PUBLIC PERFORMANCE & MANAGEMENT REVIEW 2017, VOL. 40, NO. 3, 504–528 http://dx.doi.org/10.1080/15309576.2016.1276843

Exploring the Change in Strategy Formulation and Performance Measurement Practices Under Turbulence Obed Pashaa and Theodore H. Poisterb

aUniversity of Massachusetts, Amherst; bGeorgia State University

ABSTRACT This article adds to the growing literature exploring the impact of cutbacks and environmental turbulence on organizational operations by studying the changes in strategy formulation and performance measurement practices in the public sector in the wake of the Great Recession. Survey data were collected from public transit agencies in small and medium-sized cities at the beginning of the recession in 2009 and again in 2013. The analyses found that transit agencies dealt with the fallout from the economic crisis by increasing their use of logical incrementalism approaches to strategy formulation through negotiating with organizational stakeholders and responding to newly emerging information through continuing changes in strategy. Agencies also placed greater emphasis on perfor-mance measurement practices. While the use of formal strategic planning itself remained constant under turbulence, the blended approach of formal strategic planning and logical incrementalism showed significant increase. Theoretical and practical implications of the findings are discussed in the conclusion.

KEYWORDS performance measurement; strategic planning; turbulence

Scholars and practitioners have paid considerable attention to performance- based management in the last three decades. Although the push for performance-based systems goes back to the introduction of processes such as Management by Objectives (Carroll & Tosi, 1973; Odiorne, 1965), perfor-mance management in its current form began to appear in the public sphere in the early 1990s (see Epstein, 1992; Gore, 1993; Osborne & Gaebler, 1992; Poister & Streib, 1999). Performance management systems consist of setting goals, developing plans, defining performance indicators, monitoring the progress on these indicators, analyzing the performance information, and using the results to make decisions (Moynihan, 2008).

Performance management can be divided into three broad components: strategy formulation, performance measurement, and utilization of perfor-mance information to take decisions. Strategy formulation deals with estab-lishing an overall direction for the organization, setting targets at various levels in the organization, and developing strategic alternatives to reach those

CONTACT Obed Pasha opasha@umass.edu School of Public Policy, University of Massachusetts Amherst, 418 N. Pleasant St., Amherst, MA 01002, USA. © 2017 Taylor & Francis

targets. Performance measurement builds on organizational strategy to ident-ify the appropriate indicators to measure progress toward achieving strategic goals, analyzing information, and reporting the results (Van Dooren, Bouckaert, & Halligan, 2010, p. 25). Finally, the information generated from this process is used to help make a strategic decision. This study will only focus on strategy formulation and performance measurement. These two compo-nents are bound up in a reciprocating relationship in which performance measurement is dependent on the strategy formulation process to develop the overall direction of the program, while the process of strategy formulation uses feedback generated by the performance measurement process in order to revise organizational goals, targets, strategies, services, operations, standards, and so forth (Moynihan, 2008).

Most research on performance management systems focuses on determi-nants that promote the adoption and use of performance management prac-tices, such as leadership support, public service motivation, employee attitudes, and social norms (see Kroll, 2013; Moynihan & Pandey, 2010). Other scholars have studied the effectiveness of these systems in improving the organizational performance of public agencies (Poister, Pasha, & Edwards, 2013; Sun & Van Ryzin, 2012; Walker, Damanpour, & Devece, 2010). How-ever, almost no attention in the literature has been given to the adoption and use of performance management practices in periods of stability and growth versus periods of instability and turbulence. The present article adds to the growing literature on performance information use (see Kroll, 2013; Moynihan, 2008) by proposing environmental turbulence as yet another factor which impacts the use of performance information in public organizations.

This question has gained renewed importance in the wake of the Great Recession in the United States, which hit all levels of government and put substantial pressure on organizational resources. The purpose of this article is to propose and test hypotheses about how one might expect performance management practices to change during periods of crisis and instability. There is a plethora of research on cut-back management in public organizations that focuses on responses to the recession or the permanency of the subsequent reforms (see Ammons, Smith, & Stenberg, 2012; Nelson & Balu, 2014; Scorsone & Plerhoples, 2010). However, the public administration literature lacks a coherent theory and empirical evidence on the changes in organiza-tional strategy formulation and performance measurement methods due to turbulence. In addition to examining the changes in management practices under turbulence, this article provides avenues for future research that may lead to guidance for organizations facing turbulent environments.

Turbulence in this context is defined as “externally induced changes … that are obscure to administrators and difficult to plan for” (Aldrich, 1979, p. 69) and push the organization toward “lower levels of resource consumption and

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organizational activity” (Levine, 1979, p. 180). Environmental dynamism, uncertainties, rapid changes, and unpredictability are all part of a turbulent environment (O’Toole & Meier, 2014). The impacts of economic turbulence can be best understood through the seminal piece on cut-back literature by Charles Levine (1978). Levine (1978) suggests that a decline in public services is inevitable under external environmental entropy due to an increase in financial stress on public agencies. As a result, the provision of public services decreases even in the face of constant or increasing demands for them. More recently, a study by Meier and O’Toole (2009) found that public schools mitigated the impact on performance under turbulence by engaging in such management practices as shifting resources toward the more essential tasks.

