Testing Strategy Formulation and Implementation Using Strategically Linked
Performance Measures
Dennis Campbell, Srikant Datar, Susan L. Kulp, and V.G. Narayanan*
Harvard Business School
Current Draft: December 2006
ABSTRACT: This study investigates whether strategically linked performance measures reveal
information about the quality of a firm’s business strategy. The strategy literature describes business
strategies using the concepts of formulation, implementation, and fit. The management accounting
literature links these strategy concepts with the selection and use of performance measures. Building on
these two streams we examine whether and how the performance measurement system can be used to
distinguish between formulation, implementation, and fit problems. We analyze balanced scorecard data
from a field-site which formulated, implemented, and subsequently abandoned an innovative operating
strategy. Managers learned the strategy was ineffective over a two year period. We find that the
company’s strategically linked performance measures systematically reveal more timely information
about problems with the strategy. Furthermore, the performance measures distinguish between problems
with strategy formulation, implementation, and fit. The results are consistent with a well implemented,
but poorly formulated, strategy at the research site. Additionally, the results imply a poor fit between the
strategy and the firm’s internal resources. These results provide evidence that strategically linked,
balanced scorecard measures can be used (1) to evaluate the strategy promptly and (2) to distinguish
between strategy formulation, implementation, and fit problems.
I.
INTRODUCTION
Management control theories argue that performance measurement systems consisting of
financial and non-financial metrics linked to the firm’s unique strategy should facilitate learning through
testing, validating, and revising the hypothesized relationships that describe the strategy (e.g., Eccles
1991; Kaplan and Norton 1996, 2000; Ittner and Larcker 2005; Julian and Scifres 2002; Shreyogg and
Steinmann 1987).
For example, Kaplan and Norton (1996) contend that balanced scorecards give
decision makers the ability to detect whether the company’s strategy is working or failing. We examine
this idea by empirically investigating how specific information about the quality of a firm’s business
strategy is revealed in strategically linked performance measures of a balanced scorecard (BSC).
We conduct an ex-post audit of strategy outcome, strategy implementation, employee capability,
and financial performance measures of Store24, a New England convenience store chain. In FY 1998
Store24 initiated a new store-level strategy to differentiate itself by improving customer experiences.
*
The authors thank Store24 for use of its data. We thank Chris Ittner, Robert Kaplan, Ken Koga, Michael Maher,
Joan Luft, Tatiana Sandino, Philip Stocken, Dan Weiss, two anonymous referees, and seminar participants at the
AAA Annual Meeting in Orlando, Boston University, the EIASM conference, Harvard University, Management
Accounting Section Mid-year Meeting in San Diego, Michigan State University, Ohio State University, University
of Arizona, University of Michigan, and University of Southern California for their helpful comments and
suggestions.
1
There was, however, significant variation in how much and how well individual stores executed against
Store24's implementation plan, in how customers valued this strategy, and in financial performance across
stores. Based on customer feedback during the next two years, Store24 reverted back to a traditional
strategy that emphasized speed of service and operational efficiency.
Store24 monitored store
performance via a set of performance measures formulated in a BSC.
This site provides an ideal setting to offer empirical evidence on the extent to which strategically
linked performance measures reveal specific information about a firm’s business strategy. In particular,
we are able to benchmark the information revealed in analyses of the relationships among the firm’s
performance measures against field-based evidence on the actual problems discovered by management
over subsequent time periods.
The strategy literature identifies formulation as the ends (objectives and goals) and
implementation as the means (action plans and allocation of resources) of the strategy (Snow and
Hambrick 1980). The management accounting literature on strategic control systems links these concepts
of strategy formulation and implementation with the selection of performance measures. In particular, the
BSC framework advocates choosing performance metrics related to key financial and customer
objectives, the firm's internal processes for achieving these objectives, and organizational capabilities
necessary to execute its internal processes. Moreover, performance measures should be explicitly linked
in hypothesized "cause-and-effect" relationships that depict the firm's strategy (Kaplan and Norton 1996;
2004). Improvements in measures of organizational capabilities are expected to drive improvements in
the execution of internal processes which in turn lead to customer and financial outcomes. Thus, the BSC
framework explicitly recognizes interrelationships between strategy-specific measures of financial and
customer outcomes and input-oriented "performance drivers" related to the firm's internal processes and
organizational capabilities.
Managers formulate specific strategies based on ex-ante expectations about how the strategy will
translate into organizational objectives (e.g., increased profitability). Moreover, managers translate these
2
action plans into internal processes that will implement the formulated strategy.1 We conceptualize the
quality of strategy formulation as the marginal effect of increases in strategy specific customer outcome
measures on the firm’s financial objectives. We view the quality of strategy implementation as the
marginal effect of increases in input-oriented internal process measures on the firm’s strategy specific
customer outcome measures.
A performance measure related to internal processes may not be a leading
indicator of financial performance if (1) the action plan chosen to implement the strategy, as represented
by input-oriented internal process measures, does not improve specific strategic customer outcomes (poor
strategy implementation) or (2) the formulated strategy improves customer outcomes, but does not deliver
expected financial outcomes (poor strategy formulation).
We use the framework described in the preceding paragraphs to illustrate how strategically linked
performance measures in Store 24’s BSC can be used to systematically reveal information about problems
with the firm’s strategy.
Store24’s performance measurement system contained information to
differentiate between poor strategy formulation and poor strategy implementation.
As part of the
customer perspective, Store24 management measured the extent to which individual stores provided an
entertaining experience (i.e., a strategy-specific customer outcome measure). Store24 management also
developed a store-level action plan to implement this strategy, mapped the action plan into operating
standards, and measured store-level conformance with these standards as part of its BSC internal process
perspective (i.e. a strategy-specific input measure). Thus, all stores worked on executing against these
operating standards to implement the new strategy. There was, however, significant variation in how well
the strategy was implemented in different stores and in how customers experienced the implementation.
Measures of unique internal processes (hereafter, 'input measures') are positively related to strategyspecific customer outcome metrics (hereafter, 'outcome measures') while outcome measures are
negatively related to financial performance. The results are consistent with a well implemented, but
poorly formulated, strategy at our research site.
1
For a framework articulating the interrelationships among the choice of strategic objectives, action plans, and
performance measures, see Ittner and Larcker (2001).
3
Theory from the strategy literature suggests that problems with a particular business strategy may
arise due to lack of "fit" with internal resources such as employee capabilities (Amit and Schoemaker
1993; Dierickx and Cool 1989). In this paper, strategic fit with internal resources is conceptualized in
three ways. First, the marginal effect of increases in measures of a firm’s internal capabilities on strategyspecific input measures captures the extent to which the firm's internal capabilities drive its ability to
execute its internal processes. Second, the marginal effect of measures of internal capabilities on the
quality of strategy implementation captures complementarities between the firm's internal capabilities and
the processes it uses to satisfy customers. Third, the marginal effect of measures of internal capabilities
on the quality of strategy formulation captures complementarities between the firm’s internal capabilities
and its chosen strategy.
Our results indicate that cross-sectional differences in store capabilities account for differences in
the success of Store24’s strategy.
