Week-14

Decision Sciences
Volume 47 Number 3
June 2016

© 2015 Decision Sciences Institute

Decision Making in Cross-Functional
Teams: The Role of Decision Power*

Zhijian Cui†
IE Business School, Calle de Maria de Molina, 12, Madrid 28006, Spain,
e-mail: [email protected]

ABSTRACT

Through a series of game-theoretical models, this study systematically examines de-
cision making in cross-functional teams. It provides a framework for the design of an
organization-specific decision-making process and for the alignment of a team’s mi-
crodecision with the “optimal” decision that maximizes the firm’s payoff. This study
finds that even without changing the team leader, firms could change and even dictate the
team’s microdecision outcome via adjusting the team member’s seniority, empowering
team members with veto power or involving a supervisor as a threat to overrule the team
decision. This finding implies that to reposition products in the marketplace, structur-
ing cross-functional teams’ microdecision-making processes is essential. [Submitted:
January 13, 2014. Revised: September 27, 2014. Accepted: December 6, 2014.]

Subject Areas: cross-functional team, decision-making process, decision
power, game theory, and stakeholder management.

INTRODUCTION

Many critical business decisions are currently made by cross-functional teams
composed of representatives from multiple functionalities or departments (Griffin,
1997; Hutchison-Krupat & Kavadias, 2013; Mihm, 2010). In the context of new
product development (NPD), for example, firms rely on cross-functional teams
for product development between 70% and 75% of the time (McDonough, 2000).
The widespread use of cross-functional teams is a result of the overwhelming
success attributed to them: cross-functional teams improve product quality by in-
corporating market information earlier in the design process (Terwiesch, Loch, &
De Meyer, 2002), reduce production costs by rendering products more “manufac-
turable” (Lovelace, Shaprio, & Weingart, 2001) and decrease development time
and rework (Griffin, 1997). Conversely, numerous studies also discuss the “dark
side” of cross-functional teams. Because of their diverse backgrounds, expertise,
access to information and so forth, cross-functional team members often have

*The author gratefully acknowledges the Associate Editor and two anonymous referees for their highly
constructive feedback during the review process. The author is also grateful for the grant from Spanish
Ministry of Economy and Competitiveness (ECO2013-48403-R) to support this research project.

†Corresponding author.

492

Cui 493

different opinions about “what should be done” (Atuahene-Gima & Evangelist,
2000; Griffin & Hauser, 1996; Pinto, Pinto, & Prescott, 1993; Mihm, 2010). Con-
sequently, team members’ objectives are often “misaligned” with firms’ overall
objectives and priorities (Hutchison-Krupat & Kavadias, 2013; Mihm, 2010). Such
misalignment, combined with the specific micro decision-making structure, may
lead to “suboptimal” decision outcomes.

One case that motivates our study involves the development process of
BMW’s 7-series sedan (Pisano, 2002). In developing the 7-series sedan, BMW
strategically planned to narrow the gap between its product and those of its Japanese
competitors by improving its product conformance quality and reducing its manu-
facturing costs. BMW possessed both a clear technology strategy and well-trained
design engineers. Unfortunately, because of strong organizational resistance, im-
plementing BMW’s repositioning objective was not a trivial task. At the level
of micro decision making, design engineers played a dominant project-leadership
role in BMW’s traditional design system, and their maximal flexibility was ensured
by the existence of a specialized prototype shop, general-purpose tooling and the
power to revise designs until several days before manufacturing began. In contrast,
manufacturing engineers were nearly excluded from the design process, and their
concerns regarding the ease and cost of manufacturing were rarely incorporated
into the product design. Even worse, the manufacturing engineers were not able
to learn about the detailed assembly process. Thus, they had to set up production
“from scratch,” and the 7-series sedan’s conformance quality, measured as the
likelihood of defect, was inevitably sacrificed.

