial and organizational cognition: notes froma trip down memory lane. Organizational Science, 6 (3), 280 321.

West, B. T., Welch, K. B., & Galecki, A. T. (2007). Linear mixed models: a practical guide using statistical software . Boca Raton:Chapman & Hall/CRC.

Wierenga, B., Van Bruggen, G. H., & Staelin, R. (1999). The successof marketing management support systems. Marketing Science,18(3), 196 207.

Winkielman, P., Halberstadt, J., Fazendeiro, T., & Catty, S. (2006).Prototypes are attractive because they are easy on the mind. Psychological Science, 17 (9), 799 806.

Wbben, M., & Wangenheim, F. (2008). Instant customer base analy-sis - managerial heuristics often get it right. Journal of Marketing,72(3), 82 93.

Zeger, S. L., & Liang, K.-Y. (1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42 (1), 121 130.

Zorn, C. J. W. (2001). Generalized estimating equation models for correlated data: a review with applications. American Journal of Political Science, 45 (2), 470 490.


Documents

Bauer 2013