Low employee skill levels do not directly affect strategy
implementation. But in stores with low employee skills, even when outcome measures are high, financial
performance is poor. Conversely, in stores with high employee skills, when outcome measures are high,
financial performance is strong. These results are consistent with a "poor fit" hypothesis in which
regardless of how thoroughly Store24 implements its strategy, for the strategy to succeed, store level
employee capabilities need to be high.
Our study makes three contributions to the accounting literature on performance measurement.
First, we describe and illustrate a method to use performance measurement systems to analyze and
evaluate strategy implementation and formulation.
Several studies in management accounting
demonstrate relationships among financial performance metrics and non-financial measures such as
product quality and customer satisfaction (e.g., Banker, et. al. 2001; Ittner and Larcker 1998b; Nagar and
Rajan 2001).
However, these studies do not explicitly analyze measures of a firm’s strategy and
capabilities and, consequently, the extent to which such measures provide information useful for timely
detection of problems with the strategy. In our study, contrary to prior studies, improvements in outcome
4
measures are negatively (or not) related to a variety of financial measures because the strategy, though
well-implemented, is poorly formulated.
Second, despite the academic evidence that non-financial performance measures typically lead
financial performance, Ittner and Larcker (1998b) document that many executives do not tie together
firm-specific non-financial metrics with lagging accounting measures.2
Our paper shows that the
relationships between non-financial performance measures and financial performance depend on
characteristics of the strategy captured by those measures. A lack of a relationship between firm-specific
non-financial metrics and accounting returns may be informative about (1) the firm’s strategy
formulation, (2) its strategy implementation, and (3) the effect of a firm's internal capabilities on strategy
implementation or the fit of the formulated strategy with the firm’s internal capabilities. We provide
some of the first field-based empirical evidence on the potential for a set of strategically linked financial
and non-financial performance measures to distinguish among these three alternatives.
Third, we extend prior research on the relationships between non-financial performance measures
and financial performance by examining the potential moderating effect of employee capabilities. Prior
research suggests that business models are typically depicted by linear relationships between financial and
non-financial performance metrics (Rucci et al. 1998, Kaplan and Norton 1996; 2000). Except for Ittner
and Larcker (1998b), prior empirical work typically ignores potential nonlinearities in relationships
among performance measures. Moreover, these studies do not examine interactions among non-financial
performance measures as a source of nonlinearity that may moderate these relationships (Ittner and
Larcker 1998a).
The results in this paper are subject to the caveat that the field-based nature of our research limits
the generalizability of our findings. However, the unique nature of a firm’s strategy dictates that the
performance measures and links between these measures, articulated in the firm’s business model, are
likely to be firm-specific. Future research should provide additional evidence from other settings of the
extent to which business model-based performance measurement systems capture information useful for
2
Consistent with this, Store24 management did not perform statistical analyses linking the performance measures
together, although the metrics were consistently collected across stores and across time.
5
monitoring strategic progress. Our main contribution is to describe a method that can be generally
applied to other settings and industries to isolate the effects of strategy formulation, strategy
implementation, and strategic fit.
The remainder of the paper proceeds as follows. In section II we discuss prior literature, and in
Section III we develop hypotheses. Sections IV and V present our research site and empirical research
design. Results are presented in section VI. We conclude the paper in section VII.
II.
THE LINK BETWEEN THE STRATEGY LITERATURE AND STRATEGIC
CONTROL SYSTEMS FRAMEWORKS
In this section, we discuss links among the notions of strategy formulation, implementation, and
fit with internal resources found in the strategy literature and emerging frameworks of strategic
performance measurement found in the management accounting literature.
Strategy Formulation vs. Strategy Implementation
There does not appear to be clear consensus on the definitions of strategy formulation and
strategy implementation within the strategy literature. However, several conceptual papers distinguish
these concepts based on the choice of strategic objectives and the choice of action plans to achieve those
objectives, respectively. Notably, Andrews (1971) put forth a general definition of strategy as:
… a pattern of major objectives, purposes, or goals and essential policies and
plans for achieving those goals, stated in such a way as to define what business
the company is in or is to be in and the kind of company it is or is to be.
Andrews' definition explicitly identifies two separate processes, formulation and implementation, and the
interrelation between these two concepts (Sloan 2005). Similarly, Chandler (1962) refers to strategy as
"… the determination of the basic long-term goals and objectives of the enterprise and the adoption of
courses of action and allocation of resources necessary for carrying out those goals." As with Andrews,
this definition of strategy distinguishes between formulation and implementation by encompassing both
elements of ends (goals and objectives) and means (courses of action and allocation of resources).
Subsequent strategy researchers continue the dichotomy between choosing strategic objectives (strategy
formulation) and detailing action plans to achieve those objectives (strategy implementation) (Snow and
Hambrick 1980).
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Strategic Fit with Internal Resources and Capabilities
More recent research introduces a strategy’s "fit" with the firm's internal resources and
capabilities. The resource-based view of the firm (RBV) posits that an organization’s unique, valuable,
and difficult to replicate resources and capabilities form the basis for sustainable competitive advantage
(Amit and Schoemaker 1993; Dierickx and Cool 1989).3 Others (e.g., Itami and Roehl 1987; Dierickx
and Cool 1989; Nanda 1996, Hitt et al. 2001) classify resources such as brand name, customer loyalty,
technical know-how, firm-specific human capital, and employee skills as strategic. The RBV concept of
interconnectedness of asset stocks (Dierickx and Cool 1989) posits complementarities among
accumulations of various “invisible assets” or resources such as human capital. This literature focuses on
the role of strategic resources and capabilities in successful strategy formulation and implementation.
Strategic Control Systems and the Balanced Scorecard
Recent management accounting research incorporates these strategy frameworks by articulating
linkages between performance measure choice, strategy formulation, and strategy implementation. The
value-based management framework (Ittner and Larcker 2001) emphasizes interrelationships among the
choices of strategic objectives, action plans, and performance measures. Proponents of strategic or
“business model” based performance measurement systems advocate formulating performance
measurement systems around a diverse set of financial and non-financial performance metrics linked to
the firm’s unique strategy (e.g., Eccles 1991; Kaplan and Norton 1996).
The literature on management control systems has long argued that one role of control and
performance measurement systems is the facilitation of strategic feedback and learning (Ittner and
Larcker 2005), This literature echoes the basic means-ends concepts found in the strategy literature by
emphasizing strategic feedback and learning as a process of systematically using data generated by the
firm's control systems to evaluate strategic plans, activities, and ultimately, results (Schreyogg and
Steinmann 1987; Julian and Scifres 2002). Similarly, in settings where there is uncertainty over the firm's
"profit drivers", Dye (2004) demonstrates that performance measurement systems consisting of
3
The RBV explains cross-sectional differences in strategy choices and outcomes. Related insights apply to Store24,
a decentralized company in which stores are heterogeneous with respect to demographics and employee capabilities.
7
"intermediate" measures of firm processes facilitate experimentation in that they allow managers to
determine which of several underlying processes are most strongly linked to future profitability.
Kaplan and Norton's (1996; 2004) BSC framework is perhaps most explicit in advocating that
performance measures be chosen based on hypothesized relationships between measures of financial
objectives and unique measures of nonfinancial "performance drivers".