Another example that motivates this study comes from a field study we con-
ducted at Nokia, a company famous for its cutting-edge hardware technologies in
the cell phone industry. In the past, Nokia allocated the majority of its financial
resources to hardware development. As the smart-phone market grew, its market
share began to drop, and its top management realized the strategic importance of
changing the focus of its R&D expenditures from developing hardware to develop-
ing software, such as applications and services. However, mid-level managers were
skeptical of this strategic initiative. In conversations with the authors, one senior
executive from the marketing functionality department quipped, “Our company
has historically been dominated by stubborn engineers, who do not care and do not
even know what today’s customers really need.” Ironically, one hardware design
engineer complained, “We cannot let important decisions be made by people with
‘soft’ minds…. Those people only care about customers; they do not always under-
stand technologies.” After several years of struggle, Nokia finally lost its dominant
position in the cell phone market.

The above examples show that a company in which design engineers enjoy
decision power will likely promote a design focus, with the potential downside of
excessive cost, whereas a manufacturing-focused organization may achieve mini-
mum cost at the expense of an inferior design output. Thus, the “optimal” decision
that a firm intends to pursue may be “misaligned” with the actual decision out-
come from a cross-functional team, and a simple “over-the-wall” approach (Adler,
1995) is insufficient to ensure that “correct” decisions are made within a complex
organization. In addition, both examples show that such “misalignment” does not
result from insufficient resources, such as expertise and machinery. How does
misalignment arise, then? Is it simply a result of bad management or an indicator

494 Decision Making in Cross-Functional Teams

of improper decision-making processes or even improper organizational design?
Moreover, are decision-making processes universally “proper” for all companies,
or do they depend on a company’s specific organizational characteristics? From
the perspective of a firm’s top management, what levers can companies use to
minimize such misalignment and thus mitigate the likelihood of “suboptimal”
outcomes?

Motivated by these challenges and questions, this paper aims to build a game-
theoretical framework that links team members’ preferences and decision power
that stem from both personal attributes and participation at certain stages of the mi-
cro decision-making process. Our results show when and why management should
use different levers (aligning preferences, adjusting team composition, empower-
ing team members with different types of decision power, and involving senior
management) to align a cross-functional team’s micro decision with the “optimal”
decision that maximizes the firm’s payoff. Our study therefore establishes a de-
tailed map between cross-functional teams’ micro decision-making processes and
decision outcomes and demonstrates the importance of managing and designing
the detailed structure of decision making.

In the following, we first review the literature and position our study within
the existing research. Then we describe the model setups and examine the impact of
team members’ preferences on a team’s decision outcomes and strategic misalign-
ment. Considering team members’ decision power that stems from both personal
attributes and participation in the decision-making process, we further examine
the team’s decision outcome under different settings in which the decision-making
structure is slightly altered. We conclude the study and discuss its implications in
the last section.

LITERATURE REVIEW

Prior research has established that conflicts among different firms or divisions
within a firm are caused by their fundamentally different interests (Balasubra-
manian & Bhardwaj, 2004; Hutchison-Krupat & Kavadias, 2013; Li & Atkins,
2002; Mihm, 2010; Narayanan & Raman, 2004; Siemsen, 2008). For example, a
firm’s manufacturing division incurs manufacturing costs and therefore primarily
aims to minimize costs (Li & Atkins, 2002), whereas a firm’s marketing division
aims to maximize revenue (Natter, Mild, Feurstein, Dorffner, & Taudes, 2001;
Balasubramanian & Bhardwaj, 2004). However, the marketing division’s pricing
policy, which maximizes its expected revenue, also increases the firm’s expected
leftover inventory, the cost of which is borne by the manufacturing division (Li &
Atkins, 2002). Thus, one party’s interest is maximized at the expense of another
party’s interest through a product-pricing policy that incurs excessive inventory
costs (Li & Atkins, 2002). In the references that examine inter-firm or -division
coordination, each party has its own interest and can make individual decisions
to maximize its interest. In the context of cross-functional teams, however, team
members share a fundamentally common interest, and team members’ differences
in objectives or predictions of decision outcomes are caused by their differences
in either the available information or attributions of causality (Groves, 1973). In
addition, all decisions in teams are jointly made by team members under a par-
ticular decision-making structure (Griffin, 1997; Griffin & Hauser, 1996). These

Cui 495

team-specific settings (i.e., in which team members have a common interest but
different objectives) constitute the point of departure for our study and require a
modeling approach that differs from that of prior research.