This article takes the research forward by proposing that public organiza-tions would increase the use of strategic planning, logical incrementalism, a blend of logical incrementalism and formal strategic planning, and perfor-mance measurement under turbulent conditions. Resulting evidence from the transit industry suggests that while most agencies continued to engage in formal strategic planning, they were more likely to place greater emphasis on logical incrementalism as well as the blended approach to formulate strat-egy. Consistent with Quinn (1980) and Poister et al. (2013), these results show a complementary relationship between strategic planning and logical incre-mentalism. The findings also support the hypothesis that public agencies are more likely to use performance measurement in a turbulent environment.

Prior research on the transit industry has shown that increased emphasis on logical incrementalism, the positive interaction (blend) between formal strategic planning and logical incrementalism in formulating strategy, and increased attention to performance measurement lead to higher levels of effectiveness during periods of stability (Poister et al., 2013). Hence, the find-ing reported here—namely, that agencies turn to increased use of logical incrementalism and performance measurement under turbulence—would constitute good news for public managers, as these practices may help them increase organizational performance in challenging times.

The transit industry during the Great Recession

There are over 600 small, medium-size, and large transit agencies operating in urban areas of the United States. These agencies help create economically and culturally vibrant urban and metropolitan areas, while fulfilling equity goals by attending to the transportation needs of low-income groups. Unlike other traditional services provided by local governments, such as education and law enforcement, transit agencies have been added to the local government port-folio relatively recently in the United States. and they still maintain some of the characteristics of private business to a significant degree (Poister et al., 2013). Since transit services are heavily subsidized, their operations suffered

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a great deal of stress and cutback during the Great Recession as they competed for resources against other essential public services, such as police, fire, and sanitation, for budgetary support (Villaraigosa, 2010). Furthermore, decreases in overall ridership and thus in earned revenue also occurred, as a substantial number of regular commuters lost their jobs. Hence, the impact of the Great Recession on local transit agencies was threefold: depleted resources, reduced demand, and deteriorating performance.

Figures 1 and 2 trace the changes in budget and ridership, respectively, for all U.S. transit agencies from 2004 through 2014. The vertical reference line represents the year the Great Recession hit the U.S. market and divides the graph into pre- and post-Recession periods. The average annual budget in U.S. dollars is calculated by adding the total annual per capita budgets of indi-vidual transit agencies and dividing by the total number of agencies. Ridership is calculated by adding the total unlinked passenger trips (UTP) carried by these systems and taking an average across the number of transit agencies. These data were obtained from the National Transit Database, which is main-tained by the Federal Transit Administration.

Transit agencies found themselves cash-strapped during the recession and saw their budgets shrinking by more than 10% from their pre-Recession peak of over $77 per capita in 2008 to a bit over $69 per capita in 2009 at the worst point of recession (see Figure 1). Similarly, industry-wide ridership decreased from almost 22.5 million in 2008 to around 21.5 million in 2010 (see

Figure 1. Average annual budget of transit industry in USD.

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Figure 2). The Great Recession shocked the transit industry and disturbed its equilibrium and growth, as these agencies came under immense pressure to maintain their service levels and quality while coping with budget cuts and depreciation of revenue sources, while fuel prices and wages continued to increase.

Organizational operations under turbulence

Emphasizing the importance of context in determining changes in manage-ment behavior and organizational processes, O’Toole and Meier (2014) argue that a turbulent and unstable environment would cause the organization’s operations to differ substantially, as compared to times of stability and growth. In another study, Meier and O’Toole (2009) termed the protection of public programs from external shocks a “core aspect of management” (p. 486). These assertions follow the dynamic capabilities logic, which argues that organizations shift their focus toward exploratory learning under environmental turbulence, instead of learning through internal processes dur-ing times of stability and growth (Eisenhardt & Martin, 2000; Teece, 2007).

Pandey (2010) traces the impact of cutback management due to environ-mental turbulence on the motivation of public employees. Environmental turbulence such as the Great Recession often forces public organizations to violate the principles of job security associated with these organizations and thus reduces employee trust. Nevertheless, Levine, Rubin, and Wolohojian

Figure 2. Average annual passenger trips of transit industry.

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(1981) urge public managers to take a proactive role in dealing with crisis and take control of decision-making in their own hands rather than leaving it up to external events or to outside stakeholders who might not have the necessary knowledge of the particular conditions of concern.

Four hypotheses are presented in this section based on the recommenda-tions of scholars regarding management under turbulent conditions. Some scholars emphasize the use of formal strategic planning to help reduce the negative impact of turbulence through a rational planning process. Others have argued in favor of logical incrementalism or a blend of formal strategic planning and logical incrementalism approaches characterized by purposeful political negotiations and flexibility, while the use performance management is also suggested as means to reduce uncertainty and increase the decision- making power of managers.

Formal strategic planning

The formal strategic planning approach helps organizations adopt purposeful strategies and initiatives to adjust and respond to the environment based on rational, logical, and objective analysis (Berman & West, 1998; Boyne & Walker, 2004). Grant (2003) defines formal strategic planning as a process that typically begins with data-driven analysis of the external environment (e.g., demand for ridership, competition) and internal strengths and weak-nesses (e.g., well-trained bus drivers, service interruptions due to mechanical failures), followed by the leadership’s expression of their expectations in terms of performance targets (e.g., increase ridership by 5% by next year) and policy guidance (e.g., expand service in low-income neighborhoods). Finally, a business plan may be prepared to reach the performance targets and serve the policy guidelines (e.g., expand the fleet by 10 new vehicles in order to establish an additional route through a low-income area). These alternatives are then implemented as specified and evaluated for effectiveness (Camillus, 1982).

Formal strategic planning is thought to be an effective method of formulat-ing strategy in turbulent times by Eisenhardt (1989), Goll and Rasheed (1997), Hart and Banbury (1994), and Miller and Cardinal (1994). Indeed, uncertain environments are considered by some to be the impetus for organizations engaging in formal strategic planning in order to achieve better results (Parnell, Lester, & Menefee, 2000). A changing environment requires greater breadth and depth of analysis in order to overcome threats and take advantage of the opportunities that arise in these situations (e.g., introducing new ser-vices, finding new ways to reduce expenditures) (Goll & Rasheed, 1997; Hart & Banbury, 1994). Organizations in uncertain environments need to pay greater attention to clarity of structures, procedures, and goal orientations than organizations in stable environments in order to remain focused on their

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broader raison d’être and thus ensure their long-term survival (Miller & Friesen, 1983; Perrow & Perrow, 1970). Thus, in order to succeed, they need to process more information on the available options and the likely impacts of different strategies, as well as develop an institutionalized structure, mission, and strategy (Hamel, Doz, & Prahalad, 1989; Hart & Banbury, 1994; Lawrence, Lorsch, & Garrison, 1967).

These assertions are not supported by other scholars, including Fredrickson and Iaquinto (1989) and Mintzberg (1973), who prescribe the use of formal strategic planning only in stable environments. According to these scholars, a formal strategic-planning process is incapable of drawing predictions with confidence in an unstable environment, and as a result only weak decisions can emerge through this process, because situational clarity is hard to come by in an unstable environment (Grant, 2003).

However, contingency and resource-dependence theories, with their emphasis on learning from the environment, support the use of formal strategic planning under turbulence. An organization’s well-being under turbulence depends upon its ability to adapt, and formal strategic planning is a tool that can help organizations manage in times of crisis by setting new goals and developing attendant action plans (Bourgeois, 1985; Boyd, 1991). These views are echoed by complexity theory, which argues that when an organization is hit by an external event such as the Great Recession, it may be derailed from its equilibrium and forced to embrace a major transform-ation in order to regain control over its operations and maintain viability (Adcroft & Mason, 2007).

In their study of human resource management in business organizations, Cook and Ferris (1986) found that organizations that engage in formal stra-tegic planning perform better than others in a turbulent environment. Priem, Rasheed, and Kotulic (1995) corroborated these results through their research on 101 manufacturing firms, in which they found that formal strategic plan-ning had a positive impact on organizational performance in unstable conditions but had no significant impact in stable situations. In another sur-vey on private organizations under recession, Wilson and Eilertsen (2010) showed that managers valued strategic planning as a way to cope with the crisis and achieve growth. Although these kinds of studies have not been con-ducted in the public sector, their results could logically pertain to government organizations as well.

H1: Organizations will make greater use of formal strategic planning under turbulence than under stability.

Logical incrementalism

Logical incrementalism is an alternative method of formulating strategy, in which strategy is continually revised based on learning, experimentation,

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stakeholder negotiations, political relationships, and adaptation to environ-mental changes within a general framework of organizational purposes (Behn, 1988; Quinn, 1978). Through this approach, strategy evolves as “internal decisions and external events flow together to create a new, widely shared consensus for action” (Quinn, 1978, p. 7). In these circumstances, top execu-tives tend to keep decisions vague until a consensus emerges among external and internal stakeholders through negotiations and participation. In contrast to the reliance on rational, logical, and objective analysis in the formal stra-tegic-planning approach, logical incrementalism is characterized by conflict over the allocation of resources, policy objectives, and internal and external power dynamics (Elbanna, 2006; Walker, Andrews, Boyne, Meier, & O’Toole, 2010). It is important to note that the logical incrementalism approach is by no means devoid of purpose. Instead, this approach emphasizes the need to take politics, power, and external relationships into account and to be flexible in ongoing decision-making in order to fulfill basic organizational purpose and goals (Quinn, 1980).

For example, the decision to establish an additional transit route in a low- income neighborhood might emerge after negotiations with local community leaders (who might be pushing for service expansion), the mayor (who might be arguing for needed budget cuts), and unionized bus drivers (who might be concerned about the possibility of layoffs, or, alternatively, overly demanding trip schedules). Furthermore, any decision that emerges through this process is kept flexible for future events (e.g., the length of the new route might be altered in the wake of resulting patterns of utilization or “dead mileage” in the route that are not generating ridership). In sum, logical incrementalism is a continuous and evolving negotiation or consensus-building process based on experimentation and learning; it does not have a specific beginning or end (Quinn, 1982; Quinn, 1989). Table 1 summarizes these differences between formal strategic planning and logical incrementalism as approaches to strategy formulation.

Consistent with the logical incrementalism approach, Tversky and Kahneman (1973, p. 207) assert that decision-makers “employ a limited

Table 1. Differences between formal strategic planning and logical incrementalism. Formal strategic planning Logical incrementalism

Rational-comprehensive decision-making to reach specific goals

Deliberate but incremental decision-making, negotiated within framework of overall organizational purposes

Strategic long-term decision-making Pragmatic short-term compromise decisions Fundamental political priority-setting Incremental pragmatic compromises Big decisions that establish broad direction Small decisions regarding incremental changes Decision-making concentrated in a few actors Decision-making dispersed among stakeholders Long-term sustainable solutions Short-term quick fixes

Sources: Kickert, Randma-Liiv, & Riin, 2013; Peters, Pierre, & Randma-Liiv, 2011.

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number of heuristics” to simplify judgments in order to deal with uncertainty (also see Kahneman & Tversky, 1996; Tversky & Kahneman, 1986). Adap-tation to rugged landscape theory argues that the success of any organiza-tional change leading to viability in a turbulent environment is a function of the interaction between its different elements which may inhibit radical changes (Levinthal, 1997; McKelvey, 1999). Decisions in public organizations often emerge from within the organization through stakeholder negotiations rather than a top-down process; changes in resource needs, performance expectations, and personnel management are very likely to occur under tur-bulence and may require stakeholder approval and support (Lewis, 2007; Lewis & Seibold, 1998; Mintzberg, 1994). In order to keep stakeholders on board in a time of transition forced by environmental turbulence, managers are more likely to rely on negotiations rather than objective decision-making as supported by the formal strategic-planning approach.

Furthermore, turbulent environments often require intuition and creativity, which are best supported by the logical incrementalism approach, given its emphasis on flexibility of action (Hamel, 1996; Khatri & Ng, 2000). Rapidly changing environments require prompt decision-making and not a tedious, careful analysis; organizations can better mitigate external threats and take advantage of opportunities springing up for brief periods of time by being quick and adaptive in their actions (Miles, Snow, Meyer, & Coleman, 1978).

In his study on the impact of the Great Recession on the public sector in Slovenia, Pevcin (2014) found an increased use of politics in decision-making as power dissipated from the operating agencies to other external stake-holders, such as the Ministry of Finance. Similar conclusions were drawn by Roberge, Jesuit, Cepiku, and Bonomi Savignon (2012) in their study on the impact of recession on public management in developed countries.

H2: Organizations will make greater use of logical incrementalism under turbulence than under stability.

Interaction of strategic planning and logical incrementalism

Although formal strategic planning and logical incrementalism are two separ-ate concepts, they are not mutually exclusive or necessarily in competition, and they often coexist under the broader strategy umbrella of an organization. Emphasizing the importance of an approach that blends formal strategic planning and logical incrementalism, Bryson (2011, p. 65) recommends

Whenever important opportunities to implement strategies and achieve objectives arise, they should be taken. In other words, it is important to be opportunistic as well as deliberate. And it is important to remember that what happens in practice will always be some blend of what is intended and what emerges along the way.

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In sum, the organization keeps its strategy formulation flexible while accommodating politics and compromise with formal analytical approaches.

Within this framework, an organization may set broader objectives and a strategy to reach its goals, but rely on the logical incrementalism process for day-to-day decision-making (Camillus, 1982; Papadakis & Barwise, 2012; Quinn, 1978). Alternatively, its leaders may decide to engage in formal strategic planning within a larger logical incrementalism context when orga-nizational outcomes are ambiguous and the long-term future is uncertain (Barzelay & Campbell, 2003; Poister, 2010). Thus, the present article follows Poister et al. (2013) by including the interaction of formal strategic planning and logical incrementalism in the discussion and expects an increase in the interaction of formal strategic planning and logical incrementalism under tur-bulence due to an increase in the general emphasis on strategy as well as a separate emphasis on formal strategic planning and logical incrementalism (Eisenhardt & Martin, 2000; Teece, 2007).

H3: Organizations will make greater use of a blending of formal strategic planning and logical incrementalism under turbulence than under stability.

While logical incrementalism on its own may not lead to performance, and may even detract from performance, because it does not necessarily focus on desired outcomes in a disciplined manner (Andrews, Boyne, Law, & Walker, 2009; Walker et al., 2010), integrating the interactive dimension of logical incrementalism with the more formal rational approach embodied in strategic planning may well maximize organizational effectiveness (Camillus, 2008; Papadakis & Barwise, 2012; Quinn, 1978). Other observers have noted that the value of strategic planning is enhanced when it is conducted within a larger context or with purposeful incrementalism (Barzelay & Campbell, 2003; Poister, 2010), and that it is important for public agencies to be opportunistic as well as deliberate in formulating strategy (Bryson, 2011).

Performance measurement

Unstable, difficult, and crisis-prone environments are cited as one of the major justifications for using performance measurement (Athanassopoulos & Ballantine, 1995; Bidya, 2009). Hussain and Hoque (2002) argue that organizations under crisis will rely more on performance indicators as they need the information so obtained to make better decisions to cope with tur-bulence and uncertainty. Performance measurement helps focus stakeholder attention on the outcomes rather than the immediate work done by employ-ees, hence easing the pressure on employees during times of crisis and helping them work in the direction of clear targets (Bidya, 2009). Keeping track of organizational performance may also provide managers with greater control, thereby reducing the chances of drifting with environmental changes.

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Evidence of increased use of performance measurement to cope with turbulent environments has been found by Grant (2003) in a study on major multinational oil companies. In that study, he found that in unstable con-ditions, organizations tend to shorten their planning horizons and increase the use of performance measurement to achieve greater control over the environment. Thus, performance measurement is likely to be more essential in turbulent conditions, when agencies may be operating in a crisis mode where resources are more constrained, choice sets are likely to be more limited, and the consequences of bad decisions or poor management may be much more severe.

With respect to the public sector in particular, Behn (2003) identified eight purposes of performance measurement—evaluate, control, budget, motivate, promote, celebrate, learn, and improve—all of which are likely to be more important during turbulent times. For example, it is more critical under turbulent conditions to understand how well an agency is performing on its most fundamental measures and whether and how its performance may be trending (evaluate), ensure that organizational units and employees are focus-ing attention and effort on the organization’s priorities (control), allocate scarce resources efficiently and effectively (budget), encourage managers and employees to work harder and smarter in the face of heavier workloads and a lack of instrumental incentives (motivate), advocate the agency’s mis-sion and services effectively to external audiences when their support may be needed more than in stable periods (promote), discover what works and what does not work well in difficult times (learn), and, overall, to strengthen or at least maintain performance levels to the extent possible under more challenging circumstances (improve). In sum, then, organizations are likely to have greater and more immediate need for basic performance information in turbulent conditions precisely because they face more difficult decisions brought on by more challenging circumstances.

H4: Organizations will make greater use of performance measurement under turbulence than under stability.

Methodology

This article uses local public transit agencies as the unit of analysis to test the proposed hypotheses. Comparability among the agencies was ensured by employing the National Transit Database (NTD) maintained by the Federal Transit Authority to identify a subset of transit agencies in the United States that were similar in size and operations. The analysis excludes agencies with fewer than nine vehicles in operation from the sample, because they are exempted from reporting to the NTD. In order to ensure further uniformity, only agencies operating bus transit systems were included, while agencies that

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operate rail services were excluded because they have institutional structures, operating characteristics, and service demands that are very different from those of agencies that only operate bus systems (Ryus, 2003).

The sample, thus, did not include transit agencies from the largest metropolitan areas such as New York City, Washington, D.C., Chicago, San Francisco, and Miami, among others, because the agencies in these larger metropolitan areas all offer rail as well as bus services. Given these parameters, the population of interest consists of 236 transit agencies that can generally be characterized as agencies that operate small and medium-sized bus transit sys-tems in urbanized areas. This definition of the sample of interest is consistent with other research concerning performance in the transit industry (see Leland & Smirnova, 2009; Zullo, 2008).

Information on the use of formal strategic planning, logical incremental-ism, and performance measurement was collected through two waves of surveys of the chief executive officers (CEOs) of these agencies. The contact information for the CEOs was gathered primarily from the NTD and agency websites. The first survey, conducted in early 2009, queried the CEOs of these 236 mid-sized local transit agencies about the extent to which they utilized formal strategic planning, logical incrementalism, and performance measure-ment practices. The responses were based on a 5-point Likert scale (strongly disagree, disagree, neutral, agree, and strongly agree). The CEOs were asked to base their responses on the period between 2004 and 2008, which was a period of stability and growth in the transit industry. The online survey was launched through e-mail messages that introduced the purpose of the survey and provided a link to the survey website; 105 responses were received, for a response rate of 44%.

A second online survey containing the same items was sent to the same 236 agencies in March and April of 2013. The CEOs were asked to base their responses to this second survey on the period from 2009 to 2012 in order to cap-ture the timing of the Great Recession. Sixty-three transit agencies responded, for a response rate of 27%; 42 of these agencies were repeat responders from the 2009 survey. Hence, out of the 236 transit agencies in the population of interest that were contacted in both surveys, 84 responded to one or the other of the two surveys, while 42 responded to both surveys, for a total of 168 responses. The usable sample size available for analysis further decreased some-what due to missing values for some cases. Independent-samples t-tests compar-ing the respondents and nonrespondents of the 2009 and 2013 surveys in regard to service area population, fleet size, and passengers per capita are provided in Appendices 1 and 2 respectively. See Appendix 3 for a comparison between the respondents of the 2009 and 2013 surveys. The t-tests do not indicate differences between the respondents and nonrespondents of the two surveys.

Two survey items each are used to represent the use of formal strategic planning, logical incrementalism, and performance measurement in the

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transit agencies. These items are coded from 1 through 5, where “1” denotes strongly disagree and “5” denotes strongly agree on a 5-point Likert scale. Indi-ces were created for formal strategic planning, logical incrementalism, and performance measurement by taking the averages of the responses to the two corresponding survey items for each of these categories. The six items corresponding to these components are given in Table 2, and descriptive statistics for them are presented in Table 3. Due to the repetition of transit

Table 2. Survey items corresponding to three components of performance management. Survey item

Formal strategic planning

FSP1 We have established strategic goals and have used them to drive decisions and actions throughout the agency

FSP2 We have conducted situational analyses of our agency’s strengths and weaknesses in relation to external challenges and opportunities

Logical incrementalism

LI1 Our strategy has evolved largely through negotiations with other stakeholders

LI2 Strategy has been made on an ongoing basis in our agency Performance

measurement PM1 We have set clear numerical targets and then actively monitored and

managed performance in order to achieve them PM2 We have used performance measures to track performance over time

Table 3. Descriptive statistics for responses to 2009 and 2013 surveys. Survey item Year n Min Max M SD

FSP1 2009 104 2 5 3.72 0.897 2013 62 1 5 3.71 1.01

FSP2 2009 104 1 5 3.42 1.04 2013 62 1 5 3.23 1.09

LI1 2009 103 1 5 3.17 0.830 2013 65 1 5 3.80 0.833

LI2 2009 103 2 5 3.68 0.795 2013 65 2 5 4.05 0.694

PM1 2009 103 1 5 3.47 0.948 2013 59 1 5 3.78 0.966

PM2 2009 103 2 5 4.10 0.774 2013 60 1 5 4.26 0.815

Formal strategic planning (FSP) 2009 102 1.50 5.00 3.57 0.813 2013 62 1.00 5.00 3.47 0.962