This framework can be
conceptualized as consisting of strategic outcome metrics in the financial and customer perspectives and
strategic input metrics in the internal process and learning and growth perspectives. These measures
should be explicitly linked in a series of hypothesized "cause-and-effect" relationships that represent the
firm's strategy (Kaplan and Norton 1996; 2004). With its emphasis on intangible assets (e.g., employee
capabilities) as the basis for successful strategy implementation, the BSC framework directly parallels the
notion of "fit" from the strategy literature.
III.
A FRAMEWORK FOR STRATEGIC HYPOTHESIS TESTING
In this section, we construct a set of general hypotheses guided by the literature in strategy and
strategic performance measurement. We describe how to distinguish among problems with strategy
formulation, strategy implementation, and strategic fit.
Managers formulate strategies based on ex-ante expectations about how the strategy will translate
into organizational objectives (e.g., customer satisfaction or profitability). Moreover, managers develop
action plans to implement the strategy and detail the internal processes needed to achieve the stated
objectives. Consider the relationship between a performance outcome measure, PO , such as profit and
strategy input measures, S I , related to the firm's internal processes for achieving its strategic objectives:
PO = f ( S I , ε P )
where ε P represents factors that affect performance other than S I .
effectiveness of internal processes by examining
Managers can evaluate the
∂PO
. Problems with the strategy as formulated and
∂S I
8
implemented are revealed unambiguously if
∂PO
≤ 0 .4 This suggests the following straightforward
∂S I
hypothesis, stated in null form, as a starting point for evaluating the performance of a given strategy.
H10: Ceteris Paribus strategy inputs are positively related to financial performance.
Figure 1 summarizes this and subsequent hypotheses. H10 will be rejected if the input metrics show no
(or a negative) relationship with financial performance. This may be caused by two reasons: (1) the
action plan and internal processes chosen to implement the strategy do not result in the achievement of
strategy-specific objectives or (2) the formulated strategy does not deliver expected returns, that is,
achieving the chosen strategic objectives does not result in superior financial performance.
Distinguishing between problems in the strategy formulation (e.g. choice of strategy-specific objectives)
and problems with strategy implementation (e.g. choice of action plans to achieve strategy-specific
objectives) would be possible if an intermediate strategy-specific customer outcome metric, SO , were
available.5 In this case, we have
PO = g ( SO , ε SO )
SO = h( S I , ε SI )
where ε SO and ε SI represent factors that affect performance other than SO and S I , respectively.
Problems with the strategy as formulated and implemented would be revealed unambiguously if
∂PO ∂PO ∂SO
∂P
∂S
∂P
∂S
=
×
≤ 0 .6 This occurs if either: (1) O ≤ 0 and O > 0 or (2) O > 0 and O ≤ 0 .
∂S I ∂SO ∂S I
∂SO
∂S I
∂SO
∂S I
Case 1 is consistent with good implementation but poor formulation of strategy. Input measures (unique
internal processes chosen to implement a strategy) are positively related to customer outcomes, but these
customer outcomes are not positively related to the firm's overall financial performance objectives. The
4
5
We assume throughout that higher values of Si indicate better performance.
The performance outcome PO is distinct from the strategy-specific customer outcome SO . PO represents a high-
level performance objective such as profit. SO represents a strategy-specific objective such as customer experience
or satisfaction with unique product or service attributes.
Note that firms often derive a measure of expected performance. In such cases, the strategy’s performance would
be evaluated relative to this target, rather than relative to zero.
6
9
second case is consistent with poor implementation but good formulation of strategy. Input measures are
not positively related to customer outcomes, but the customer outcomes are positively related to the firm's
overall financial performance objectives. In a BSC framework with strategically linked input- and
output-oriented metrics, this suggests the following hypotheses for evaluating whether observed problems
with a given strategy’s performance is due to poor implementation or poor formulation:
H20: Ceteris Paribus strategy inputs are positively related to strategy-specific customer
outcomes.
H30: Ceteris Paribus strategy-specific customer outcomes are positively related to
financial performance.7
H20 or H30 could be rejected if the given strategy requires the presence of complementary
intangible assets to succeed. The RBV literature suggests that returns to formulating and implementing a
strategy may depend on the level of complementary strategic resources. Much of this literature argues
that specialized complementary resources provide the basis for sustainable competitive advantage (Teece
1986; Tripsas, 1997).
Empirical research in this area demonstrates that strategic resources, such as
human capital, interact with strategy inputs and strategy outcomes to affect performance (Hitt et. al.
2001). That is, the marginal effects of customer outcomes on financial performance (quality of strategy
formulation) and input measures on customer outcomes (quality of strategy implementation) are
determined by whether the level of a complementary strategic resource is below the level necessary for
positive returns to the formulated strategy. We refer to these strategic resources as internal capabilities
and focus on manager and employee skills. Thus, we have the following hypotheses for evaluating
whether observed problems in strategy formulation and strategy implementation are due to poor fit with
internal capabilities:
H40: Ceteris Paribus the marginal impact of increases in strategy inputs on strategyspecific customer outcomes is positively related to the level of internal capabilities.
7
Our framework could also be used to identify cases where unique internal processes chosen to implement strategy
are negatively related to strategy-specific outcomes and strategy-specific outcomes are negatively related to overall
performance objectives. In this case, unique internal processes chosen to implement strategy are positively related
to overall performance objectives, but the detailed analysis would highlight problems of poor implementation and
poor formulation that make such a strategy unsustainable.
10
H50: Ceteris Paribus the marginal impact of increases in strategy-specific customer
outcomes on financial performance is positively related to the level of internal
capabilities.
Thus far, the hypotheses have focused on identifying problems with strategy formulation,
implementation, and fit from the perspective of senior management. It is at this level where strategic
objectives are chosen and strategy inputs (unique internal processes) for implementing those objectives
are selected. However, problems of strategic "fit" may also arise if a company's internal capabilities do
not allow it to achieve the desired strategic inputs needed to successfully execute the implementation
plan, suggesting the following straightforward hypothesis.
H60: Ceteris Paribus internal capabilities are positively related to strategy inputs.
IV.
RESEARCH SITE
Store24 is a privately held convenience store retailer in New England, the 4th largest in the region.
Its stores, located through Massachusetts, New Hampshire, Rhode Island, and Connecticut, are grouped
into nine geographic divisions, each with its own division manager. Stores are homogenous in many
aspects of their operations including compensation, technology, management structure, and product
pricing, but they vary in size, geographic location, market demographics, and product mix.
The company’s primary product categories include cigarettes, beverages, snacks, prepared foods,
and lottery tickets. Revenues totaled approximately $180 million in fiscal year 1998 (May 1, 1998 to
April 30, 1999). Store24 employed 800 people including 740 store managers and crew and 60 corporate
level employees. The skills and experience of these employees vary widely overall and across stores.
Store24 operates in a mature environment with competition from convenience stores, gasoline
retailers, and drug stores. Traditionally, convenience store retailing focused on short-term productivity
(e.g., inventory and cash control). As the convenience store industry matured and competition intensified,
marketing, customer service, and brand name emerged as differentiating factors. Before FY 1998 and
after FY 1999, Store24 did not differentiate itself; rather it focused on excelling at traditional service
quality metrics such as physical environment (cleanliness and store layout) and quality of the customer
experience (fast, friendly service) (Fitzsimmons and Fitzsimmons, 2001).
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During FYs 1998 and 1999 (that is from May 1, 1998 to April 30, 2000), Store24 formulated a
strategy aimed at increasing same-store sales and margins because growing via new sites was difficult.
“Location is a primary driver of store performance. However, we are stymied on the growth front due to
a lack of acceptable new sites. This has led to a focus on optimizing our existing sites through an
increasing emphasis on store-level marketing and operations,” explained Store24’s CFO. To achieve its
goals, Store24 changed its strategy to creating entertaining in-store atmospheres that would differentiate
its stores from those of competitors.
The Differentiation Strategy
Store24 implemented this new, innovative store-level strategy during the first quarter of FY 1998
(i.e., beginning May 1, 1998). It aimed to differentiate its stores while maintaining performance on
traditional productivity measures. Successful retailers, such as Disney stores, offer “fun and interactive”
shopping experiences. Store24’s CEO believed that adopting a similar strategy would improve financial
performance. Store24 provided a fun in-store atmosphere by emphasizing specific themes.
Store-level strategy execution centered on a large display case (i.e., “endcap”) featuring themeoriented promotional items and store decorations that fostered employee interaction with customers. For
example, during the old movie theme stores featured life-size cutouts of movie stars, endcaps contained
high-margin videos of old movies, and old movies became a conversation piece. The themes sought to
attract urban adults between the ages of 14 and 29 years, a growing market segment and Store24’s target
market. A senior manager explained, “The [Differentiation] strategy was really playing off of the urban,
young adult market. Marketers know that this demographic gets bored easily and needs to be stimulated.
We wanted this group to always see new and different things in the store.”
In contrast to the basic service quality component, store-managers were accorded autonomy in
implementing the differentiation strategy. That is, although all stores were required to implement the new
strategy, how they implemented or how much they implemented varied across stores. Corporate defined
a theme and provided the endcaps, but store employees possessed considerable flexibility in strategy
execution. Thus, manager and crew skills were at least as important as theme choice to the strategy’s
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success. Store24’s controller explained, “Our best managers really took the strategy to heart. The
strategy served as an outlet for manager and crew creativity. However, other managers put minimal effort
into this strategy and even stocked traditional items such as chips on the endcaps saying they needed the
product space.”
The differentiation strategy, as originally conceived, centered on the physical environment. But
the interaction between store employees and customers was crucial to the strategy’s success. Senior
management intended the themes and promotions to serve as points of interaction that would help Store24
establish relationships with customers and cross-sell high margin products. Explained a senior executive,
“The endcaps and displays under the [differentiation strategy] had the dual intention of building a rapport
with customers and bumping up the average sales per customer. We felt that store management and crew
could use the displays as “ice-breakers” in talking with customers. In addition, the margins on the
promotional items featured under the [differentiation] strategy were typically two to four times the
margins of our traditional products.
When customers were browsing or “window shopping” we
encouraged store crew to direct the customer’s attention to these promotional items.” Store24 looked to
its differentiation strategy to attract new customers and increase store sales, specifically, sales of highermargin, strategy-specific products, and thereby boost store profits.
Performance Measurement System
Store24 used a balanced scorecard-based performance measurement system.
The company
collected information on a variety of performance measures at various levels of the organization and at
various frequencies. Management collected store-level financial performance metrics quarterly.
It monitored store-level customer measures less frequently. Between the 1st and 4th quarters of
FY 1999, an external research firm solicited feedback from customers at 65 stores about Store24, its
product selection, and other factors that would persuade them to shop at Store24 more often. Customers
ranked unique attributes related to the differentiation strategy that they found appealing; among these was
“fun place to shop,” “entertaining,” and “unexpected.” Additionally, the research firm conducted semiannual telephone surveys of self-identified convenience store customers in Store24’s major markets to
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assess the likelihood of customers shopping at Store24, name recognition of Store24, and, for Store24
customers, the quality of merchandise, price, and store cleanliness.
Store24 translated the components of its strategy into a set of store-level operating standards and
measured store-level conformance to these standards via walk-through audits conducted twice per quarter.
During these announced visits management evaluated store performance on various dimensions including
in-store image, in-stock position, and store appearance. The walk-through audit score quantified the
store-level implementation of Store24’s operating strategy. For FYs 1998 and 1999, the standards
reflected both the differentiation strategy as well as traditional service quality metrics.
A store’s
differentiation score referred to a separate measure of conformance to only standards related to the
differentiation strategy such as actions in terms of themes and products that would make Store24 a fun
and entertaining place to shop. Store24 also measured conformance to store-level operating standards
through monthly surprise visits or “mystery shops.” The mystery shop review, which consisted of twenty
high-level questions, helped to ensure the validity of the walk-through audit scores. Scores on the
announced and unannounced visits are significantly and positively correlated.
Senior and division management considered employee skills critical to consistent implementation
of the store-level operating strategy. Accordingly, Store24 measured manager and crew skills through biannual evaluations of performance in guest interactions, merchandising, machinery maintenance, store
condition, adherence to policy, loss prevention, and problem solving.
Store manager and crew
compensation was tied to, for example, store-level profit and strategy implementation measures. To
encourage implementation of the differentiation strategy specifically, employee rewards were based on
both the differentiation score and total walk-through audit score.
As a result of these measures and incentives, all stores implemented the new strategy. But,
implementation of the differentiation strategy was not straightforward. Beyond the physical environment
and stocking of new products, it required store staff to establish relationships with customers and sell
high-margin products. Implementation of the strategy varied significantly among stores. Even when
stores implemented the strategy well, there was variation in how customers experienced the new strategy.
14
There was also significant variation across stores in profitability. These variations allow us to draw
conclusions about both strategy formulation and implementation.
We analyze Store24’s BSC data, including the walk-through audit scores, financial performance
measures, and employee metrics, to learn more about the hypothesized causal links around the strategy.8
Strategy Change
Store24 incorporated the differentiation component during FYs 1998 and 1999. During this time,
management monitored the scorecard. Store-level execution of operating standards (strategy-inputs)
declined and then gradually increased over this period (Figure 2), and the strategy-specific customer
outcome measure followed the same pattern. In each quarter of FY 1999 Store24 posted a higher profit
than in the corresponding quarter of FY 1998 (Figure2). Store24 management, however, could not
attribute the strong financial performance to the new strategy as growth in profits closely tracked industry
averages. In FY 2000, based on negative customer feedback, Store24 concluded that the differentiation
strategy had failed and refocused its strategy on traditional service quality activities.9 See Figure 3 for a
timeline of events related to Store24’s strategy change. Based only on trends in the balanced scorecard
metrics, it was difficult for management to definitively disentangle problems with strategy formulation
from those with strategy implementation. That is, it wasn’t easy to pinpoint why the strategy failed.
Store24 senior management also identified potential problems related to the “fit” of the
differentiation strategy with the existing level of employee capabilities at its stores. Senior management
believed successful store-level implementation of this strategy required performance in complementary,
difficult to measure activities. To leverage the environment into financial performance, skilled employees
needed to establish customer relationships. Senior management believed that high skill levels enhanced
and low skill levels limited, the relationship between implementation of the differentiation strategy and
store performance. Explained Store24’s CFO, “Managers and crew that were already skilled in our core
[efficiency based] strategy and other basic store operations such as cash, labor, and inventory control,
8
We omit the mystery shop scores due to their correlation with walk-through audit scores and data availability. We
cannot disaggregate mystery shop scores into basic service quality and differentiation strategy implementation
measures.
9
Store24 received negative feedback from in-store comment cards, telephone surveys and focus groups.
15
were able to devote considerably more time to implementing the [differentiation] strategy and to tailor
this strategy based on knowledge of their customers.
These skills made it easier to build the
[differentiation] strategy on top of the basics.” The success of local strategy implementation relied on
manager and crew interactions with customers and local market knowledge. Absent these complementary
activities, differentiation implementation might not translate into improved store performance, and might,
in fact, adversely affect performance, particularly on the productivity dimension.
Using the information learned by management over time about problems with the strategy as a
benchmark, we seek to examine the insights derived from systematic analysis of the scorecard measures.
V.
EMPIRICAL RESEARCH DESIGN
Our sample consists of financial, non-financial and customer performance measures for 65 stores
during fiscal years 1998 and 1999 (i.e., during implementation of the differentiation strategy). To obtain
scores on store-level differentiation, we disaggregate the walk-through audit scores into their constituent
components. We have data for store-level implementation of the differentiation strategy for the fourth
quarter of FY 1998 and the second and third quarters of FY 1999.
We supplement Store24’s balanced scorecard data with information on store competition and
demographics gathered during the same time period. To gain familiarity with the business environment
we interviewed Store24 senior management and reviewed company documents about the measurement
system and strategic learning process. Finally, we interviewed five store managers about store-level
execution of the differentiation strategy.
Empirical Variables
Financial Performance
To improve its financial performance, Store24 can: i) increase customers; ii) increase spending
per customer; or iii) increase the efficiency and effectiveness of store personnel (decrease costs).
Operating profit (Profit) summarizes these categories at the store level; it is defined as revenues (Sales)
from general merchandise, lottery tickets, money orders, and phone cards less cost-of-goods sold, utilities
expense, and labor expense. This measure reflects the financial components that Store24 believes store-
16
level management can influence and is the primary measure used by management to evaluate overall store
financial performance. We measure Profit as annual operating profit during FY 1999. This is the period
we are able to match with available strategy input measures, strategy outcome measures, and measures of
employee capabilities. FY 1999 is the second year of Store24's differentiation strategy, allowing enough
time for any start-up problems in implementation to be worked out. In all analyses, we scale Profit by
square feet of store selling space.
Non-financial Performance Measures
Measure of Strategy Inputs. Store-level measures of strategy inputs capture store-level activities that
management believes drive strategy success. Senior and mid-level management measure performance by
conducting walk-through audits twice per quarter. Management awards points based on compliance with
78 operating standards related to in-store image, in-stock position, merchandising and marketing
management, and facilities appearance. A percentage score is calculated by dividing total awarded points
by total potential points.10
We disaggregate stores’ total operational audit scores into scores that reflect the store’s
compliance with operating standards (strategy input measures) for the differentiation strategy.11
Input_Diff reflects a store’s percentage score on operating standards related to differentiation; it reflects
how well each store executed this strategy or the quality of the “inputs”.
We use the strategy input
measure taken at the beginning of FY 1999 in all our empirical analyses (Input_Diff).
Measure of Basic Service Quality. During the walk-through audit, Store24 management also measures
basic service quality items such as in-store image, fast service, and in-stock position. BSQ is the average
percentage score on operating standards related to basic service quality taken over the same period as our
measure of strategy inputs.
10
Mystery shop scores are positively and significantly correlated with walk-through audit scores and cannot be
disaggregated. Adding mystery shop scores to the analyses does not change the results.
11
Due to extra credit points for strong implementation of Differentiation, a store’s score on Input_Diff can reach
135%. Employees were compensated based on a separate measure of this strategy normalized by total available
points. Thus, they were induced to invest in this implementation.
17
Measure of Strategy-Specific Customer Outcomes. A third-party research firm conducted in-store
customer interviews at a subset of stores throughout FY 1999.12 Customers rated the attributes they
“liked most about this particular Store24,” including whether Store24 was “entertaining,” “a fun place to
shop,” and “unexpected.” We collect the metrics specific to the differentiation strategy; these metrics
comprise a reliable set as evidenced by a Cronbach’s coefficient alpha of 0.9596. Each attribute is
measured as the proportion of surveyed customers who stated that they liked this characteristic about a
particular store; Outcome_Diff is the average of these measures.
Outcome_Diff reflects whether
customers observe and value the new strategy; it represents a strategy-specific customer outcome measure
resulting from implementation of the differentiation strategy (strategy input measures).
Employee Capabilities. Store24 relies on its employees to execute strategy at the point of customer
contact. Thus, we take measures of manager and crew skills as our primary measures of the firm’s
strategic resources. Store24 evaluates its managers during the 2nd and 4th quarters of each fiscal year.
Managers are rated, on a five-point scale, on many dimensions including ability to retain, train, and
interact with crew; customer service; merchandising; time and labor management; maintaining store
safety; and technology use. A store manager’s skill rating (MgrSkill) is the average score across all
dimensions. Crew skills are rated on a five-point scale along similar dimensions; all non-management
employee scores are averaged to devise a store’s crew skill rating (CrewSkill).
In all subsequent
empirical tests, we use the skill metrics taken in the beginning of FY 1999.13
Were Store24’s senior management simply to infer skill ratings from actual store performance, a
store’s manager and crew skills ratings would reflect store performance rather than exogenous skill levels.
As shown in Table 2, neither manager nor crew skills exhibit significant correlations with Profit.. Thus,
on average, senior executives do not provide higher skill ratings to employees in better performing stores.
Data on individual employee skill ratings for a sample of 20 stores reveals variation in skill ratings across
12
Data was collected for approximately 15-20 stores per quarter.
Our results are invariant to the use of average skills throughout FY 1999 rather than taking the skill metrics at the
beginning of FY 1999.
13
18
individual employees within a particular store, reflecting senior management’s desire to identify
individual skills rather than infer skill-level from store performance.
Control variables. Store24 collects demographic information for the half-mile radius around each store.
Many of these demographics relate to population and foot traffic in the trading area of a given store and
are highly correlated. Because many of these variables are correlated we use factor analysis to identify
the underlying constructs and find one population factor with an eigenvalue greater than one. Population
represents daily activity around the store location. It comprises primarily the student population (pre-high
school, high school, and college), pedestrian count rating, and population density. Income is an estimate
of the median level of annual disposable income available to a family for grocery and convenience store
purchases in the surrounding area.which Store24 obtains from a third-party research firm. Because we
expect high income and/or large population areas to offer more sales potential, these variables should
relate positively to financial performance. Finally, having more competing stores in the area is expected
to be associated with lower financial performance. To control for this effect, we include Competition
which reflects the number of competing stores within a half-mile radius of each store.
We also control for unobservable location characteristics by including rent per square foot (Rent).
Store24 pays a premium to rent facilities in locations with, for example, high visibility. Cross-sectional
differences in Rent should capture store location differences which we do not directly control for in our
analyses. Finally, we include a measure of store size (SQFT), measured as square feet of retail selling
space, and a variable that indicates whether a store is open 24 hours per day (24Hours).
Methodology
We test the baseline hypothesis, H1, by estimating the following equation:
PROFITi t = α 0 + α1 Input _ Diffi + α 2 MgrSkilli + α 3CrewSkilli +α 4 BSQi + α 5Competitioni
+ α 6 Populationi + α 7 Incomei + α 8 24 Hoursi +α 9 SquareFeeti +α10 Renti +ε i
(1)
Where PROFITi denotes operating profit for store i during FY 1999. We estimate this equation using
OLS on a cross-sectional sample of 65 stores.
To reduce collinearity due to the inclusion of the
19
interaction terms and to maintain interpretability of the coefficients, we mean center the interaction
variables prior to estimation (Aiken and West 1991).
If the strategy-input measure leads to improved financial performance, we expect α1 to be positive
and significant. Finding no (a negative) relationship implies that improved strategy implementation is not
(negatively) associated with improved performance, signaling problems with strategy formulation,
strategy implementation or strategy fit.
Consistent with the framework outlined in section III, we test for problems in strategy
implementation (H2), strategy formulation (H3), and strategy fit (H4, H5, and H6) by using OLS to
estimate the following equations.
PROFITi = γ 0 + γ 1Outcome _ Diffi + γ 2Outcome _ Diffi × MgrSkilli + γ 3Outcome _ Diffi × CrewSkilli
+ γ 4 MgrSkilli + γ 5CrewSkilli + γ 6 BSQi + γ 7Competitioni + γ 8 Populationi + γ 9 Incomei
+ γ 10 24 Hoursi + γ 11SquareFeeti + γ 12 Renti + ηi
Outcome _ Diff
t
i
(2)
= β 0 + β1 Input _ Diffi + β 2 Input _ Diffi × MgrSkilli + β 3 Input _ Diff i × CrewSkilli
+ β 4 MgrSkilli + β 5CrewSkilli + ε i
(3)
Input _ Diff i = α 0 + α 1 MgrSkilli + α 2 CrewSkilli + μ i
(4)
Equation (2) is analogous to equation (1) where the outcome measure replaces Store24’s internal
input measure14. Equation (3) tests the relationship between the outcome measure and Store24’s input
measure.15 A positive correlation, β1, indicates relatively good implementation of the differentiation
strategy because the outcome measure correlates with the input metrics.
β1>0, γ 1 ≤0 would provide
evidence in favor of H20 and against H30 implying a good implementation of a bad strategy.
Conformance to operating standards (strategy inputs) leads to the desired strategy-specific customer
14
In untabulated tests, we estimate equation 2 separately for stores where Outcome_Diff was measured during the
first 6-months and second 6-months of FY 1999 respectively. In these tests, for stores measured in the first (second)
6-months, we measure manager and crew skills as the average of skills as measured during the end of the fourth
quarter of FY 1998 (second quarter of FY 1999) and the second quarter of FY 1999 (fourth quarter of FY 1999).
The results from estimation of equation 2 on each of these sub-samples are substantively similar to those reported in
Table 5 on the full sample of stores. These results mitigate the potential that the findings in our paper are due to any
mismatch in performance measurement periods within Store24.
15
Note that we do not include demographic and other store location characteristics as controls in Equation 3. There
is no a priori reason to believe that strategy-specific outcomes should be driven by these factors. However, we have
estimated Equation 3 using the same controls as in Equations 1 and 2 and results are substantively similar.
20
outcome (customers view stores as “entertaining"), but the strategy-specific customer outcome does not
translate into improved store financial performance. β1≤0, γ 1 >0 would provide evidence against H20 and
in favor of H30; it is consistent with bad implementation of a good strategy. Strategy-specific customer
outcomes (more entertaining stores) are associated with higher financial performance; however the
strategy input measures do not lead to higher levels of strategy-specific customer outcomes.
To test the complementary impact of Store24’s strategic capabilities on the relationships between
input,
outcome
and
financial
performance
measures,
we
rely
on
the
interaction
terms,
λ2Outcome _ Diffi × MgrSkilli and λ3Outcome _ Diffi × CrewSkilli , for strategy formulation tests and
β 2 Input _ Diffi × MgrSkilli and β 3 Input _ Diffi × CrewSkilli
for strategy implementation tests.
Significant coefficients on these variables indicate that the level of internal capabilities impacts the
relationships among input measures, outcome measures and financial performance (H4 and H5).
Finally, we use equation (4) to investigate the final part of the "strategic fit" hypothesis (H6) by
examining the relationship between performance on the input metric (Input_Diff) and the level of internal
capabilities (MgrSkill, CrewSkill). We include MgrSkill, and CrewSkill in equations (2) and (3) to
account for any main effects of employee capabilities on store financial performance.16
Although scaling by store size (Square Feet) alleviates concerns with heteroskedasticity, we
calculate p-values based on both OLS standard errors and Mackinnon and White’s (1985)
heteroskedasticity consistent “HC3” standard errors with no substantive differences in results.17
RESULTS
Descriptive Statistics
Table 1 provides descriptive statistics and Table 2 presents the correlation matrix for the sample
of 65 stores. Note that the stores exhibit wide cross-sectional variability in both Store24’s input measure
16
Managers with high skills may, for example, more effectively manage labor and inventory costs which would
have a direct effect on store-level financial performance.
17
White’s test for heteroskedasticity is not as reliable in small samples (Mackinnon and White 1985, Long and
Ervin 2000). Long and Ervin (1997) suggest using the HC3 estimator for standard errors when heteroskedasticity is
suspected. Although we have no a priori reason to suspect heteroskedasticity, we check p-values based on HC3
estimators for robustness (untabulated).
21
(Input_Diff) and outcome measure (Outcome_Diff).
Outcome_Diff is negatively related to Profit.
The univariate correlations suggest that
Additionally, the outcome measure is significantly
positively related to Store24’s input measure (Input_Diff). Together, this provides preliminary evidence
that the differentiation strategy was well implemented, as Store24’s view of good implementation
corresponds to the customer outcome, but possibly poorly formulated due to the negative relation of the
customer outcome with financial performance. Since stores vary on other factors that might affect
financial performance (e.g., location and skills) we refrain from making conclusions based on these
univariate tests. Competition, Population, Income, Sqft and Rent all exhibit significant correlations with
Profit. Thus, these seem to be powerful controls for unobserved location characteristics that might affect
store performance.
Tests of H1 (Identifying Problems with Strategy Formulation and/or Implementation)
Table 3 reports the results of estimating the relationship between Profit and Store24’s assessment
of stores’ internal conformance with strategic operating standards.
On average, the input metric,
Input_Diff, is not associated with Profit. This suggests that store-level effort to implement the new
strategy was not translating into store-level profits. Manager skills significantly and positively relate to
profit as does population in the surrounding area; competition is negatively related to profit. Rent per
square foot is positively related to profit suggesting that higher rents are proxying for characteristics
associated with better store locations.
These results highlight that the hypothesized link between internal implementation of the action
plans related to the new strategy and financial performance does not exist. However, it is unclear whether
the strategy is poorly formulated or poorly implemented.
Tests of H2 and H3 (Distinguishing between Problems of Formulation vs. Implementation)
Table 4 contains results from estimation of equation (3). On average, Store24’s input metric
(Input_Diff) positively relates to the outcome measure (p<0.10). This result provide support for H2 and
suggest that the strategy of creating entertaining stores was well implemented. The operating standards
management selected for the differentiation strategy related to customers’ views of stores being
22
innovative and entertaining (strategy-specific customer outcome). Estimation of equation (2) yields Panel
A of Table 5.
