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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical considerations and hypotheses
  5. Data, research methodology and analysis
  6. Results
  7. Discussion and conclusions
  8. References

Studies into the impact of top manager change on organization performance have revealed inconsistent findings. Using longitudinal data over a 12-year period on football organizations, we test for the short-term and long-term effects of manager change in comparison to the tenures of incumbent top managers. We find that long incumbent tenures are associated with performance far above the average. But when looking at change events, contrary to theoretical expectations, we find that change in the short term leads to a brief reprieve in poor performance only for performance to deteriorate in the long term as underlying weaknesses once again take hold. Our findings reveal the illusion of a short-term reprieve and the long-term consequences of this illusion. We map several implications for research and practice from our work.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical considerations and hypotheses
  5. Data, research methodology and analysis
  6. Results
  7. Discussion and conclusions
  8. References

Manager changes are critical decisions that can shape organizational performance (Miller, 1991). However, inconsistent findings (Greiner, Cummings and Bhambri, 2002) have led to three contrasting theories that explain the association between manager change and organizational performance. First, scapegoating theory posits that manager change does not affect performance (Sakano and Lewin, 1999). According to this theory, managers are replaced as a ritual to signal that boards of directors have taken action to address poor performance. Yet, Khanna and Poulsen (1995) find that managers are rarely to blame for poor performance as they do not deliberately make value-destroying decisions. Scapegoating then occurs as a CEO or other executives protect their own positions by blaming and removing certain managers. Changing managers, therefore, may not resolve underlying organizational weaknesses (Sakano and Lewin, 1999).

Second, vicious circle theory postulates that manager change harms performance because replacement events disrupt well-established processes and bring instabilities and tensions that deteriorate performance (Grusky, 1963). The disruptive nature of manager change is exacerbated by the loss of firm-specific knowledge which further deteriorates performance in the short to medium term (Greiner, Cummings and Bhambri, 2002). The contrast between these two theories raises two research questions. (1) Does short-term performance improve after manager change? (2) If no improvement occurs, is there evidence of a vicious circle of perpetual change and underperformance?

Third, tenure and life-cycle theories (Hambrick and Fukutomi, 1991) suggest that a new manager develops new processes, a new team and a fresh strategy that will improve long-term performance as they learn and make necessary adaptations. Yet Hambrick and Fukutomi (1991) suggest that managers over time become dysfunctional in an inverted U-shaped relationship with performance. Studies propose that organizational performance increases for the first 8–10 years of tenure but decreases thereafter as managers apply old formulae to new conditions (Miller and Shamsie, 2001). However, Henderson, Miller and Hambrick (2006) suggest this pattern might depend on industry. This contrast raises two further research questions. (1) Does tenure deteriorate performance over time? (2) In the long term, does performance increase dramatically to justify manager change?

These three contrasting theories suggest that short-term and long-term differences might explain inconsistent findings, yet studies rarely discriminate between the two. We address this problem. Our assumption is that while scapegoating and vicious circle theories explain the short-term impact of manager change, tenure and life-cycle theories explain the long-term impact. We analyse football organizations registered in England's Premier League between 1992 and 2004 to this end. Research on manager change in sports organizations inspired succession research in the general management literature (Giambatista, Rowe and Riaz, 2005), and the choice of industry confers several advantages. Specifically, objectives in football organizations are clearer than those of a conventional firm (Koning, 2003); measuring and assessing the success (or otherwise) of manager change is less ambiguous (Brady, Bolchover and Sturgess, 2008); and competing organizations possess similar structures, objectives and industry constraints (Audas, Dobson and Goddard, 2002).

Our contributions to theory are twofold and relate directly to the discovery of an illusion of short-term recovery, which is not accounted for in existing theories. First, our results challenge the theory that short-term disruption weakens performance further before learning and adaptations by new managers eventually restore performance. Our results suggest that, as new managers implement a wave of changes to signal their arrival, a modest positive disruption results, which creates an illusion that the fundamental problems of the organization have been resolved. But the lull is illusionary because performance in the long term afterwards deteriorates at the same rate as prior to dismissal. Consequently, a vicious circle emerges not because of disruption but because of the illusion of a return to better performance and the delayed effect of hasty adaptations. Vicious circle theory fails to account for these negative consequences.

Second, our results challenge Hambrick and Fukutomi's (1991) theory that new managers will positively impact performance immediately upon taking office and will continue to do so until dysfunction sets in. The short-term reprieve can fool new managers into believing that problems are solved, leading them to misinterpret organizational conditions. Consequently, long-term weaknesses again take hold and these again compromise performance. The learning process must then restart as managers realize the depth of problems is far greater. Tenure theories then require revision to better account for the learning experienced by new managers. Our results do not vindicate scapegoating theory either because performance improvement is possible from manager change but the firm suffers from the loss of firm-specific knowledge when existing managers are replaced.

These results explain why arguments of scapegoating emerge, why vicious circles appear, and why the efficacy of change may not follow the assumptions of tenure and life-cycle theories. Thus, researchers benefit from empirical evidence reconciling the short-term and long-term impact of manager change with contrasting theories of the effects of change. Practitioners benefit from research evidence of the consequences of change, the illusion of a short-term reprieve and the long-term consequences of this illusion.

Theoretical considerations and hypotheses

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical considerations and hypotheses
  5. Data, research methodology and analysis
  6. Results
  7. Discussion and conclusions
  8. References

Top managers exhibit specialist skills depending on their role. Football managers for example may be likened to senior operating officers.1 An operating officer is typically a CEO's greatest asset; while CEOs focus on external and strategic activities, an operating officer focuses on internal operating matters, solves workplace problems, detects early signs of marketplace change, and nurtures talent (Hambrick and Cannella, 2004), much in the same way as a football manager. Football managers are more outward-facing, however, with much emphasis given to their external and strategic involvement. The football manager in summary is responsible for strategy (playing style and player organization), tactics (measures taken for or during individual games), talent nurturing, human capital acquisition, media management, competitor analysis and managing marketplace change.

Research into manager change in sports and general business organizations informs our work.2 Our theoretical development is framed by scapegoating, vicious circle, and tenure and life-cycle theories of manager change. In scapegoating theory, the loss of firm-specific knowledge acts as a mechanism to explain why performance might not improve after manager change. Vicious circle theory regards disruption as well as loss of firm-specific knowledge as mechanisms to explain further deterioration in performance after manager change. Tenure and life-cycle theories view learning and adaptations made by managers over time as mechanisms to explain the inverted U-shaped performance exhibited by new and current managers. These mechanisms form the basis of our discussion.

For reference, we define ‘short term’ as ten games after the change event (e.g. Audas, Dobson and Goddard, 2002). We define the ‘long term’ as 30 games afterwards. This classification is appropriate because the average tenure of football managers is now approximately 1.38 years (Bridgewater, 2007) (which, assuming one game each week, would amount to about 70 games); because of the existence of the league calendar; and because in a full season a team will only play 38 league games. We use points earned to define performance because it determines league position, on which managers are principally judged (Audas, Dobson and Goddard, 2002; Brady, Bolchover and Sturgess, 2008).

Manager change in sporting organizations

Football is increasingly seen as a domain from which managers can learn new techniques for organization, people and change management. Brady, Bolchover and Sturgess (2008) cite the case of Guus Hiddink and South Korea as a perfect illustration of this point. Hiddink was the Dutch-born coach of South Korea from early 2001 to late 2002. Following tremendous success at the 2002 World Cup, the Korean Government specified the need for Korean society to apply ‘the Hiddink-style leadership to the operation of government [and] corporations’ (Brady, Bolchover and Sturgess, 2008, p. 54). Korea Telecom also highlighted Hiddink's ability to coax an effective blend of flair and work-rate from his team as a ‘crucial lesson’ (Brady, Bolchover and Sturgess, 2008, p. 54). Hiddink organized his team to deliver success with much less apparent ability than many of their defeated opponents. His style was characterized in Korea as clear vision, steady implementation, discipline and introduction of openness, global standards and fair competition (Brady, Bolchover and Sturgess, 2008). Despite a poor start to his tenure, characterized by media criticism and poor results, the turnaround in long-term performance displayed at the World Cup cemented his place in South Korean history.

Table 1 summarizes studies that examine manager change in a sports context. These studies suggest a pattern of short-term decline followed by long-term recovery (Giambatista, 2004; cf. McTeer, White and Persad, 1995), much like Hiddink's Korean tenure. These studies posit disruption as a reason for short-term fluctuation and time needed to learn and implement appropriate individual and firm-level change as the basis for long-term performance.

Table 1. Summary of manager change research in a sporting context
StudySport industryFindingsReasonsGap
Grusky (1963)Major League baseballNegative relationship between change and performance creates a vicious circle of continual decline (poor performance triggers manager change which intensifies poor performance)Perpetual cycle of decline driven by increased organizational instability brought on by changeImplies short-term deterioration after manager change leading to further panic which triggers additional change
Brown (1982)National Football LeagueFollowing change, organizational effectiveness and performance do not increase; change better viewed as ritual scapegoatingDisruptive effect of change offered as rationale for this outcomeDoes not identify whether performance would have improved over time (after disruption settles)
McTeer, White and Persad (1995)Major League baseball, National Football League, National Basketball Association and National Hockey LeaguePerformance immediately after a manager change increased but in the full season after change showed no improvementPossible shock effect but underlying weaknesses seem unrelated to the previous managerLong-term effect is unknown beyond the next season or year
Audas, Dobson and Goddard (2002)English football organizationsOrganizations that change managers within the season underperform over the following three monthsMinimum three months needed to train, renew, improve and align human capital with the demands of the new manager, and to adapt the organizationPerformance effect if any after this three-month period remains unclear
Bruinshoofd and ter Weel (2003)Dutch football Premier LeagueChange preceded by decline in performance but followed by some improvement in performance; control group shows that if the manager had not been changed, performance would have improved more rapidlyDisruption and loss of knowledge may explain result; sacking manager a costly way of signalling a problem with the team; manager is a scapegoatDoes not distinguish between short-term and long-term effect
Koning (2003)Dutch football Premier LeagueTeam performance does not improve when a manager is fired and new managers perform worse than their fired predecessors in several instancesDisruption and loss of knowledge may explain result; since results do not improve after a change of manager, the board of a team seems to intervene for other reasons (fan and media pressure)Does not distinguish between short-term and long-term effect
Giambatista (2004)National Basketball AssociationHambrick and Fukutomi (1991) learning effects supported for first 3 years; performance declines from 7 years onwards. In-season succession disrupted performance; between-season succession not related to first-year performance. Effects stronger for coaches than ownersManagers need time to learn and teach after change; increase performance but eventually experience stagnationImplies initial short-term disruption followed by recovery. Tenure unusually long versus short average tenure in football organization
Rowe et al. (2005)National Hockey LeagueGiving managers more time leads to better performanceNew managers need time to lead organization reconstructing, learn the right initiative to undertake and the right ways to implement those initiativesDoes not preclude retaining managers given their unique knowledge of firm-specific conditions. Short-term effect unclear. Implies long-term return from tenure or change
Dios Tena and Forrest (2007)Spanish top-division footballModest positive differences to match results in the short term; but effect derived almost entirely from improvement at the home stadium. No change in away performance detectedNew coach does not bring technical solutions to the weaknesses of the teamLong-term performance remains unclear

Research on football organizations supports these assertions. Audas, Dobson and Goddard (2002) found that manager change causes further underperformance over the following three months. Thus, a minimum of three months is needed to train, improve and align human capital with the strategies of the new manager. Bruinshoofd and ter Weel (2003) and Koning (2003) found that performance either worsened under new managers or would have recovered faster without change. Although both studies overlook short-term and long-term consequences, they do suggest that disruption and loss of firm-specific knowledge render manager change an ineffective strategy, symptomatic of scapegoating and vicious circle theories.

Disruption and organizational instability might shape the short-term effect of change, but its long-term effect is shaped by how new managers learn and adapt the organization to match new strategies going forward. Unless a manager (incumbent or otherwise) receives more time at the helm to address the underlying causes of poor performance, disruption and increased organizational instability can trigger a vicious circle of decline (Grusky, 1963).

Managers and organizational performance over time

Hambrick and Fukutomi (1991) theorize seasons in the tenure of managers where managers make immediate major organizational changes (reflecting their own paradigms3) followed by more gradual, incremental initiatives that revise the fit between the organization and its environment. However, Hambrick and Fukutomi (1991) posit that the initial years of performance improvement give way to decline as the manager becomes increasingly psychologically disengaged. Miller (1991) and Miller and Shamsie (2001) place the decline event at around 8–10 years of tenure. Decline occurs because managers' successes over time lead them to reinforce their preconceptions, maintain ‘tried and true’ strategies, and shape organization initiatives around their own biases (Mellahi and Wilkinson, 2004). Decline then triggers manager change. However, these views assume that immediate radical change is feasible and desirable, that short-term performance will improve accordingly, and that in the long term a failure to adapt is inevitable.

Hambrick and Fukutomi (1991) acknowledge that exceptions to their model may exist. Exceptions can be caused by industry, competitive and organizational pressures for top managers to remain vigilant. Greater manager vigilance and adaptation is expected to persist in environments that have moderate pressures in the form of competitive rivalry and where owners, overseers or stakeholders place considerable emphasis on a top manager to perform (Hambrick and Fukutomi, 1991). These conditions approximate those of the football industry (Koning, 2003). Owner, fan and media pressure to perform can drive top managers to remain vigilant and adaptive to sustain long-term performance as their tenure increases. Ironically, these same pressures induce dismissal, which sports studies have associated with scapegoating due to little evidence of improvement (Audas, Dobson and Goddard, 2002; Bruinshoofd and ter Weel, 2003; Koning, 2003).

Some turbulence in organizational performance is inevitable over time. But Simsek (2007) argues that long tenures generate a well-seasoned individual who possesses idiosyncratic knowledge of the organization and its industry following years of learning and experience. These incumbent managers are then best placed to restore performance. Their personal knowledge of organizational resources, human capital and strategy enables better identification and correction of weaknesses, improving performance in time (Simsek, 2007). In football organizations, Bruinshoofd and ter Weel (2003) found that performance would have improved more rapidly had organizations retained rather than replaced their managers. Brady, Bolchover and Sturgess (2008) discovered that teams with better talent quotients are regularly outperformed by teams with similar or worse talent quotients. Accordingly, managers' idiosyncratic knowledge of team talent and players' contextual talent can enable superior long-term performance.

Coaxing talent further depends on shaping and refining strategy, culture and training schemes. Kor (2003) argues that longer tenures increase managers' knowledge of firm resources and improve opportunity identification. In a football context, increased tenure augments managers' knowledge of players' strengths and weaknesses, which enables managers to better match players to task demands. Indeed, giving managers more time has been associated with better long-term performance as time is needed for managers to nurture, train and shape human capital (Giambatista, 2004; Rowe et al., 2005). In the long term, therefore, changes are more likely to enhance performance when building on existing formulae, structures and resources than entirely new ones (Sastry, 1997). Thus:

H1: Longer manager tenure is positively related to performance.

Short-term performance after manager change

Manager change is meant to stimulate adaptive behaviour from incoming managers. New managers are expected to reinvent strategy and redeploy resources to reinvigorate the organization and restore its excellence at core activities (Virany, Tushman and Romanelli, 1992). However, proponents of scapegoating theory report that little meaningful change occurs after succession (Sakano and Lewin, 1999). Scapegoating theory suggests that, in underachieving organizations, blame for poor performance is placed on a top manager for being responsible for strategy or its execution (Khanna and Poulsen, 1995). However, Khanna and Poulsen (1995) found that such managers are scapegoats for greater endemic problems, finding no evidence that past managerial decisions are the root causes of organizational failings. Consequently, change gives rise to disruption which masks underlying problems, thereby causing further damage to performance in the short term (Brown, 1982), as is indicative of vicious circle theory.

In sports organizations, Audas, Dobson and Goddard (2002) reported that, in a three-month period after change, performance continued to struggle. Audas, Dobson and Goddard (2002) speculated that a minimum of three months is then needed to train, renew and improve human capital to align strategy with the views of the new manager. Time is also needed to establish a suitable system to coax exceptional performance from what may be a group of ‘ordinary’ people (Brady, Bolchover and Sturgess, 2008). But, prior research in the management literature suggests that new managers typically need six to 18 months to initiate major change (Gabarro, 1987), because their ability to execute these strategic changes in the short term is limited by organization, industry and learning constraints (Henderson, Miller and Hambrick, 2006). Whilst manager change is still performed in the hope of a ‘shock effect’ that promotes internal change (Bruinshoofd and ter Weel, 2003), new managers lack firm-specific knowledge of prevailing organizational conditions, making it harder for them to instigate appropriate change (Zhang and Rajagopalan, 2004).

Gabarro (1987) found that initial organizational change carried out by a new manager is rarely strategic but rather is based simply on paradigm realignment in an effort to rapidly move away from past regimes. Initial changes and adaptations are therefore informed by the biases of the individual (Hope Hailey and Balogun, 2002). Gabarro (1987) noted that after this initial realignment an in-depth period of diagnosis and transformation takes place. The lack of a strategic approach at the start of the change programme will likely disrupt performance in the short term as initial changes are unlikely to resolve underlying problems.

Disruption to routines and structures can create instability in the short term. Thus, new managers may be unable to readily alter strategy, resource allocation and behaviour, further deteriorating performance. Grusky (1963) and Gamson and Scotch (1964) put forward a vicious circle theory in which poor performance triggers manager change, but its disruptive effect further damages performance which leads to further manager change and ultimately a spiral of decline. This trend appears evident in the rapidly declining average tenures of football managers, now less than 1.38 years (Bridgewater, 2007), with some recent manager tenures shorter than eight months. Since time is needed for new top managers to learn about cause–effect relationships relevant to their organizations, the fact that they enter with little firm-specific knowledge exacerbates this information asymmetry, and delays any impact on performance.

An illustration of this effect can be seen with the appointment of Jurgen Klinsmann as manager of the German national team. Klinsmann immediately introduced new diet, fitness and coaching techniques (Brady, Bolchover and Sturgess, 2008). Yet short-term performance worsened with the team winning only two matches in seven. Overseers, fans and media were ritually calling for his dismissal at this point, symptomatic of vicious circle theory. Thus:

H2: Manager change is negatively related to short-term (ten-game) performance.

Long-term performance after manager change

Appropriate changes to strategy and organizational design over time can reverse failure, but manager change may take longer than 1 year to effect strategic change and increase performance (Giambatista, Rowe and Riaz, 2005). Miller and Shamsie (2001) argue that paradigm realignment between the new manager and the organization can in fact take 2–4 years. This can result in a performance lag of 3–5 years depending on industry (Henderson, Miller and Hambrick, 2006). At best this time-frame nears the end of the average tenure of football managers. At worst it is far beyond their typical tenure.

Incumbent managers will already have prior knowledge of resources, employees, organizational weaknesses and conditions supporting or obstructing change. Manager change removes that intellectual capital, necessitating that additional learning processes take place (Kor, 2003). But new managers tend to be better attuned to the external environment than incumbent managers such that manager change can increase the likelihood of transformative change to correct performance problems (Romanelli and Tushman, 1994). Transformational change needs time to establish new patterns of activity, standards and strategies across the organization (Hope Hailey and Balogun, 2002). This effort improves when the new manager receives ample time to learn, decipher and correct organizational weaknesses. Time also enables the top manager to orchestrate, nurture and gain support for new initiatives (Simsek, 2007). When new managers receive sufficient time to effect strategic change, we can surmise that long-term performance should be positive after the initial short-term disruption subsides (Giambatista, 2004).

This long-term dynamic is further illustrated by the fortunes of Jurgen Klinsmann. Klinsmann managed to transform the German playing style and had the courage to continue his strategy despite considerable criticism. Following great success at the 2006 World Cup, the German business community lauded Klinsmann as a ‘true modernizer’, an ‘ideal leader of change’ and a symbol of ‘flexible, innovative corporate leadership’ (Brady, Bolchover and Sturgess, 2008, p. 55). With time, he successfully isolated Germany's problems, implemented appropriate change and successfully learnt how to coax contextual talent from his players to restore performance. Thus:

H3: Manager change is positively related to long-term (30-game) performance.

Data, research methodology and analysis

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical considerations and hypotheses
  5. Data, research methodology and analysis
  6. Results
  7. Discussion and conclusions
  8. References

Industry

We examine football organizations in England's Premier League. In 2007, Premier League football organizations generated in excess of £1.4 billion in revenue with total wages approaching £1 billion; they contributed about £480 million in taxes to the British government; and the growth rate of Premier League revenue is around 4% per annum (Deloitte, 2007). Failure can cost organizations tens of millions in lost revenue. For example, failure to qualify for the UEFA Champions League can cost £10 million in lost revenue from UEFA without accounting for separate sponsorship and advertising revenues (Deloitte, 2007). Absolute failure in the Premier League (relegation to a lower league) creates a revenue gap of approximately £56 million to £70 million (Deloitte, 2007). Against this backdrop, football managers now have a tenure of less than 2 years, falling from 3.12 years at the start of the Premier League in 1992 to an average of 1.89 years in 2006–2007 with statistics placing the average tenure for 2007–2008 at 1.38 years (Bridgewater, 2007). The average salary for a Premier League football manager remains high and reflects the turbulence of the profession. In 2007, the average salary of the top five managers in England was around £3.26 million with salaries increasing rapidly in general (Taylor, 2007).

The choice of industry is advantageous. First, it minimizes the random effect of industry forces as they are homogeneous to each organization; organizations possess relatively straightforward structures; organizations cannot acquire each other and nor can their shareholders or top managers have major holdings in other organizations; and each organization is constrained in its ability to acquire new resources at short notice. Since these factors remain constant across organizations, the internal validity of findings increases (Giambatista, Rowe and Riaz, 2005; Pitcher, Chreim and Kisfalvi, 2000). Second, generally, the chairman as the key decision-maker holds the power to recruit and dismiss the single football manager (the top manager). The chairman acts as the chief owner and exercises budgetary power whilst the football manager is responsible for running the team, training and nurturing employees, human capital acquisitions, team strategy and performance.

Sample and data set

We obtained longitudinal data from a secondary, commercial source, AFS Enterprises Ltd, on football organizations that competed in the English Premier League from its inception in 1992 through to 2004. The data contain the results of each competitive game played by every team (including points gained, goals scored, goals conceded and league position), the manager in charge, length of tenure and changes in manager. Twenty organizations compete in the Premier League every season. The data contain the results of more than 5000 football matches. No data were collected for games in any other competition as these results are independent of results in the Premier League and would raise problems in carrying out analysis among teams.4

Measures

We call manager change an event, and we divide our event window into three periods. Ten matches after the change reflect the short term and 30 matches afterwards capture the long term. We also capture ten matches before the change as these data can be used for comparison to examine for scapegoating. We denote this event window as 10–10–30, which is justified given the current average tenure of managers and that the annual league calendar consists of 38 games. We use points earned as the performance variable because it is transparent (Audas, Dobson and Goddard, 2002; Brady, Bolchover and Sturgess, 2008), has considerable heritage in sports-based manager change research (Giambatista, Rowe and Riaz, 2005) and determines league position.5 Teams are ranked on the basis of the total points they accumulate by the end of the season, which unlocks various revenue rewards such as prize money, competition qualification and greater merchandising, sponsorship and advertising opportunities.

We measure the performance of a given team by two proxies. The first is a proxy for current performance, calculated as the points earned during the current match. We denote current performance at time (match) t by pt, which is the number of points earned at time t. The second proxy measures cumulative performance. We measure cumulative performance by a simple cumulative point variable, inline image. Because pt≥0, the cumulative performance inline image trends upwards. However, as pt is highly unpredictable, this upward trending is likely to behave like a random walk with drift:

  • image(1)

where ɛt is the disturbance term. Given an initial value of inline image, a random walk process can be written in terms of a deterministic trend process:

  • image(2)

To enable cross-team comparison, we need to specify a benchmark team against which all other teams are compared. We assume a benchmark team earns 4 points every three matches (i.e. successively wins, draws and loses matches). Thus, this team scores 4/3 points every match on average. The cumulative performance of this benchmark team at any period t, ctb, will then trend at around 1.33t. Thus, its data-generating process is exactly linear in time.

The cumulative abnormal or excess performance of any given team can be calculated as the difference between the cumulative score of that team and the cumulative score of the benchmark. Thus, the cumulative excess performance at time t, denoted ct, is given by

  • image

Replacing by their respective values, we obtain

  • image(3)

where β0=δ– 1.33, inline image and inline image. The time series of cumulative performance (ct) will therefore exhibit no trend if the team in question is not outperforming the benchmark. But it will have a positive (negative) trend when the team in question is outperforming (underperforming) the benchmark. We call this cumulative measure excess performance.

Testing the effect of change on cumulative performance

To test for the cumulative effect of manager change we use ct as the dependent variable. In order to test for the effect of manager change, we extend model (3) to capture any possible changes in the evolution of performance during the period surrounding the event. We propose the following model:

  • image(4)

where νt is a disturbance term and t is the time trend.

We use dummy variables to estimate the effect of manager change. The dummy Bt equals 1 for the ten matches before the change and zero otherwise. This dummy captures the possible poor performance preceding the change and can be used for comparison to examine for scapegoating. The second dummy, At, equals 1 for the ten matches following the change and zero otherwise. This dummy captures the short-term changes in cumulative performance. The third dummy, Lt, captures the long-term performance effect. It is equal to 1 between the 11th and 40th matches following the change.

The betas here are parameters to be estimated and help quantify the effect of manager changes. Testing that cumulative performance is affected in the short and long term amounts to testing for the significance of β2 and β3 respectively. The overall cumulative performance (outside the event) is represented by β0. Finally, β1 measures the possible fall in performance before manager change.

Testing the effect of change on current performance

The dependent variable for current performance, pt, is the number of points earned at match-time t. The dependent variable takes three possible states, namely a win, a draw and a lose. The team earns 3, 1 and 0 points for winning, drawing and losing a match respectively. We assign these values to our dependent variable pt, namely winning (pt=3), drawing (pt=1) and losing (pt=0). We model this variable through a probit model following prior studies (Audas, Dobson and Goddard, 2002; Dios Tena and Forrest, 2007).

The probit model

As the dependent variable has three states, we use an ordered probit to model current performance of teams. In a probit model we focus primarily on the probability that the dependent variable, m, takes one of the three values. The probabilities are given by

  • image

where Φ is the normal cumulative distribution function and μ is a threshold parameter. A significant estimate of a threshold parameter indicates significant difference between two adjacent states. Probit models are estimated by the maximum-likelihood method.

The dependent variable is essentially ‘explained’ within the index equation, given by

  • image(5)

where the betas are coefficients to be estimated. The dummy variables B, A and L are defined as before except that they are now indexed for team i at time t. The other independent variables are pit–1, pit–2 and pit–3, which represent the points earned in the previous three matches respectively. These three variables represent the possible momentum effect of the very recent performance of a given team. The variable MAit is the moving average of the past ten matches for team i at time t. This variable represents the current average performance of a given team. For both current and cumulative performance, under the statistical null hypothesis of no effect, β1, β2 and β3 will not be significantly different from zero.

We use a pooled probit model for two reasons. First, given that we have 28 teams, estimating and analysing 28 separate probit results would be difficult to decipher. Second, since we are interested in the effect of manager change regardless of when the team played in the Premiership and regardless of whether the team made a change during the sample period, by pooling data we can capture information that would otherwise have been discarded had we estimated individual models. The disadvantage is that we will only examine the overall effect because pooling the data discards the individual effect in the panel estimation. However, we know of no methodology that actually allows individual effects in a probit framework. Still, since we are not directly interested in the teams and only interested in the effect of manager change, this should not be too disadvantageous.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical considerations and hypotheses
  5. Data, research methodology and analysis
  6. Results
  7. Discussion and conclusions
  8. References

Features of Premier League organizations

We begin by examining features of the teams that played in the Premiership during the sample period. Table 2 shows the number of manager changes, the change ratio (changes per 100 matches), the mean of MA (ten-match moving average of points earned), the standard deviation of MA, the cumulative excess points earned by the end of the sample period or by the time the team left the Premiership, and the relative cumulative excess points (obtained by dividing the excess cumulative points by the number of matches played in the Premiership). Table 2 is divided into four panels. Within each panel, the teams are ranked by cumulative excess performance. Panel (A) shows the top nine teams which were present in the Premiership throughout the entire sample period.6 The table shows some association between the number of management changes and team performance. Except for Everton and Liverpool, lower frequency of manager changes seems to be compatible with higher average and cumulative performance, which provides support for Hypothesis 1. Data in panels (C) and (D), which contain teams that played less than 300 games in the Premiership, also provide support for Hypothesis 1. For example, Queens Park Rangers has the lowest percentage change and the highest cumulative abnormal points. In panel (D), the three top teams have only one change among them, while the bottom three teams have five changes. However, when examining the teams in panel (B), the picture is not so clear. The top three performing teams have more relative percentage changes than the lower teams.

Table 2. Summary statistics of team performance
 Matches in PremiershipNumber of manager changesChange ratioMean MAStd dev. MACumulative excess pointsRelative cumulative excess points
(A) Top teams
Manchester United46200.0001.9160.158345.6670.748
Arsenal46220.4331.7740.178229.6670.497
Liverpool46251.0821.5760.069155.6670.337
Leeds United46240.8661.5630.09794.6670.205
Chelsea46251.0821.3980.10287.6670.190
Aston Villa46251.0821.4470.06243.6670.095
Tottenham Hotspurs46261.2991.2430.104−18.333−0.040
Everton46240.8661.2980.106−38.333−0.083
Southampton46261.2991.1440.050−69.333−0.150
(B) Teams with more than 300 matches
Newcastle United36041.1111.7350.126117.6670.327
Blackburn Rovers33061.8181.7230.15458.6670.178
Sheffield Wednesday31041.2901.5080.121−3.667−0.012
Wimbledon32930.9121.3880.116−20.000−0.061
Manchester City31341.2781.4270.157−26.667−0.085
West Ham United38220.5241.1380.102−40.667−0.107
Middlesbrough31730.9461.1640.080−47.000−0.148
Coventry City36841.0871.1670.059−79.000−0.215
(C) Teams with more than 200 matches
Queens Park Rangers21520.9301.4070.0750.0000.000
Nottingham Forest25141.5941.3680.074−18.000−0.072
Sunderland20141.9901.1760.119−44.333−0.221
Leicester City24052.0830.9990.183−63.333−0.264
Derby County20131.4930.9150.245−66.333−0.330
(D) Teams with more than 100 matches
Sheffield United13800.0001.4060.132−6.333−0.046
Crystal Palace13510.7411.3220.100−15.333−0.114
Charlton Athletic16600.0001.1390.089−16.667−0.100
Norwich City17821.1241.2410.101−17.667−0.099
Bolton Wanderers12910.7750.8600.154−37.333−0.289
Ipswich Town19221.0421.1740.149−45.333−0.236

The correlation coefficient between percentage manager change and relative cumulative performance is −0.38, while the correlation between percentage manager change and the mean MA is −0.24. These statistics provide evidence of the negative association between manager change and overall team performance. In addition, the correlation coefficient between percentage manager change and the standard deviation of MA performance is 0.10. The analysis suggests that teams with a higher rate of manager change tend to be more volatile in terms of performance.

Tenure and performance

We run two regressions to investigate the effect of tenure on both average and cumulative performance. Because the teams did not play the same number of matches, we use a relative measure of manager change, called the change ratio. This represents the number of managers a given team changes every 100 matches. We run the following two regressions:

  • image

where inline image is the sample-time mean of the ten-match MA for team i; CRi is the change ratio; RCUMi is the relative cumulative excess points; and ɛi is a disturbance term. We run two sets of tests, one including only the top nine teams and another including all teams. The results are summarized in Table 3.

Table 3. Regression effect of tenure on average and cumulative performance
 Top nine teamsAll teams
Coefficientt statisticp valueR2Coefficientt statisticp valueR2
Average performance
Constant1.95019.5900.000 1.46513.7810.000 
Change ratio−0.524−5.1340.00179.02%−0.118−1.2780.2135.91%
Cumulative performance
Constant0.7255.6500.001 0.1801.8240.080 
Change ratio−0.590−4.4890.00374.28%−0.178−2.0810.04714.28%

When analysing the top nine teams, the results suggest strong negative association between performance and manager change. The coefficients are significant (p≤0.01) and the R2 are in excess of 70% for both average and cumulative performance. However, estimating the model using the full 28-team sample weakens the results. For both average and cumulative performances, the association is weaker and is insignificant for average performance but significant at the 5% level for cumulative performance. Moreover, there is a possibility that the coefficients are compromised by endogeneity in the regressor (CR) since manager change can also depend simultaneously on the team's performance. Although the assumption that managers' decisions and actions determine performance is the basis for leadership and succession research, we still cannot discount the possibility that the performance of staff and so the organization can be simultaneously influenced by other forces independent of the manager (Giambatista, Rowe and Riaz, 2005). However, given limitations in data availability, it is not possible for us to obtain any instrumental variables with which to correct the possibility of such bias. Still, we cautiously state that statistically significant evidence exists herein of a negative relationship between manager change and both short-term and long-term performance. The results indicate that more frequent manager change is detrimental to average and cumulative performance, providing support for Hypothesis 1.

Explaining cumulative performance

The results of the cumulative performance effect are based on data from the top nine teams (those present throughout the entire sample period). Ideally, all available teams should be included in the analysis, but cumulative estimation results are only comparable if the teams played for the same period of time. This analysis will necessarily suffer from survivor bias so the results in this instance should be treated with caution. However, we do supplement the cumulative performance results with the ‘current’ performance analysis in which we use all available teams. Survivor bias is partially mitigated by these additional tests.

Table 4 presents the cumulative regression results. The parameter β0 shows how fast a team is improving relative to the average benchmark team. As suggested in Table 2, Manchester United is far ahead of the rest, followed by Arsenal and Liverpool. The worst teams are Southampton and Everton, which experience a downfall relative to the benchmark. Tottenham is evolving roughly at the same rate as the benchmark, although the coefficient is still positive and significant. The effects of ten matches before change, ten short-term matches and 30 long-term matches afterwards are captured by β1, β2 and β3. These measure the changes from the cumulative trend (β0) that occur during these specific periods.

Table 4. Regression results for cumulative performance
 ManUArsenalLiverpoolLeedsUChelseaAstonTottenhamEvertonSouthamp.
  • The teams are, respectively, Manchester United, Arsenal, Liverpool, Leeds United, Chelsea, Aston Villa, Tottenham, Everton and Southampton.

  • *

    Significant at the 5% level or lower.

α−22.778*0.100−16.570*1.642*−18.606*−1.601*−11.317*7.329*−0.751
β00.753*0.410*0.338*0.225*0.175*0.131*0.006*−0.099*−0.167*
β1 −0.109*−0.002−0.010−0.056*0.005−0.007*−0.015*−0.020*
β2 0.0020.0000.009−0.0110.001−0.003−0.001−0.002
β3 −0.111*0.019*−0.021*−0.046*−0.004−0.005*−0.017*−0.022*

Most teams experience a significant fall in performance in the period preceding manager change. However, there are three exceptions, namely Aston Villa, Leeds and Liverpool. The long-term period is interesting. In most cases β3 has the same negative sign and magnitude as β1. This is particularly true for Arsenal, Chelsea, Everton, Southampton, Tottenham and to some extent Leeds. Thus, Hypothesis 3 is rejected as a positive effect was expected. However, none of the short-term coefficients is significant. But since the periods before change and after the short-term time-frame (following change) are negative, significant and of the same scale (in the majority of cases), this suggests that during the short term the team's performance reverts to its overall (outside-event) performance (β0). For example, in the case of Arsenal, while its overall performance during normal times is 0.410, it is 0.301 before the change, goes up to 0.408 during the short term and down again to 0.299 after that. Thus, although the coefficient is insignificant, a short-term effect may exist. But in that context Hypothesis 2 would be rejected as the effect appears moderately positive, not negative as predicted. A value of zero for β2 simply means that the team temporarily goes back to its outside-event potential. Although most teams behave in a similar manner, there are two exceptions. For Aston Villa the effect is absent, while Liverpool is the only team that significantly increases its performance after the short-term period.

Overall, the average values for the period before change and the long-term period (β1 and β3) are −0.027 and −0.026 respectively. The associated t ratios are 1.99 and 1.87, both significant at the 5% level. These effects are significant, negative and of the same magnitude and so reject Hypothesis 3. In the long term after change, therefore, performance continues to be poor. The average value of the short-term coefficient (β2), however, is –0.001 with a t ratio of −0.317. This confirms the rejection of Hypothesis 2. Performance does not decrease in the short term after change. The results imply a short-term return to potential performance as the deviation from overall long-term performance (β0–β2) is minor compared to before the change (β1) and long term after change (β3).

Explaining current performance

We estimate the pooled probit model based on data from 28 teams. Ten teams have less than 120 matches in the Premiership and were excluded from the sample. The included teams have sample sizes varying from 129 observations to 462 (we lose ten observations for using moving averages). However, the majority of teams played more than 300 matches (Table 2). Thus, we have an unbalanced panel with a total of 9193 team–time observations.

The results for current performance are shown in Table 5. All coefficients are significant with the exception of the coefficient of At. The pre-change, short-term and long-term effects are captured by Bt, At and Lt respectively. The short-term coefficient (At) is non-significant and has the same interpretation as the one given for the cumulative performance analysis above and confirms the rejection of Hypothesis 2. The coefficients of Bt and Lt are negative and significant, which suggests that the probability of good performance by a typical team decreases just before the change and in the long term afterwards. This result rejects Hypothesis 3. A given team is therefore expected to undergo a significant fall in performance before the change, a return to the potential performance during the short term and a fall of a smaller magnitude to the one before the change in the long term afterwards.

Table 5. Probit estimation results
 CoefficientStandard errort statisticp value
  1. N=9193.

Intercept−0.4790.058−8.2600.000
Threshold (μ)0.7280.01166.5990.000
Bt−0.2630.038−6.9670.000
At0.0560.0501.1110.266
Lt−0.1220.030−3.9910.000
pt–10.0140.0091.6460.100
pt–20.0140.0062.2610.024
pt–30.0130.0081.6240.104
MAt0.6200.04214.8200.000

To see the effect of manager change on the probability of performance by a given team before the change, ten games after the change and 30 games afterwards, we calculate the marginal effect of the three dummy variables. To do that, we set the other regressors (pt–1, pt–2, pt–3 and MAt) equal to their mean values in equation (5). We then calculate the marginal effect as the probability when the dummy in question equals zero (i.e. normal times) minus the probability when the dummy in question equals one (i.e. during one of the three events). The marginal effect on the probability to win, draw and lose is given in Table 6.

Table 6. