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Dynamic linkages betweenmental models, resource

constraints and differentialperformance

A resource-based analysis

Abhijit Mandal and Howard ThomasWarwick Business School, University of Warwick, Coventry, UK, and

Don Antunes IMD, Lausanne, Switzerland

Abstract

Purpose – The purpose of this paper is to focus around the literatures of the resource-based firm andcognitive mental models, explores the dynamic linkages between cognitive models, resources and firmperformance in the context of the insurance industry.

Design/methodology/approach – In a real-life example drawn from the insurance industry,a process-based simulation model is developed to explore the linkages between managerial mentalmodels, resources and performance. It represents resources as endogenous flows and mental modelsand resource constraints as exogenous parameters. This allows, for example, the impact of heterogeneity in mental models, on such factors as the time path of resource allocations, resources andcapabilities, and ultimately performance, to be studied in two firms (business units) in the insuranceindustry.

Findings – In general, heterogeneity in mental models leads to differences in performance in the long

run. This finding is reinforced by the presence of resource constraints. Facing strategic change,however, it is often difficult for senior managers to overcome the influence of well-establishedmanagerial mental models or recipes which create cognitive inertia and, in turn, hinder performanceimprovements.

Originality/value – There are few empirical studies which explore the impact of changes in mentalmodels and resource constraints on firm-performance and resource allocation decisions.

Keywords Modelling, Cognition, Resources, Resource management, Business performance,Competitive strategy

Paper type Research paper

IntroductionAccording to the resource-based view (RBV), sustainable differential performance may

be obtained from a firm’s superior resource position (Dierickx and Cool, 1989), whichresults from the firm’s decision processes including prior resource allocation decisions(Levinthal and Myatt, 1994), In the presence of resource constraints, resourceallocations are strategic choices (Child, 1972), which are driven significantly by mentalmodels of the competitive environment (Mintzberg, 1975; Prahalad and Bettis, 1986).

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1755-425X.htm

The authors acknowledge the helpful and constructive comments of the Editors and theanonymous reviewers.

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Journal of Strategy and Managem

Vol. 2 No. 3, 20

pp. 217-2

q Emerald Group Publishing Limi

1755-42

DOI 10.1108/175542509109824


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Thus, the existence of a range of managerial mental models in association withchanges in resource constraints will produce shifts in firm performance and affect theresulting industry structure. The determination of how changes in resource constraintsand managerial decision processes influence a firm’s resource allocations and

performance is highly relevant for the evaluation of firm performance, yet, it hasreceived relatively little attention in the literature.

The paper addresses this gap by first examining the current literature concerningthe linkages between managerial cognition, the presence of organizational slackresources and the RBV. It then, in an empirical context, tests using a simulationapproach, the impact of managers’ mental models and resource constraints onperformance. The existence of, and shifts in, resource constraints (Bowen, 2002)focuses attention on the strategic implications of mental models. The managerialcognition literature recognizes that managers’ mental models (or frames) of thedecision situation influence managerial choices (Bobbitt and Ford, 1980). Indeed,heterogeneity among firms’ shared mental models in the context of a competitiveindustry may re-structure both industry boundaries and segments (Porac et al., 1989).However, it is unclear how these mental models impact resource allocations anddecisions at the firm level that arise from resource constraints.

We use a process-based simulation method (Davis et al., 2007) to examine first theimpact of managers’ mental models on firm performance. We then investigate how achange in a firm’s resource constraints, and hence in resource profiles, alters resourceallocations thus creating and sustaining resource heterogeneity and leading todifferential performance.

The paper first explores these issues by developing propositions from existingtheory and explaining how the simulation method can test these propositions.The simulation experiment undertaken in a real-life insurance organization is thendescribed and the results are analyzed and discussed. Conclusions are drawn which

focus on the implications of the results for both strategy theory and managerial practice.

Theoretical backgroundThe managerial cognition literature indicates that the process of strategic decisionmaking involves the use of cognitive simplification processes by managers(Schwenk, 1984). It notes that bounded rationality (Simon, 1957), biases andheuristics (Tversky and Kahnemann, 1974), strategic assumptions and frames of reference (Markus and Wurf, 1987; Handy, 1985) create the cognitive maps or schemataof senior managers (Schwenk, 1988). A way to express cognitive maps (Huff, 1990) isthrough the conceptual framework of mental models (Craik, 1943; Holyoak, 1984;Rouse and Morris, 1986).

Cognitive scientists have used concepts such as schemata, cognitive maps and

mental models to describe the mental representation of the knowledge of activitiessuch as remembering, perceiving, reasoning, and decision making (Hodgkinson, 2003).We, therefore, use the term mental model to refer to these inter-related terms. In essence,it represents a “working model” of a phenomenon (Johnson-Laird, 1983), that is,a simplified, internalized representation of the knowledge and understanding of agiven reality (Hodgkinson, 2003).

Managerial mental models have a large impact on the implementation of strategicdecisions and thus, performance (Klimoski and Mohammed, 1994). Further, the sharing

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of mental models with cognitively-close competitors influences strategic groupformation and in turn industry structure (Porac et al., 1989; Johnson and Hoopes, 2003).Thus, the managerial task of reconciling the heterogeneity of cognitive maps andframes of reference can improve processes of strategic decision-making (Hodgkinson

and Johnson, 1994). It is important that heterogeneous cognitive maps at the firm-levelare surfaced so that a range of ideas are discussed in strategy formulation andinnovation (Tyler and Gyanwali, 2002; Swan, 1997).

Since mental models usually change very slowly (Hodgkinson, 1997), they cansometimes lead to cognitive inertia in organizations (Porac and Thomas, 1990).Therefore, the level and availability of organizational slack resources (Child, 1972) mayinduce a change in managers’ mental models (Ginsberg, 1994) and enhance the firm’scapabilities for achievingstrategic advantage and competitive performance.

Figure 1 shows how mental models and resource constraints impact performance.The RBV literature argues that sustainable competitive advantage requires unique

and inimitable resources (Barney, 1991). It is the heterogeneity of (potential) skills andcapabilities available from its resources that gives each firm its uniqueness (Penrose,

1959). Such capabilities are not only a complex function of existing resources andcapabilities, but also of mental models, which ultimately determine the use of scarceresources. Persistence in the heterogeneity of mental models may lead firms to differ, atleast, in the short-term allocation of their resources and capabilities (Mahoney, 1995;Noda and Bower, 1996). This differential resource allocation will lead to differentialperformance over time (Tripsas and Gavetti, 2000), even if there is no initialheterogeneity in resources, capabilities or skills. Hence:

P1. An initial difference in mental models will lead to differential performancebetween firms over time, even if they are initially identical in all other aspects.

Organizational slack is often described as a buffer resource that facilitates firm

adaptation and change (March and Simon, 1958; Thompson, 1967). Bourgeois (1981)indicates that slack resources can be used for the maintenance of coalitions (March andSimon, 1958; Cyert and March, 1963), conflict resolution (Pondy, 1967), effectiveoperations management (Thompson, 1967) and experimentation with new strategies

Figure The impact of ment

mode

RC RA P

MM

R & C

MM = Mental models

RA = Resource allocation

R & C = Resources & capabilities

(Changes in) (Shifts in)

Strategic choices

OS

OS = Operating slack

RC = Resource constraints

P = Performance

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and strategic innovation (Hambrick and Snow, 1977; Cyert and March, 1963; Nohriaand Gulati, 1996). Bourgeois puts forward two main functions for slack resources:internal maintenance of such issues as conflict resolution and a work-flow buffer on theone hand and facilitation of strategy development through innovation and political

processes on the other hand. The latter differs from the former as it affects resourceallocation through the consequences of a strategy process involving managerial maps,decision-making behavior and strategy implementation.

However, there is a lack of clarity in the literature about the role of slack resources inachieving organizational efficiency or effectiveness. In the satisficing mode of decisionmaking it is noted that a decrease in slack may improve efficiency (Child, 1972). On theother hand, it is pointed outthat an increase in slackcan produce greater disagreement instrategy and goals within top management, as there are more flexible resources fordiscretionary use (Bourgeois and Singh, 1983). Given adequate operational freedom atlower levels of management, this may result in different resource allocations and hencedifferential performance. This arises from the heterogeneity in managers’ mental modelsand framing of the competitive environment. Yet, these authors also report some

empirical evidence showing that an increase in slack does not necessarily intensifystrategy discord particularly in routine, operational decision-making situations.Therefore, we assert that, when certain tasks are seen as routine even if slack resourcesare available, managers in a satisficing mode will dedicate only some minimumamount of resources to execute all their responsibilities, irrespective of their mentalmodels – heterogeneous or not. This would tend to lead to similar resource allocation.Hence:

P2a. When there is positive slack in managerial time such as an absence of, or areduction in resource constraints, an initial difference in mental models willnot lead to differential performance between firms over time, provided theyare initially identical in all other aspects.

However, when there is a presence of negative slack resources in these conditions,managers do not have the required minimum resources to dedicate towards meetingthe same responsibilities. In this situation, an opportunity exists to reallocate resourcesand to compromise on the minimum resource requirements. Heterogeneity inmanagers’ mental models will prompt more disagreements about what to compromiseon. Thus, a drop in the availability of managerial time (negative slack) intensifiesresource constraints. It may bring out disagreements in strategy inherent inheterogeneous mental models and in conditions with sufficient operating freedom itcan result in differences in resource allocations and performance. Hence:

P2b. When, there is negative slack in managerial time and there is an initialdifference in the shared mental models amongst firms, it will lead to

differential performance amongst them over time, even if they are initiallyidentical in all other aspects.

Research methodThe propositions require the examination of strategic situations that evolve over time,have endogenous cause and effect implications and involve a large number of variables. Such investigations require the specification of the impact of actualdecisions and interactions over time (Mackenzie, 1986; Abbott, 1990; van de Ven, 1992).

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The method of simulation can address these requirements (McKelvey, 1997, 1999;Davis et al., 2007). Further, it can enhance ecological validity (Hodgkinson et al., 2002),that is, the relevance and reproduction of the details of the context in a realistic manner,by specifying the model with rules based on actual behavioral and related context-rich

details. Further, the repeatability of creative simulation experiments enhances rigour.Mental models can be elicited in different forms (Cooke et al., 2000). A common form

is though causal maps. Comparing maps which combine structural and causalelements remains an important and stimulating challenge (Hodgkinson and Johnson,1994). When represented mathematically, they can blend with the process of deployment of different resources, capabilities and their dynamic interactionconsequences over time. This allows comparison of consequences of different mentalmodels by simulating the evolving behavior of interrelated variables (Sastry, 1997).

System dynamics is chosen as the basic simulation approach because, besidesfacilitating the execution of the possibilities above, it has three other advantages. First, itfollows a history-friendly approach that models real business structures according to theempirical facts in an industry, including its actual resources and their configurationsand transformation over time. Second, it is a non-equilibrium approach (Foster, 1998;Mathews, 2002), which permits the portrayal of choice, heuristics and dynamics (Millerand Shapira, 2004; Rivkin, 2000). Third, it is compatible with the RBV literature, since italso employs Penrose’s distinction between resources and productive services, whichwas later translated as “stocks and flows” (Mahoney and Pandian, 1992). Given thatcritical firm-specific resources need time to accumulate (Dierickx and Cool, 1989), theliterature has empirically and extensively used “cumulative” variables (Thomas et al.,1992; Knott, 2003; Helfat and Peteraf, 2003); although authors (DeCarolis and Deeds,1999) have used the labels “stocks” and “flows” without taking advantage of theproperties of accumulation or the difference between stocks and flows. Furtheradvantages of using system dynamics in process models are detailed thoroughly in

other areas (Sastry, 1997; Repenning et al., 2002).We use firm-level resources and the causal links among them to write equations that

constitute the mathematical representation proposed above, while organizing them interms of stocks and flows to facilitate accumulation (Forrester, 1961). Using conceptsfrom system dynamics, a basic structure of stocks and flows is used to create andrepresent different capabilities that constitute a process. System dynamics is uniquelygeared towards comprehending natural and social science phenomena in terms of stock-and-flow models, since it supports and documents the construction, simulations,results, and insights from them (Sterman, 2000). As a result, differential rates of accumulation arising from heterogeneity in managerial choice or context can generatedifferent time-paths for critical resources and for performance.

The mathematical representation was developed in modeling the strategic activities

of the agency department (strategic business units – SBU) of a major firm in theinsurance industry. We used three stages to develop the model: data collection,formulation, and validation. The field work during data collection was undertakenin collaboration with a management consulting firm. In this stage, more than60 interviews were undertaken by four mid-level consultants, involving around40 managers as well as some ex-managers of the various branches and the corporateoffice of the insurance firm in question; many of the senior managers were interviewedmore than once. These hour-long interviews were carried out in a semi-structured

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manner where they were asked about the execution of their responsibilities. The aim of the interviews was to develop an understanding of the various policies, processes,process adjustments and informal targets that were active. Wherever possible, writtendata and internal studies were used to support and verify claims. Statistics about

salesmen and their performance was a key part of this; the numerical data aboutsalesmen was analyzed to reveal longitudinal trends. Interview content was probed tosee which aspects of management were common to all and which were distinct; it wasused to cluster the branches according to differences in management practice andperformance.

In the second stage, we developed a theory to explain the evolution of differentbranches, with the help of causal loop diagrams (Sterman, 2000). The procedure is alsodescribed in Repenning and Sterman (2002). The variables and causal links from thisanalysis include the feedback processes that generate the dynamics of interest.Separate diagrams were created for each cluster of branches. These were merged to aunified framework so that a single set of feedback processes could generate the maintrajectories of interest. Subsequently, each causal link was converted to equations or

graphs where the variables change with time. Literatures in various relevantdisciplines were also consulted to justify the causal relationships. The set of equations(or model) was further calibrated with the help of the available numerical data. In thelast stage, three independent industry experts validated the behavior of the model byconducting their own experiments. Since these results conformed to their expectations,we assume that the link between the structure of the model and its field setting is valid.Further experiments were conducted by two industry experts who were on theresearch team.

Our model represents resources as endogenous stocks, resource allocations asendogenous flows and certain aspects of mental models and resource constraints asexogenous parameters. This allows initial outcomes to influence the time-path of various resource allocations, resources and capabilities to produce an overall picture of the impact of heterogeneity in mental models on the performance of the firms beingmodeled.

Empirical context and model structureFollowing the development of the research propositions, the experimental setting of thefirms must satisfy two conditions:

(1) Initial heterogeneity should be limited to mental models, to show their potentialimpact on performance.

(2) A focus on a very short list of potentially differentiating factors for the chosenfirms to allow for the depiction of the situation without the complexity of toomany details, thereby letting us address the complexity arising from the keydifferentiation factors and their interactions.

The sample firms, which are business units (in this case two business units of a majorinsurance firm), must have the same strategic positioning and capabilities. Yet, withinthe sample, the firms must initially differ only in the factor suspected to causethe difference, but later, also in performance (Pettigrew, 1990) – this allows us toexamine the “pure” impact of the factor in question. The data available in the matchedsample must include resource structure and configurations and managerial choices

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resulting from mental models. The selection of the firms is motivated by a four-stepprocess (Rouse and Daellenbach, 1999). These process steps indicate that the samplemust be matched by drawing it from the same industry and same strategic groups; yet,it must differ significantly in performance when measured by the same indicators.

To satisfy the conditions above, we have chosen to examine two SBUs in the agencyarea from a leading firm in the UK insurance industry. Our choice is appropriate for thereasons that follow. In the mid-1990s new regulations were imposed on all sales agentsin the life insurance industry. These required salesmen to specify whether they wererepresenting a third-party. It also required insurance firms to specify the amount of commission being paid to agents for selling a specific policy. The effect was to increasebureaucracy and compliance costs, and consequently consumed a significant share of managerial time. This required a strategic response about which market space firmswanted to occupy. The lack of an immediate strategic response combined withcompetition for reducing the newly-transparent margins made top players ultimatelyabandon this market segment and disband their sales forces. The sales forces that

survived did so by moving up-market, in terms of selling more customized productsthrough better trained salespersons. The different responses, arising essentially fromdifferent mental models, re-configured industry structure. Figure 2 shows aprogressive divergence in the most important aspect of performance among variousbusiness units in the firm, which have been classified into three categories – prosper,survivor, and uncompetitive.

In the insurance industry, it is difficult to sustain a difference based on productcharacteristics. Hence, the quality of salespersons, monitored closely by the topmanagement team, has important strategic consequences. Therefore, our modelfocuses on this intangible resource by tracing the causes of its evolution through time.We chose two business units in the same firm that sold insurance policies to similarmarkets. Being branches of the same corporate unit ensures that, in the stylized firm of

Figure Productivi

of the three group

30,000

50,000

70,000

90,000

1,10,000

1,30,000

1989 1992 1995 1998 2001 2004

Productivity is measured in UK £ per person per year

Prosper

Survivor

Uncompetitive

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the model, the environment, nature of competition, intended strategic position,capabilities, capability improvement abilities, endowments, corporate targets, initialresources and policies are identical for both – allowing us to introduce a heterogeneityin mental models to see if that is capable of bringing about a significant divergence in

the differential performance of the two business units.Appendix 1 presents a short background of the insurance industry and a brief

description of typical firms in the industry in terms of their resources and activitiesperformed. The business units that afford the greatest flexibility for the firm toestablish a sustainable competitive advantage through productivity are the marketingagencies. Management decides what kind of agents (i.e. sales persons) to hire, howmany to hire, how much training they should get, how to train them, where to spreadtheir agents, how to identify and retain/promote their star performers and how toimplement the growth target announced by the top management team. This unit isresponsible for the entry of new money streams and to maintain the firm’s “goingconcern” status. It is also the largest cost item that can actively be managed in thebusiness plan of the insurance company. For these reasons we focus on the functionsassociated with agencies as strategically appropriate business units of firms in theinsurance industry. The insurance industry has its own particular measures of profitability as we also explain in Appendix 1.

Model structureThe overview of the core of the model is shown in Figure 3, followed by a word-sketch.

As the model focuses on the activities of a stylized agency department of a businessunit of the insurance firm, henceforth we refer to it as a firm. This builds upon thetradition of stylized firms in economic literature typically symbolized by very fewparameters. The core of the firm consists of four sectors that address headcount, skill,productivity and compensation. The headcount sector is the collection of processes

directly concerned with managing agent headcount, consisting of hiring, firing andpromotions to grow the firm. Managers recruit agents from the market to be part of theagent body that sells policies to prospective customers. Firms seek to hire moreexperienced agents and retain the better-performing agents as they deliver more value.A fraction of the agents are always moving out through resignations and firings.Promotions are decided in-house and the rate depends on managerial vacancies, as theratio of managers to agents is legally regulated. The number of agents and the overallrates of agents moving into or leaving the firm have an important impact on thedynamics of the skill pool of the agents.

The skill sector is about the management of agents’ sales skills. For simplification,multiple dimensions of sales skill have been collapsed into one dimension, which isbased on the years of sales experience possessed by an agent. Even though the

measure of this skill is fairly intangible, it is one of the most important drivers of performance in the industry. The movement of agents into and out of the firm has acorresponding impact on the firm’s skill pool. The higher the skill level of thosequitting the firm, the greater is the depletion of the skill pool. It is thus a mostimportant challenge to management to maintain and improve the skill level of theiragent pool.

The productivity sector highlights the productivity and turnover of the sales agentswhich adds to the product portfolio. The sales function provides the firm with

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premiums for the life of the product’s length, if they do not lapse. Policy sales andlapses are a function of the agents’ skill level. The larger the product base which isdefined as the inventory of live policies sold by the firm, the larger the cash flow andrevenue to the insurance firm. We use skill level per agent in the firm as the keyproductivity and profitability indicator.

The compensation sector models the mechanics of fixed and variable compensation,or commission, for agents and managers. Agents are compensated based on the salesmade and the lapses that occurred in a particular year. It specifies how the level of skills, the lapse rate and the quit rate of the agents affect compensation and, in turn,how the compensation level affects the same three variables. If agents perform above

the performance level expected by the market, the resulting compensation is abovemarket expectations. A performance lower than market standards of performance,brings inferior compensation. Thus, the compensation affects the quit rate of agents,which influences the lapse rate of new policies sold, as well as the attractiveness of thefirm to new agents who are considering whether to join the firm. Sectors addressingthe comparison of performance and managers’ reaction to the comparison are outsidethe direct scope of the model; nevertheless these issues have been addressed after thenext section.

Figure Model overvie

Productivity

sector

Skill

sectorHeadcount

sector

Compensation sector

Agent quits

Hires

Agents promoted

Agents Skill per agent

Product

sales

Products

lapsingImpact on

lapse rateAgent

quit rateImpact on

agents

recruited

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The simulation adds a partial model to the basic core structure detailed above[1].During the execution of the simulation, new values are calculated at every time step foreach variable. One can explore alternatives by changing the coefficients or theequations or both. Its advantage is that the link between structure and behavior is

easier to grasp when the structure is developed in stages, with access to intermediateresults (Morecroft, 1984, 1985; Sterman, 2000). The simulation experiment willtherefore show the time-path of two firms (actually business units of an insurancefirm), a and b , having identical resources, resource structure, policies regardingstrategic positioning and (lack of) amount of slack managerial time – differing initiallyonly in managerial mental models. Thus, any differential performance can only beattributed to policy differences arising from mental models (a test of P1 ). Further, wewill address the interaction of mental model heterogeneity with resource constraints inthe two subsequent propositions ( P2a and P2b ).

Simulation: results and analyses

The core model assumes that managers have infinite time. In reality, managers have toallocate their time amongst different responsibilities such as recruiting, training andadministration. The latter is imposed through industry regulations but the others arisefrom the physical constraints of the chosen strategic position and direction:, i.e. tomake up for those who leave, to meet growth targets set by the top management team,and to improve average firm productivity. Together, these actions tend to stabilize thelapse rate of new policies sold. However, the amount of time actually spent to trainagents depends on the managers’ subjective beliefs about its efficacy. In certaincircumstances –, e.g. a drop in managerial time, they may adjust their allocation of time in subtle ways. This intangible, idiosyncratic but subtle adjustment is an exampleof heterogeneity in mental models.

Simulation: the dynamics of preferential time allocationAccording to the theory of substantive rationality (Simon, 1976) – which guidesbehaviour to attain a given goal, under constraints – there ought to be an optimizedallotment of time to meet all responsibilities. However, we observed in our interviews amanagerial preference for establishing priorities. For instance, managers in a recruitrather than train, while managers in b prioritize training over recruitment. Suchheterogeneity in mental models between the two firms arises due to a difference inpriorities, which in turn results from differences in motivation arising from differencesin psychological needs. This is congruent with self-determination theory (Deci andRyan, 2000) which takes into account three main aspects in the tasks of recruiting andtraining: ability development vs demonstration (Nicholls, 1984; Dweck, 1986),intangible vs tangible rewards (Herzberg, 1982; Riedel et al., 1988) and extrinsic vs

intrinsic motivation (Thomas, 2000; Brief and Aldag, 1977). We simulate theconsequences of the time allotment behavior of an “extremist” in each category.“Extremist” managers move on to their other task only after completing the full extentof their favorite task. Administration is compulsory, so it is out of the purview of allocation. Appendix 2 details the mathematical equations of the two responsibilities,how time is allocated and the partial model (i.e. the causal links) for time allocation.

Initially both a and b have skill levels of three years of experience as per marketexpectations, the same number of agents and just sufficient time for all three tasks.

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The exogenous change of increased administrative responsibilities occurs only afterfive years. The simulation results display the trajectories of the firms for 20 years(Figures 4-6). They show no divergence in the first five years; while Figure 4 showsnegligible separation between the two firms, Figures 5 and 6 do eventually show

dramatic separation. a maintains its growth rate (Figure 5) and consequentlyaccumulates a sales force about 16 times the initial size but sees a steady decline in thequality of its agents (Figure 6). b has a clear increase in the quality of its agents, but itsgrowth rate has clearly suffered though the gap in the growth rate eventually starts tonarrow. The outcome is a sales force barely five times the initial value.

The trajectory deviations observed after the first five years of Figures 5 and 6apparently support the first proposition which states that heterogeneity in mentalmodels will lead to differential performance. In Figure 5, a sees a decline inproductivity. In Figure 6, b sees a decline in the growth of sales agents. Both thesedeclines occur only after the reduction in the amount of time managers could dedicateto discretionary activities. Prior to the first five years time available to managers is justadequate with respect to the demands placed on them; thus managers are not yetforced to make choices and the different propensities and priorities have not reallycome into play, though they are there. This supports P2a which states that in theabsence of constraints, there is unlikely to be differential performance – evidenceagainst the first proposition. We hesitate to conclude that the persistence of mentalmodel heterogeneity (in recruiting vs coaching) is the only reason for a clear differenceto the firms’ performance as there is no impact until the slack in managerial time

Figure 4Product sal

Net products sold per year

Time (year)

0 4 8 12 16 20

83 K

58 K

33 K

8,000

Units: policies per year (left ) and equivalent years

of experience (right )

108 K

α: Full weight to recruiting

β: Full weight to training

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becomes a shortage of time, thus indicating the presence of resource constraints andthe moderating effect of mental models.

The divergence is to be expected as a, after the exogenous regulatory change,spends a greater-than-appropriate share of their time recruiting. Though the headcount

Figure 6.Divergence in size

Graph for achieved growth rate0.20

0.10

0.05

0.0

0.15

0 4 8 12 16 20 0 4 8 12 16 20

Units: dimensionless (left ) and agents (right )

Agents (size of sales force)

Time (year)Time (year)

1,600

800

400

0

1,200



Figure 5.Divergence inperformance

Skill per agent6

5

4

3

20 4 8 12 16 20

Time (year)



Units: policies per year (left ) and equivalent years

of experience (right )

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target is met, neglect of training causes a cumulative weakening of the skill pool.b spends more of its time training and accumulating superior productivity withobvious productivity implications, but the headcount targets are not met; the actualgrowth rate falls way short of what was intended. These outcomes provide evidence

for the last proposition which states that a reduction in managerial time in the presenceof mental model heterogeneity can manifest as differential performance. Table Isummarizes the results which show full support for P2 , and contingent support for P1.

DiscussionThere is negligible separation in Figure 4 which shows an aggregate financial measurecombining productivity and absolute size. In the simulations these two are negativelycorrelated; so the critical information about the divergence gets suppressed.Examining firm performance at such an aggregated level does not help usappreciate the significant differences that originate from differences in cognitive biasesand mental models. Superficially, the outcome may point to “equifinality” of actionsvis-à -vis sales revenue in the earlier years, but in reality, the difference in thefunctionality of the key resources (Peteraf and Bergen, 2003) of salespersons and theirskills led to an overhaul of the then-existing market segments.

This outcome is very interesting as it shows the deviation, arising due to acompromise made to pursue a personal target, would be ambiguous, if not completelyopaque to the top management team. It would be difficult for them to determinewhether failure to achieve the requisite growth rate was due to external circumstancesbeyond their control or purely due to timid planning, when presented with a fait accompli . Thus, differences arising from such heterogeneity persist, even while it isdifficult to track their sources. Considering the performance of the real-life firm inquestion, the masking of the differences here could be one more reason why thebranches continued along different paths until it was too late to recover the situation.

A review of the seeming lack of reaction by management is justified, as the actualindustry events parallel the simulation results. We have used extreme values toemphasize differences, but the important similarity to the empirical events is thatmanagers, on eventually understanding the increasing lag in skills or seeingthemselves fall behind in size, did not change their practices. Interviews with seniormanagers and industry experts revealed this was due to strong cognitive orientations(and some inertia). Apart from taking advantage of the delayed emergence andintangible nature of their poor performance, managers in a rationalized away their

Support for the following propositions in thesimulation results

In the firstfive years

In the lastfew years

P1. Heterogeneity in mental models leads todifferences in performance

No Yes

P2a. Heterogeneity in mental models in absence of resource constraints leads to NO differences inperformance

Yes NA

P2b. heterogeneity in mental models in presence of resource constraints leads to differences inperformance

NA YesTable

Summary of resul

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that are ever more difficult to interpret. Nevertheless, the model is open to extension – e.g. one could bring in the impact of the financial performance of the investing arm of the insurance firm through the price of its policy premiums and examine theconsequences on competitive interactions.

In the theory section, we laid out the reasons why performance differentials existand explained how they persist. Through the simulation of the model of the insurancefirm, we have shown that, in certain circumstances, the heterogeneity of mentalmodels, in interacting with resources, resource constraints and resource linkages, islikely to create a change in resource allocation and perhaps go beyond, to increasedifferential performance. We also pointed out that these heterogeneities are likely topersist and therefore it is difficult to eliminate their harmful effects in the absence of sufficient slack.

It should be noted that, initially firms in our model lack any distinguishing featuresin terms of resource-stocks (Amit and Schoemaker, 1993) that can be classified asunique resources, (Barney, 1986, 1991; Peteraf, 1993) – notwithstanding thehighly-inimitable variation in mental models that we have highlighted. Rather, it isa process of accumulation and depletion of ordinary resources that brings about adifference in the critical resources of the firms such as the number of agents and theskill pool of the agents, etc. that in turn provide a basis for differential performance.The implication is that RBV should seek to integrate process considerations so as tocomplement and expand the understanding and insights it offers in order to addresshow the dynamics of resources and capabilities build or dissipate competitiveadvantage. This could help deeper theory building about dynamic aspects of resourcesin RBV. For example, a change in resource allocations, driven by mental models andanticipated changes in resource constraints, constitutes an underlying mechanism of dynamic capabilities (Teece et al., 1997) which facilitates the creation of newcapabilities. We have also indicated some of the dynamic aspects about the link

between resources and competitive advantage in the previous section.For top management, we believe that adopting a dynamic or systems perspective issuperior to a static or equilibrium perspective, when addressing strategy development(King et al., 2002). The prescriptions from the traditional RBV encourage managementto acquire critical, inimitable resources, which are then supposed to deliver competitiveadvantage provided there is a mechanism to appropriate the rent generated from theseresources. However, our experiments reveal that so-called critical resources derive theirpotency from the particular competitive dynamics of the existing situation. Theirimportance changes with the shifts in resource constraints and allocations achievedthrough dynamic balances and reinforcements. Changes in strategy are rarely made intime since they need convincing cognitive interpretations that link events (Barr andHuff, 1997). Therefore, managers need to fully understand the impact of external

factors and mental models that affect the process of achieving appropriate reinforcingand balancing dynamics as they search for systemic insights about superior ways of building resources over time instead of leaving it to chance, habit and circumstances – or else these dynamics may provide unpleasant and pleasant surprises in hindsight,with not so flattering implications on their own efficacy and efficiency.

Note

1. The complete details of the simulation equations can be had from the first author.

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Appendix 1. Description of activities in the life insurance industryMajor players have a common set of tasks – selling insurance contracts, selecting risks, fixing

and collecting premiums, writing policies, investing money, keeping accounts, collecting,researching and analyzing statistics, processing claims and dealing with legal issues and cases.

These tasks are executed by building the required skills as individual firms, or shared from acommon pool, depending on the quantum of required investment and the scope for

differentiation. Since offerings are easy to copy, it is impractical to use this for sustained

differentiation from competitors. Owing to economies of scale collecting, researching andanalyzing statistics are pooled.

The claims department (a cost center) processes claims and the legal issues involved therein.

Scope to differentiate here is limited. The investment department (a profit center) investsincoming premiums. Some countries require the financial performance of investments be legallykept apart from rest of the organization. The last activity is sales of insurance contracts. Sales are

made through push (sales) and pull (marketing). Scope for differentiation through marketing is

limited, as product details are quite transparent. So, marketing campaign sizes are relativelysmall compared to funds for the agency department, which handles sales agents. Figure A1

shows the departments. Several kinds of agency systems exist – e.g. general agency and branch

office systems in life insurance, independent agency and exclusive agency systems in propertyand casualty insurance. All agency systems sell policies through agents for commission. They

differ in the degree of control that the firm management exercises over its agents and their

Figure A1.Model boundary

Actuarial

dept.

Marketing

dept. Agency

dept.

Claims

dept.

Investment

dept.

Basket of

policy

variations

Portfolio

of

policies

Claims

Pricing

of

policies

New

kinds of policies

Sales Cash

inflows

Investment

of cash

Reject or

accept

Lapsed

policies

Matured

policies

Premiums

Operatingexpenses

Model

boundary

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compensation structure. Agents are responsible for minimizing “lapses” in the policies they sell.A “lapse” is when the client discontinues payment of premiums towards his insurance contractbefore the contract permits.

Activities in a typical agency department The stylized agency department of large insurance industry firms will henceforth bereferred to as a firm. The firm sells policies to those who want to buy insurance. Salesprovide the firm with premiums for the length of the life of the product, if they do not“lapse”. Larger the product base (i.e. the inventory of live policies sold by the firm), largerthe cash flow and revenue to the insurance firm. Sales are in a cycle of three stages. First,agents are recruited from the market as employees, to be part of the agent body that sellspolicies to prospective customers in the market. Firms always seek to hire more experiencedagents from the market and to retain the better-performing agents (as they are economicallymore attractive for the firm). Second, accumulated policy sales of policies in force form thebasis of the future revenue stream (as premiums). Policy sales and lapses are a function of the agents’ skill level. Third, agents are compensated based on the sales made and lapsesoccurred in that particular year.

By joining a firm, agents increase the headcount of sales employees and add their sales skillsto the skill pool of the firm. From time to time, some agents quit the firm and some are promoted.These decrease the headcount of sales employees and the aggregate skill pool of the firm. If agents quitting the firm have lower than average skill level, those promoted will have a higherthan average skill level. It is thus a challenge to management to maintain and improve the skilllevel of their agent base. Agent compensation is very important. If agents perform above theperformance level expected by the market (i.e. the average skill level prevalent in the market,which is assumed to be three years in the simulations), the resulting compensation is abovemarket expectations. Lower than market standards performance means inferior compensation.In turn, compensation affects the quit rate of agents (which influences the lapse rate of newpolicies sold) as well as the attractiveness of the firm to new agents who are considering whetherto join the firm.

We use quantum of new products sold per year in lieu of the premiums earned annually due

to new business. As we assume products of standard length and premium, the only dimension of distinction in sales is the number of products sold. Larger the number of contracts the firm cansell (that do not lapse), per unit labor cost, greater the profitability of the firm.

Appendix 2. Mathematical representation of managerial responsibilitiesThe measure of sales productivity is given by “Skill per Agent”:

Skill per agent ¼ ðagent sales skillÞ

ðagentsÞðunits : years of experience=agentsÞ

Agent sales skill is the pooled experience of all the agents in the firm:

Agent sales skill ¼ Integration of ðadded skill at hire2 lost skill from agent quits

2 lost skill from promotion þ agent learningÞ over time

ðyears of experienceÞ

Added skill at hire ¼ hires * agent skill at hire ðyears of experience=yearÞ

Lost skill from agent quits ¼ agent quits * skill per agent* relative skill of quits

ðyears of experience=yearÞ

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Lost skill from promotion ¼ agents promoted * skill per agent * relative skills of promoted agents

ðyears of experience=yearÞ

Agent learning ¼ Agent learning rate * agents * share of time allowed for coachingðyears of experience=yearÞ

The last term on the right hand side is the share of time that managers of that firm devotetowards coaching. Recall that they also have to devote time towards recruiting. These are notmeasured here in fractions but in more absolute terms. The following equations build up theamount of time theoretically required by managers:

Target number of recruits ¼ computer adjusted target * agents þ agent quits

þ agents promoted ðagents=yearÞ

Indicated recruiting time ¼ days needed per recruit * target number of recruits ðdays=yearÞ

Indicated coaching time ¼ target days of coaching per agent in a year * agents ðdays=yearÞ

Total time needed ¼ Indicated recruiting time þ indicated coaching time ðdays=yearÞ

Ratio ¼ðindicated recruiting timeÞ

ðindicated coaching timeÞðdays=yearÞ

The following equation gives the amount of time theoretically available to managers:

Managerial time available ¼ managers * working days per year per manager ðdays=yearÞ

The following equations show how the available amount of time is allocated:

Total shortfall ¼ MAXðtotal time needed2managerial time available; 0Þ ðdays=yearÞ

Shortfall in recruiting ¼MINðMAX ðratio * shortfall * ð12 weight to recruitingÞ

ðweight to recruiting þ ratio2 weight to recruiting* ratioÞ;

indicated recruiting time2managerial time availableÞ;

indicated recruiting timeÞ ðdays=yearÞ

Shortfall in coaching ¼ MAXð0; shortfall 2 shortfall in recruitingÞ ðdays=yearÞ

Share of time allowed for recruiting ¼ðindicated recruiting time2 shortfall in recruitingÞ

ðindicated recruiting timeÞ

ðdimensionlessÞ

Share of time allowed for coaching ¼ðindicated coaching time

2shortfall in coachingÞ

indicated coaching time

ðdimensionlessÞ

A shortfall in recruiting has its impact on the growth achieved through hiring, as indicated bythe following equations:

Agents ¼ integration of ðhires2 agent quits2 agents promotedÞ over time ðagentsÞ

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Hires ¼ target number of recruits * share of time allowed for each recruiting ðagents=yearÞ

Target number of recruits ¼ growth target * agents þ agent quits þ agents promoted

ðagents=yearÞ

Agent quits ¼ agents * agent quit rate ðagents=yearÞ

Agents promoted ¼ promotion rate ðagents=yearÞ

Corresponding authorHoward Thomas can be contacted at: [email protected]

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