Existing studies on cross-functional teams have empirically studied the ef-
ficacy of management “levers” in mitigating conflicts among project members.
These management levers include aligning team members’ preferences via job
rotations and team-building training sessions (Griffin & Hauser, 1996), facilitating
communication (Griffin & Hauser, 1996; Henke, Krachenberg, & Lyons, 1993),
and building trust (Henke et al., 1993; Natter et al., 2001) and ensuring team auton-
omy (Griffin & Hauser, 1996). However, these levers are often studied in isolation,
and it remains unclear how these levers may interact with each other and the condi-
tions under which they can be jointly applied. Our study makes two contributions
to this stream of literature. First, we introduce decision power, which has been
rarely examined in prior research, as a management lever to align a team’s micro
decision with the firm’s objective. Second, we build a framework that considers
several management levers and provide new insights into the efficacy of some
well-established management levers from the perspective of strategic alignment.

Existing studies in the NPD literature have analytically examined the de-
sign of mechanisms to align a team’s micro decisions with the firm’s objec-
tives. One mechanism that is often mentioned is monetary incentives, such as
transfer payments within an organization (Mihm, 2010) or a codevelopment con-
tract between collaborative organizations (Bhaskaran & Krishnan, 2009; Savva &
Scholtes, 2014). In a general setting, Mihm (2010) shows that by the revelation
principle, an optimal Bayesian contract can be constructed between a principal
(firm) and multiple agents (engineers) such that all engineer-agents with private
information will behave more in line with the firm’s interests. Another mechanism
is performance measurement (Hutchison-Krupat & Kavadias, 2013; Mihm, 2010;
Siemsen, 2008). This stream of literature primarily aims to tackle the challenge of
managing agents’ nonverifiable efforts in complex organization by awarding (pe-
nalizing) individual employee actions that are aligned (misaligned) with a firm’s
overall task priority. For example, Mihm (2010) shows the value of target cost-
ing in aligning design engineers’ individual objectives with the firm’s objectives.
Hutchison-Krupat and Kavadias (2013) studies the conditions in which offering
performance-based contracts is beneficial to organizations. Our study takes a dif-
ferent approach from that of previous studies set forth above. We abstract out
the challenges of nonverifiable efforts in cross-functional teams and instead focus
on examining the impact of the team’s decision-making structure on the team’s
decision outcome and the magnitude of misalignment.i

Recently, a stream of NPD literature has emerged to account for the hierarchi-
cal nature of organizational decision making (Chao & Kavadias, 2008; Hutchison-
Krupat & Kavadias, 2013; Mihm, Loch, Wilkinson, & Huberman, 2010). These
studies primarily consider the vertical distribution of decision power between the
principal (firm) and its agent (the division). For example, Hutchison-Krupat and

i In the following section, we provide more discussions on how monetary incentives could be implemented
for alignment.

496 Decision Making in Cross-Functional Teams

Kavadias (2013) compares the efficacy of a decision-making process that dictates
resource levels (top down) with that of one that delegates resource decisions and
relinquishes control (bottom up). In their setting, the tradeoff that a principal (firm)
must make is whether to delegate decision power to the division in exchange for
more precise information or to retain decision power to achieve more efficient
resource planning. Our study differs from Hutchison-Krupat and Kavadias (2014)
in two important aspects. First, motivated by the BMW challenge (Pisano, 2002),
the firm is assumed to have a better vision of “what should be done” than the team
members in our study; by contrast, in Hutchison-Krupat and Kavadias (2013), the
agent (division) has more precise information than the principal (firm). Therefore,
our study aims to reduce the misalignment between a cross-functional team’s mi-
cro decision and the firm’s macro objectives via different organizational levers,
whereas Hutchison-Krupat and Kavadias (2013) aims to show when it is optimal
to implement a “top-down” vs. “bottom-up” strategy. Second, in our models, de-
cision power is horizontally distributed among team members in a team, whereas
in Hutchison-Krupat and Kavadias (2013), decision power is vertically distributed
from the headquarters to the divisional level.

Our study is also related to, yet different from, the economics literature on
decision rights (Aghion & Tirole, 1997; Grossman & Hart, 1986; Hart & Moore,
1990). In our study, the members of cross-functional teams do not have the right of
ownership (e.g., Grossman and Hart, 1986; Hart and Moore, 1990). Instead, team
members can obtain decision power through personal attributes or participation
in specific stages of the decision-making process. Therefore, our study implicitly
models team members’ real authority in the organization, whereas in Aghion
and Tirole (1997), an agent’s incentive to invest in information gathering is only
affected by his or her formal authority.

Finally, our study contributes to the literature on strategy implementation.
Similarly to ours, studies in this stream of literature suggest that a particular type
of “alignment” or “congruence” within an organization is essential to a firm’s
success (we refer readers to Yang, Sun, and Eppler (2010) for a comprehensive
review of this literature stream). To implement a strategy, firms must also use
several tactics, such as persuasion, teamwork, negotiation, goal commonality, and
total quality management (Nutt, 1989). However, these tactics are inevitably ad
hoc, and their applicability heavily depends on managers’ personal characteristics
and management skills (Nutt, 1989; Sashittal & Wilemon, 1996). By contrast, our
study presents a game-theoretical framework to show how a change in a team’s
decision-making structure can systematically alter the team’s decision outcome.

MODEL SETUPS AND SOURCES OF DECISION POWER

In this section, we formulate a simple and intuitive game-theoretical model to
capture the decision-making process in a cross-functional team. First, we describe
the decision value as a single dimensional space f, where f � 0. In the context of
NPD, the decision to be made, for example, the product feature parameter, can be
a multiple-dimensional space (Srinivasan, Lovejoy, & Beach, 1997), as a product
usually contains multiple features, such as weight, size, shape, manufacturing cost,
the number of functionalities, etc. However, these features are rarely independent

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of each other. For example, increasing product size creates extra space in which
to install additional functionalities, which, in turn, increases the product’s weight
and manufacturing cost. Thus, given a fixed R&D budget, the decision-making in
NPD also involves tradeoffs between different performance requirements (Ulrich
& Eppinger, 2003). We can therefore collapse a multi-dimensional decision space
into a critical, one-dimensional decision value, and the decision values of other
dimensions can be simply mapped as a function of this critical decision. This
method is commonly applied within the NPD literature (e.g., Terwiesch & Loch,
2004; Lacourbe, Loch, & Kavadias, 2009; Williams, Kannan, & Azarm, 2006).

Second, the cross-functional team in our study is composed of two parties,
and each party has one specific team responsibility. For example, one party is in
charge of collecting vital information about market needs, whereas another creates
a detailed product design based on technology feasibility and customer needs.
In contrast to the situation of firms involved in inter-firm collaborations, team
members must share a fundamental common interest (because they belong to the
same team), but they have different preferences because of their access to different
information (Groves, 1973). Thus, the two parties have symmetric utility functions
(reflecting a fundamental common interest) but different optimal solutions (i.e.,
the utility functions shift relative to each other).

To echo the Nokia example mentioned above, the tension between two parties
is assumed to arise from the budget allocation for developing hardware. In partic-
ular, one party (we refer to it as party A) believes that the quality of a product’s
hardware performance is vital for market success and thus favors an R&D budget
that is primarily allocated to hardware development. In contrast, another party (we
refer to it as party B) believes that software performance plays a more important
role than hardware performance in product success. Thus, party B favors a budget
that allocates a greater proportion of funds to software development. To focus on
examining the impact of decision power, our model abstracts the team members’
moral hazard effect. In other words, the team members honestly use the budget to
develop the product only. Therefore, there is a one-to-one mapping between the
budget and the realized product performance (i.e., a higher budget for software
development leads to higher software performance, and vice versa). Therefore,
the expected utility of each party can be transformed into a function of the single
decision value f, and we let each party’s utility function be a quadratic function of
f ii. Therefore, the payoffs of the two parties can be described as follows:∏

A

= P − a
(
f − f ∗A

)2
. (1)

∏
B

= P − a
(
f − f ∗B

)2
. (2)

ii In the literature of NPD and design engineering, the quadratic utility function has been widely used
(Bhaskaran and Krishnan, 2009; Williams et al., 2006). In fact, we can show that the shifted quadratic
utility representation is quite general. Specifically, it can capture both a vertical (which drives costs) and
a horizontal product feature (which is cost-neutral, such as color). The formal analysis is available upon
request.

498 Decision Making in Cross-Functional Teams

Figure 1: Firms’ and team members’ optimal decision values.

Here, P denotes the maximum payoff potential from making the decision,
and f ∗

i
denotes each party’s perceived “optimal” decision value that maximizes

the expected payoff. Similarly, the firm’s payoff function also takes a quadratic
form, and it can be described as follows:∏

F

= P − a
(
f − f ∗

)2
. (3)

where f� denotes the truly “optimal” decision value that maximizes the firm’s
expected payoff.

The term a captures the sensitivity of the decision’s payoff with respect to
the decision value. For simplicity, we let a =1 throughout this study. To exclude
the trivial case, let f ∗ �= f ∗

A
�= f ∗

B
. Without a loss of generality, let f ∗

A
> f ∗

B
. The

utility functions of the firm and the two team members are plotted in Figure 1.
One may argue that the sharp focus on the effect of each party’s utility

on software and hardware performance may seem limiting because both utilities
usually depend on both hardware and software. Thus, alternatively, we consider
the case in which both parties have weight in affecting software and hardware
performance such that their utilities are jointly concave for both software and
hardware performance. Note that this model formulation encompasses cases of
convex combinations of party A and B’s utility. With a few additional minor
technical assumptions, all of the results in this paper can be replicated. Thus,
our results are robust to relatively drastic changes in model formulations.In an
organization, stakeholders can gain decision power from two sources: (1) personal
attributes, such as reputation, social status, and experience (Mintzberg, 1979;
Pfeffer, 1992); and (2) structural sources, such as a stakeholder’s participation in
the decision-making process (Atuahene-Gima & Evangelist, 2000; Pfeffer, 1992).
To quantify the decision power that arises from their personal attributes, we borrow
a method from bargaining theory (Muthoo, 2002) and define party A (B) to have
an outside option or fallback utility uA (uB ). Higher ui implies stronger personal
attributes and therefore more decision power (Pfeffer, 1992). In cross-functional
teams, a team member’s outside option denotes his or her potential payoffs from
other projects in the project portfolio (Chao & Kavadias, 2008) as he or she releases
resources for the focal project.

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To account for decision power that stems from structural sources, we must
examine in detail the role played by a team member in the decision-making process.
In the context of supply chain management, the Stackelberg game is a well-
established approach to model leader-follower competition between firms (Li &
Atkins, 2002). In the context of cross-functional teams, however, the Stackelberg
game is not a suitable model to represent a team leader’s decision power. First, in
a Stackelberg game, the “leader’s” decision power solely results from his or her
first-mover advantage, and the follower’s (i.e., second mover’s) action can only be
the best response to the first mover’s action. In cross-functional teams, however,
a team leader may not always move first, and his or her power may instead arise
from the participation in a critical stage of decision making. Second, parties in
a Stackelberg game make individual decisions to maximize their own payoffs,
whereas in cross-functional teams, all members are bounded by one common team
decision, which can be a joint decision agreed upon by all parties or a decision
made and enforced by the team leader. Therefore, the unique nature of decision
making in cross-functional teams requires a different modeling approach.

According to the seminal work by Mintzberg (1979), a decision-making
process can be described as a sequence of stages that includes collecting infor-
mation, processing that information to present advice, choosing what is to be
implemented, and authorizing the decision, which may mean vetoing the proposed
choice. We refer to the resulting types of decision power, which depend on the
control over these various stages, as information, initiation or advice, veto, and
choice power. Among the four types of decision power that arise from participa-
tion in the decision-making process, choice power differentiates a leader from a
follower (Mintzberg, 1979). In cross-functional teams, information and initiation
power cannot be treated in isolation because initiation (advice) power is futile
when it is accorded to a party who is not aware of critical information. A team
member could also raise objections to or “veto” another member’s plans and thus
“turn down” a proposal that counters his or her preference. In this study, we let
party A (B) be the team leader (follower). The team leader (party A) owns choice
power and may also own three other types of decision power depending on the
participation of the team follower (party B).

In the following sections, we examine the impact of three organization-
specific factors on a team’s decision outcome and the magnitude of misalignment
with the firm’s interest: (1) the discrepancy between the preferences of team
members, (2) decision power that arises from their personal attributes, and (3)
decision power that arises from participation in the decision-making process.

THE ROLE OF TEAM MEMBERS’ PREFERENCES IN DECISION
MAKING

To facilitate the analysis, we first define several notations. Appendix A summarizes
all of this study’s assumptions and parameters. Define for party i of type t, where
i � {A,B} and t denote the party’s optimal decision value f t∗

i
, the set of decision

values �t
i

that the party prefers to receive his or her outside option, i.e., �t
i

defines
a range in which potential mutual agreement is acceptable for party i, which we

500 Decision Making in Cross-Functional Teams

Figure 2: Compromise potential for party B of type t when preference varies.

term “party i’s compromise potential.” Figure 2 illustrates party B’s compromise
potential �t

B
, where party B’s preference can B only two values (t � {A,B}). The

limit f t
B

is the right endpoint of �t
B

which represents the “best” decision value for
party A from party B’s compromise potential.

Using the simple formulations of our model, we can immediately categorize
two extreme scenarios. First, when the preferences of team members are “close”
enough (for example, team leader A’s optimal

decision value f ∗
A

lies within team follower B’s compromise potential �t
B

),
the team leader can simply assert his or her preferred design (f ∗

A
) and knows that

the team follower (party B) will always accept. In this case, the decision value
will be set at f ∗

A
, and the magnitude of misalignment is | f ∗

A
−f ∗|. By contrast,

when the parties’ preferences are too divergent such that the team leader will
receive less of a payoff from working on the project than from pursuing his or
her outside option (P − (f t

B
− f ∗

A
)2 < uA), the team leader would rather abandon the project or at least postpone making any team decisions even though such an outcome is “unwanted” from the firm’s perspective. In this case, firms could consider using traditional project management methods, such as organizing team- building training sessions or job rotations to align team members’ preferences (mathematically equivalent to minimizing | f t j − f ∗ i |, where j �{A,B} and j �i ). Between these two extreme scenarios, we define the third case, in which party i’s most favorable decision value f ∗ i is not acceptable to party j, and thus, bargaining is indeed necessary (we call this condition the “no easy win-win” condition) in which a mutually acceptable solution could be found, given that party i has outside option ui (we call this condition the “common ground” condition). In this scenario, one general conclusion immediately follows: Lemma 1: A set of decision values always exists such that both parties prefer any one of these decisions to receiving an outside option ui when two conditions hold: Condition1 (no easy win − win) : f ∗i /∈ …