Logical incrementalism (LI) 2009 101 1.50 5.00 3.42 0.578 2013 65 2.50 5.00 3.92 0.639

Interaction (FSP*LI) 2009 101 2.25 25.00 12.36 3.932 2013 61 3.75 25.00 13.96 4.903

Performance measurement 2009 101 1.50 5.00 3.78 0.766 2013 59 1.00 5.00 4.02 0.793

Institution type 2009 104 1 5 2.60 0.795 2013 58 1 4 2.03 1.059

Population density 2009 104 40.75 6281.56 1854.10 1241.28 2013 58 87.18 6281.56 1901.34 1264.27

Percent contracted out 2009 88 0.00 100.00 26.26 33.55 2013 54 0.00 100.00 31.00 36.00

Fleet size 2009 104 9.00 366.00 84.56 80.91 2013 66 16.00 400.00 96.50 90.15

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agencies in the two survey cycles, OLS regression was used with clustered standard errors by transit identification number to correct for autocorrelation.

The variable for turbulence is coded as “1” for the responses to the 2013 survey and “0” for the responses to the 2009 survey. This variable captures the difference in performance management practices due to environmental turbulence. The following control variables are included in the analysis: insti-tution type, population density, percentage of services contracted out, and fleet size. Institution type is a categorical variable, where “1” is coded for a unit of state or local government, “2” for a subsidiary unit of a larger transit agency, “3” for an independent public agency, and “4” for an independent nonpublic entity. This coding is based on the assumption that independent agencies would have more discretion in formulating strategy than those that operate as a unit of local government, and can thus change their operations more readily under turbulence.

Population density, defined by population (in 1,000 s) per square mile, is used as another control variable. This measure serves as a proximate control for stakeholder pressures and “transit intensity” in terms of both service pro-vision and utilization. Percentage of service delivery contracted out addresses the ease with which an organization can alter its strategy, as well as its internal capacity to cope with turbulence. This is consistent with the “hollow state” concept, which associates lower levels of flexibility and internal capacity with public agencies that contract out their services (Milward & Provan, 2000). It is more difficult for an organization to take drastic measures, change its strategy, or alter resource allocation when contractors are involved as stakeholders. The more important the role of the contractors, the more difficult would it be to bring about changes. Hence, agencies with a higher percentage of services contracted out are expected to engage more in logical incrementalism and less in formal strategic planning. Finally, fleet size, measured in terms of number of vehicles available for service, represents the size of the transit agency. Larger transit agencies may be less flexible than smaller ones in changing their services.

Limitations

This article is limited by its use of retrospective perception-based surveys ask-ing transit managers to remember their organization’s strategic management practices up to four years in the past. Thus, the perceptions of the managers may or may not be reflective of the actual use of these practices in their respective organizations. The article relies on taking the average of the responses to two items to construct the dependent variables, whereas a more robust approach would have been to use confirmatory or exploratory factor analysis with more items for each of these variables. Such an analysis was not possible because of data restrictions.

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Other historical events, such as a change in general perceptions in the tran-sit industry regarding the effectiveness or popularity of formal strategic plan-ning, logical incrementalism, and performance measurement practices might also have occurred independently of the recession, but the article does not account for this. One example is the 141st Transport Cooperative Research Program (TCRP) report, published in 2010, which before the period under scrutiny in this article already emphasized the increased use of performance measurement in transit agencies (Ryus, 2010). In addition, possible turnover in the leadership of the repeat respondents between the four years of the study might have impacted the use of these practices. The article also ignores time as a factor that characterizes formal strategic planning and logical increment-alism. Organizations can engage in long- or short-term formal strategic planning, or can rely on logical incrementalism in the long run instead of using this framework in the short run only during turbulence.

Results and discussion

Formal strategic planning

Turbulence is not found to have a statistically significant impact in either direction on the use of formal strategic planning, as shown in Table 4. These results fail to provide support for H1, and the assertions made by scholars such as Parnell, Lester, and Menefee (2000), as none of the three models in Table 4 shows a significant impact of turbulence or of any of the control variables, including type of institution, population density, and percentage of services contracted out, on formal strategic planning. The descriptive stat-istics in Table 3, in fact, show slight decreases in the mean of formal strategic planning in the later survey, but this difference is not statistically significant in the regression results presented in Table 4.

Notwithstanding the weaknesses in the data, which may have failed to capture the difference in formal strategic planning, rapidly changing environ-ments and ensuing difficulties might have left little room or time to make

Table 4. OLS regression results for use of formal strategic planning under turbulence. Formal strategic planning

(1) (2) (3)

Turbulence −0.106 (0.139) −0.080 (0.152) Institution type −0.128 (0.109) −0.144 (0.113) Population density −0.00005 (0.00007) −0.00004 (0.00007) Percent contracted out 0.002 (0.002) 0.002 (0.002) Fleet size 0.0003 (0.001) 0.0003 (0.001) n 136 164 136 R2 0.036 0.004 0.039

Notes: Unstandardized coefficients; robust standard errors shown in parentheses; *p < 0.05, **p < 0.01, ***p < 0.001.

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broader strategic decisions. Additionally, since strategic goals are mostly long- term, it is unlikely that they would change due to short-term disturbances in the environment. A volatile environment would make it difficult to extract meaningful information out of chaos, which would restrict the ability of transit managers to revise their strategies based on questionable information (Fredrickson & Iaquinto, 1989; Mintzberg, 1973). Hence, the agencies princi-pally stuck to what they had been doing, in terms of these practices, in the preceding relatively stable period, rather than increase their use of formal strategic planning.

Logical incrementalism

Table 5 (Model 3) shows increased utilization of logical incrementalism. None of the control variables except institution type seems to have an impact on the use of logical incrementalism. The negative coefficient for institutional type suggests that agencies that have more discretion in formulating strategy engage in logical incrementalism to a lesser degree. This is expected, since agencies that are units of general-purpose local governments or subsidiaries of larger transit agencies will need to engage in more stakeholder negotiations because of their dependence on other entities, as compared to independent organizations that are not as reliant on others and can make their own decisions.

These results support H2 along with the assertions made by scholars such as Miles et al. (1978) and Mintzberg (1994), who stress the need for flexibility and political negotiations during periods of crisis. These results suggest that transit agencies tend to increase the use of logical incrementalism by 0.457 units during turbulence. This represents an important increase of 0.79 standard deviations (based on the 2009 standard deviation), or a strong effect size. Model 2 further supports the significant impact of turbulence on logical incrementalism even without the control variables.

In periods of growth, transit agencies can make their own decisions and devise strategy without substantial stakeholder consultation, due to resource

Table 5. OLS regression results for use of logical incrementalism under turbulence. Logical incrementalism

(1) (2) (3)

Turbulence 0.502*** (0.094) 0.457*** (0.107) Institution type −0.217** (0.065) −0.147* (0.063) Population density 0.00004 (0.00005) 0.00002 (0.00004) Percent contracted out −0.002 (0.002) −0.0009 (0.002) Fleet size 0.0003 (0.0005) 0.0002 (0.0005) n 139 166 139 R2 0.085 0.144 0.187

Notes: Unstandardized coefficients; robust standard errors shown in parentheses; *p < 0.05, **p < 0.01, ***p < 0.001.

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availability as well as a relatively high level of performance and credibility. As their customary resources dwindle, and their performance decreases due to the lower levels of ridership during recession, the agencies need more support from stakeholders to remain up and running. The ever-changing circum-stances would force these agencies to keep their strategies flexible, not only to adjust to new challenges and information, but also to keep up with the changing demands and perceptions of other stakeholders, such as social ser-vice agencies, colleges and universities, other institutions, and even private firms that might constitute niche markets that could be served by transit operators.

Interaction of strategic planning and logical incrementalism

Table 6 provides support for H3, as the approach that blends formal strategic planning and logical incrementalism is positively and significantly related to environmental turbulence. Model 3 shows that organizations typically make 1.423 units, or 0.362 standard deviations, more use of the interaction of formal strategic planning and logical incrementalism during turbulence. This represents a small to medium-size effect. The low R2 suggests that the inde-pendent variables have only a limited impact on the variation of this measure. None of the control variables has a significant impact on the blended approach.

These results support a general increase in strategizing by transit agencies under turbulence, as supported by Eisenhardt and Martin (2000) and Teece (2007). Environmental turbulence may have forced these agencies to rethink their existing strategies and make quick changes based on emerging realities while retaining their focus on broader organizational goals and viability. This resonates with Meier and O’Toole (2009), who found that leaders of public schools shifted resources toward the core organizational functions when faced with turbulence. In doing so, the schools took funding decisions (at least tem-porarily) based on logical incrementalism under turbulence rather than follow

Table 6. OLS regression results for use of interaction between formal strategic planning and logical incrementalism under turbulence.

Formal strategic planning logical incrementalism

(1) (2) (3)

Turbulence 1.604* (0.702) 1.423* (0.794) Institution type −1.214* (0.501) −0.967* (0.524) Population density −0.0001 (0.0003) −0.0002 (0.0003) Percent contracted out 0.0007 (0.012) 0.004 (0.011) Fleet size 0.002 (0.004) 0.002 (0.004) n 136 162 136 R2 0.068 0.0317 0.089

Notes: Unstandardized coefficients; robust standard errors shown in parentheses; *p < 0.05.

520 PASHA & POISTER

their original strategic plans. Similarly, transit agencies may maintain their overall strategic goals and formal analysis, but increasingly rely on negotia-tions and keep their plans flexible for any changes in strategy.

Performance measurement

Turbulence is found to have a positive and statistically significant impact on the use of performance measurement, as shown in Table 7. None of the con-trol variables except fleet size was found to have an impact on the use of these practices. The positive coefficient for fleet size suggests that larger agencies tend more to use performance measurement than agencies with smaller fleets. Larger agencies may have more access to resources, information, and trained personnel to adopt performance measurement systems.

These findings provide support for H4. The results suggest a 0.40 unit or 0.52 standard deviation increase in the use of performance measurement during turbulence in Model 3, and remain consistently significant in Model 2. This represents a medium effect size. Transit agencies were found to increase the use of performance measurement under turbulence. This reflects the kind of management that is required under crisis. Since turbulent conditions are markedly different than stable ones (O’Toole & Meier, 2014), managers need to be more vigilant and monitor their performance more intensively in a rapidly changing environment. Managers are also challenged to make quick adjustments to their strategy based on changes in performance, which relates to an increased use of logical incrementalism; instead of relying on formal strategic plans to strengthen long-term viability, managers are operating in more of a firefighting mode to mitigate problems and address short-term issues.

In sum, the results show a tendency of managers to regularly monitor performance and modify their strategy based on stakeholder negotiations to achieve short-term targets that are established or adjusted under turbulence. This is an especially interesting finding, given the emphasis in the literature

Table 7. OLS regression results for use of performance measurement under turbulence. Performance measurement

(1) (2) (3)

Turbulence 0.235* (0.123) 0.400*** (0.110) Institution type −0.082 (0.074) −0.014 (0.075) Population density 0.00008 (0.00005) 0.00006 (0.00005) Percent contracted out −0.0004 (0.002) 0.0006 (0.002) Fleet size 0.002** (0.0005) 0.001** (0.0005) n 134 160 134 R2 0.062 0.021 0.124

Notes: Unstandardized coefficients; robust standard errors shown in parentheses; *p < 0.05, **p < 0.01, ***p < 0.001.

PUBLIC PERFORMANCE & MANAGEMENT REVIEW 521

on the mutual reliance of formal strategic planning and performance measurement processes (Moynihan, 2008; Poister, 2010). These findings, however, suggest that managers increased their focus on performance measurement while largely maintaining the existing levels of formal strategic planning. This might suggest a possible branching away of the performance measurement process from formal strategic planning under turbulence. Managers may place greater emphasis on performance indicators that reflect stakeholder priorities (e.g., decreased operating cost) instead of indicators that reflect strategic priorities (e.g., increased ridership) and deal with the new realities.

Conclusion

Scholars generally recommend that organizations should modify their operations based on changes in their environment in order to better mitigate new threats and take advantage of new opportunities that come along with environmental disturbances (see Kennerley & Neely, 2003; Mintzberg, 1973). Following the arguments of O’Toole and Meier (2014), this article introduces organizational turbulence as a factor that might impact the use and adoption of performance management systems. Data derived from transit agencies in small and medium-sized cities are drawn upon to explore the extent to which transit agencies altered their strategy formulation methods and performance measurement practices when they found themselves caught up in the economic recession of 2009–2012.

Support was not found for a significant increase in the use of formal stra-tegic planning, but use of logical incrementalism, a blended approach, and performance measurement are all found to have increased significantly. These results indicate that transit agencies generally continued with their formal strategic-planning activities, but also made increased use of logical increment-alism during the Great Recession. O’Toole and Meier (2014), in keeping with dynamic capabilities logic (Eisenhardt & Martin, 2000; Teece, 2007), argue that in turbulent times managers look outwards toward the environment for information rather than focus on their own internal processes. The analysis provides support for this claim, as outward-looking practices (e.g., negotiating with stakeholders) was found to increase under turbulence. This article adds to the growing literature on performance information use (see Kroll, 2013; Moynihan, 2008) by proposing environmental turbulence as yet another factor that impacts the use of performance measurement in public organizations.

Practitioners may benefit from these results by learning about the changes in general industry-wide practices under recession. Previous studies (e.g., Poister et al., 2013) have shown that performance measurement and the blended approach of logical incrementalism and formal strategic planning

522 PASHA & POISTER

help increase organizational performance in stable times. Taken together, these results show that the response of transit agencies to the crisis are in the right direction, as these agencies continued using formal planning but became more flexible and relied more heavily on stakeholder negotiations in their ongoing decision-making.

There are, however, certain theoretical questions that remain unanswered. The article does not address the argument about whether changes made to performance management systems under the Great Recession are temporary, as suggested by Ammons, Smith, and Stenberg (2012), or long-lasting and fundamental, as proposed by Martin, Levey, and Cawley (2012). A follow- up study examining strategy formulation and performance measurement practices in the current period of recovery, may help answer this question. Another important question concerns the results generated by changes in management practices under turbulence: Does increased emphasis on logical incrementalism and performance measurement, in addition to a continued emphasis on formal strategic planning, lead to higher levels of organizational performance in turbulent conditions? This argument strikes at the heart of Pandey’s (2010) concern about the trade-off between long-term and short- term goals during cutback. This article calls for the need to develop normative theory backed by empirical evidence to guide public organizations in adopting formal strategic planning and/or logical incrementalism approaches in the wake of turbulence.

Notes on contributors

Obed Pasha is a lecturer in the School of Public Policy at the University of Massachusetts, Amherst.

Theodore H. Poister is a Professor of Public Management in the Andrew Young School of Public Policies at Georgia State University.

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Appendices

Appendix 1

Appendix 2

Appendix 3

Independent samples t-test of nonresponders and responders to 2013 surveys. n M t Sig. SE

Population Nonresponders 168 212,127 −1.210 0.227 38346 Responders 63 258,533

Fleet size Nonresponders 168 91.01 −0.419 0.676 18.200 Responders 63 98.52

Passengers per capita Nonresponders 168 13.41 0.104 0.917 1.987 Responders 63 13.20

Independent samples t-test of responders to 2009 and 2013 surveys. n M t Sig. SE

Population 2009 100 248,212 −0.138 0.890 74,619 2013 63 258,533

Fleet size 2009 100 83 −1.126 0.262 13.736 2013 63 98

Passengers per capita 2009 99 13.23 0.012 0.990 1.827 2013 63 13.20

Independent samples t-test of nonresponders and responders of 2009 survey. n M t Sig. SE

Population Nonresponders 132 232,964 −0.266 0.791 57,408 Responders 100 248,212

Fleet size Nonresponders 132 96 0.887 0.376 13.518 Responders 100 83

Passengers per capita Nonresponders 132 13.60 0.200 0.841 1.876 Responders 99 13.20

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