On average, the outcome measure of the differentiation strategy implementation is
negatively related to Profit (p<0.10).
Overall, these results tend to support H20 and negate H30. Specifically, the relationship between
the way Store24 operationalized the differentiation strategy (input measure) and the way in which the
strategy was viewed by customers (outcome measure) is correlated; the implementation seems to be good.
However, the outcome measures of strategy implementation and financial performance are negatively
correlated. Together, these results imply that although the strategy was well implemented, the strategy
formulation may have been flawed.
Tests of H4 and H5 (Identifying Strategic Fit)
H4 and H5 focus on Store24’s resources and capabilities and whether the fit between these
resources and the strategy influences its performance. In particular, employee skill levels might impact
the relationship between the input and outcome measures and/or the relationships between the outcome
measure and financial performance.
Table 4, presents the results for tests of the skill interaction (H4). The results imply that the
relationship between input and outcome measures is not contingent on the store-level capabilities of
Store24. Neither the interaction of the input measure and manager skills nor the interaction of the input
measure with crew skills is significant at conventional levels.
Panel A of Table 5 presents tests of H5.18 On average, crew skills appear to moderate the
relationship between the outcome measure and financial performance. The interaction between crew
skills and Outcome_Diff positively relates to Profit (p<0.001). Crew skills appear to be important only
when implementing a strategy that offers employee autonomy.19
Although Store24 predicted a positive relationship between the differentiation strategy and Profit,
the results imply that the benefits derived from the strategy depend on the level of crew skills in a store.
18
In untabulated results we interact skill metrics with BSQ. The interaction is not significant, and the results are
substantively unchanged.
19
We also investigate, in untabulated results, whether the interaction of BSQ and skills as well as the interaction of
Input_Diff with store location variables relates to performance. All results hold and the additional interactions are
not significant.
23
We further examine the interaction between crew and the outcome measure using post-hoc probing as
suggested by Aiken and West (1991).20 Panel B of Table 5 illustrates the estimated relationship between
the outcome measure (Outcome_Diff ) and financial performance (Profit), conditional on high (1 point
above the mean rating), mean, and low (1 point below the mean rating) crew skills, respectively. We
compute the standard errors for each estimated relationship in Panel A of Table 5 conditional on the level
of crew skills and adjust t-statistics accordingly prior to inference.
The outcome measure negatively impacts Profit in stores with low and average skills. However,
these negative impacts seem to be mitigated in stores with high crew skills where there is a positive
relationship between the outcome measure and Profit. Overall, the results suggest problems with the fit
of the differentiation strategy with Store24’s employee capabilities. Crew skills determine the magnitude
of the relationship between strategy outcomes and financial performance, but the relationship is only
greater than zero for high levels of crew skills.21
Results of H6 (Identifying Fit at the Operational Level)
The results of tests of the drivers of the input metrics are presented in Table 6. On average, crew
skills are not significantly related to strategy execution at the store-level; manager skills are positively and
significantly related to store-level strategy execution (Input_Diff) (p<0.10) These results again call into
question the fit of the strategy with the resources at Store24; in the case of crew skills, increases in the
underlying capabilities do not correlate strongly with better implementation of operating standards related
to the differentiation strategy. Therefore, we find only mixed support for H60 in this setting.
VI.
DISCUSSION AND CONCLUSION
Our research investigates whether and how strategically linked performance measures reveal
information about the quality of a firm’s business strategy. Using data from Store24, we document that
the extent to which non-financial performance measures predict future financial performance depends on
characteristics of the underlying strategy captured by those measures. Little, or no, relationship between
20
Because we mean-center all variables prior to interaction, it is difficult to interpret the economic significance of
the results directly from the tables.
21
The training costs associated with skill improvements should be considered before the long-run viability of
differentiation is evaluated. Because data constraints preclude this analysis, this study indicates only the benefits of
the strategy gross of training costs.
24
firm-specific non-financial metrics and accounting returns may be informative about (1) the firm’s
strategy formulation, (2) its strategy implementation, or (3) the strategy’s fit with internal capabilities.
We provide some of the first field-based empirical evidence on the potential for a set of strategically
linked performance measures to distinguish between these three alternatives.
Companies develop links in non-financial performance measurement systems based on ex ante
expectations (Ittner and Larcker 1998). Our findings indicate that non-financial and financial measures
and the hypothesized links between them can be used more extensively for continuous hypothesis testing
ex post. Building on prior research illustrating the use of balanced scorecards data to communicate
strategy (Selto and Malina 2001; Banker et. al. 2004), we use Store24’s balanced scorecard data to study
how the system can be used to test strategy performance. Our findings suggest that ongoing tests of these
relationships are important to ensure that hypothesized links are valid. Such investigation can potentially
reveal specific aspects of a strategy’s merits as well as its shortcomings; it can help distinguish between
strategic problems related to formulation, implementation, or fit of the strategy with the firm’s internal
capabilities. If a company consistently applies its scorecard across multiple units, these tests can be
performed at an early stage, prior to collecting an extensive longitudinal sample.
The results in this paper are subject to the caveat that the field-based nature of our research limits
the generalizability of our findings. However, the unique nature of any firm’s strategy dictates that the
performance measures and links between these measures, articulated in the firm’s business model, are
likely to be firm-specific. Future research should provide additional evidence from other settings of the
extent to which business model-based performance measurement systems capture information useful for
monitoring strategic progress. We recognize that there is a strong interrelationship between the concepts
of strategy-formulation, strategy-implementation, and fit. We define our tests of strategy-formulation as
analyzing whether, given the resources available to Store24, their choice of strategy was sound. Similarly,
our tests of implementation refer to the efficacy of Store24's unique internal processes in achieving its
strategic objectives given its available resources. Our point is not to belabor the distinction between
formulation, implementation, and fit, but rather to identify a method that can systematically test how well
25
different drivers of performance are working to achieve strategic objectives and superior financial
performance.
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FIGURE 1
Summary of Strategic Hypothesis Tests
Financial Performance
Financial Perspective
H30
H10
Strategy-Specific Customer Outcomes
Customer Perspective
H50
Internal Process Perspective
H20
Strategy Inputs
H60
Learning and growth Perspective
H40
Internal Capabilities
(Strategic Resources)
27
FIGURE 2
Store24’s Scorecard Metrics during Differentiation Period
Strategy-Spcific Customer Outcome Measure
Strategy-Specific Input Measure
6.20
Enjoyable Experience Rating
Walk-Through Audit Score
124%
124%
123%
123%
122%
122%
121%
121%
6.10
6.00
5.90
5.80
5.70
120%
5.60
120%
Q1 FY99
Q1 FY99
Q2 FY99
Q3 FY99
Q4 FY99
Q2 FY99
Q3 FY99
Q4 FY99
Quarter
Quarter
Average Operating Profit
$5,400
Operating Profit
$5,200
$5,000
$4,800
$4,600
$4,400
$4,200
$4,000
Q1 FY98
Q2 FY98
Q3 FY98
Q4 FY98
Q1 FY99
Q2 FY99
Q3 FY99
Q4 FY99
Quarter
*
**
Operating profit is scaled by the number of weeks in each respective quarter.
Note that operating profit in convenience store retailing exhibits strong quarterly seasonality.
FIGURE 3
Timeline of Events Related to Store24’s Strategy Change
Q1 FY 1998
Q3 FY 1999
Q4 FY 1999
•Store24 implements
•Customer
•Customer focus
differentiation
strategy.
•Translates strategy
to a set of operating
standards and
measures store-level
implementation of
these standards using
walk-through audits.
•Monitors and enforces
store-level strategy
implementation using
walk-through audits
•Monitors customer
feedback
surveys suggest
differentiation
strategy is not
resonating with
customers.
groups confirm that
differentiation strategy
is not resonating with
customers.
• Store 24 refocuses
basic service
operations.
Q1 FY 2000
•Store24
updates
performance
measures to only
reflect basic
service
operations
feedback about strategy
through in-store comment
cards and telephone
surveys.
28
TABLE 1
Descriptive Statistics for the Sample of 65 Stores Used in Empirical Analyses
Variable
Profit
Input_Diff
Outcome_Diff
MgrSkill
CrewSkill
BSQ
Competition
Population
Income
24hours
Sqft
Rent
Mean
133.93
108.16
27.98
3.27
3.35
89.89
3.87
-0.06
2,588.35
0.85
2,139.05
23.73
SD
54.88
22.39
9.88
0.63
0.43
5.58
1.38
0.90
532.20
0.36
374.78
15.90
Min
Median
51.88
121.63
46.43
117.85
2.56
26.85
1.21
3.27
2.75
3.24
71.21
89.60
1.65
3.68
-1.27
-0.28
1,700.00 2,499.00
1.00
1,333.00 2,133.00
4.76
19.02
Max
349.49
135.71
51.87
4.38
4.51
99.26
11.13
3.06
4,230.00
1.00
2,919.00
85.71
Profit = Revenue from general merchandise, lottery tickets, money orders, and phone cards less expenses related to
cost-of-goods sold, utilities, and labor, scaled by square feet of the store;
Input_Diff = measure of store-level implementation of Differentiation strategy measured as percentage compliance
with operating standards related to the differentiation strategy;
Outcome_Diff = customer (Outcome) measure of Differentiation strategy;
MgrSkill and CrewSkill =Average of bi-annual measures of the manager and crew skills in basic store operations,
rated on a five-point scale;
BSQ = measure of percentage compliance with operating standards related to basic service quality;
Competition = number of competitors within the trading area of a store;
Population = store location factor score capturing items related to population density and foot traffic around the
stores’ trading area;
Income = Measure of median annual disposable income available for grocery and convenience store purchases in the
stores trading area
24hours = 1 if store is open 24 hours per day, 0 otherwise;
Sqft = square footage of the store; and
Rent = monthly rent per square foot for store.
29
TABLE 2
Correlation Matrix for 65 Stores during FY 1999
Profit Outcome_Diff Input_Diff MgrSkill CrewSkill BSQ Competition Population
1
Profit
-0.4125*
1
Outcome_Diff
-0.1293
0.2433*
1
Input_Diff
0.1412
-0.0241
0.2179*
1
MgrSkill
0.127
-0.0768
-0.0179
0.1909
1
CrewSkill
0.1022
0.1007
0.2612*
0.3548* 0.1492
1
BSQ
-0.3920*
0.2477*
0.0419
0.1701 -0.1642 0.0138
1
Competition
0.4452*
-0.0848
-0.2750*
-0.1793 0.0407 -0.0592
-0.1303
1
Population
0.2223*
-0.4648*
-0.1132
-0.2596*
0.108
0.0434
-0.3821*
0.0281
Income
-0.096
0.0209
0.2731*
0.1127
0.0625 0.2005
0.0998
-0.1776
24hours
-0.5790*
0.2280*
0.1885
0.2313* -0.0859 0.1552
0.3145*
-0.181
Sqft
0.6526*
-0.4003*
-0.3130*
-0.188
0.136
-0.0654 -0.4161*
0.3904*
Rent
* Significant at the 10% level. All significance levels are reported using a two-tailed test.
Income
24hours
Sqft
Rent
1
-0.0095
-0.0661
0.4206*
1
0.035
-0.1363
1
-0.5487*
1
30
TABLE 3
The Relationship between Strategy-Specific Inputs and Financial Performance (Dependent Variable
= Profit; Adjusted R2 = 0.71)
Intercept
Input_Diff
MgrSkill
CrewSkill
BSQ
Competition
Population
Income
24hours
Square Feet (00's)
Rent per Square Foot
Coefficient
84.13
0.14
33.74
-9.91
0.69
-4.95
19.63
0.01
-6.02
-0.06
0.97
Standard Error
84.18
0.17
7.33
11.01
0.83
2.56
4.97
0.01
12.53
0.02
0.38
Two-Sided p-Value
0.322
0.421
0.000
0.372
0.41
0.059
0.00
0.183
0.633
0.00
0.012
All bolded coefficients are significant at least at the 10% level using a two-tailed test.
TABLE 4
Strategy Implementation Tests
(Dependent Variable = Outcome_Diff; Adjusted R2 = 0.08)
Intercept
Input_Diff
Input_Diff x MgrSkill
Input_Diff x CrewSkill
MgrSkill
CrewSkill
Coefficient
26.46
0.102
-0.001
0.000
-1.185
-1.651
Standard Error
12.56
0.059
0.001
0.001
2.045
2.979
Two-Sided p-Value
0.039
0.087
0.554
0.724
0.564
0.582
All bolded coefficients are significant at least at the 10% level using a two-tailed test.
31
TABLE 5
Panel A: Strategy Formulation Tests
(Dependent Variable = Profit; Adjusted R2 = 0.76)
Intercept
Outcome_Diff
Outcome_Diff x MgrSkill
Outcome_Diff x CrewSkill
MgrSkill
CrewSkill
BSQ
Competition
Population
Income
24hours
Square Feet (00's)
Rent per Square Foot
Coefficient
108.99
-0.81
-0.54
3.26
30.07
0.43
0.89
-4.87
18.36
0.01
-4.44
-0.07
0.93
Standard Error
82.33
0.48
0.59
0.93
8.45
10.29
0.85
2.14
4.56
0.01
10.45
0.02
0.38
Two-Sided p-Value
0.191
0.098
0.358
0.001
0.001
0.967
0.300
0.027
0.000
0.604
0.673
0.000
0.019
All bolded coefficients are significant at least at the 10% level using a two-tailed test.
Panel B: Summary of Moderating Effect of Crew Skills on Strategy Formulation Tests
Two-sided p-value for test of γ 1 + γ 3 = 0
Low Crew Skills
Mean Crew Skills
High Crew Skills
Coefficient
-4.07
-0.81
2.44
Two-Sided p-Value
0.000
0.098
0.020
TABLE 6
Tests of Strategy Fit
(Dependent Variable = Input_Diff; Adjusted R2 = 0.05)
Intercept
MgrSkill
CrewSkill
Coefficient
92.42
8.10
-3.21
Standard Error Two-Sided p-Value
24.03
0.000
4.45
0.073
6.55
0.626
All bolded coefficients are significant at least at the 10% level using a two-tailed test.
32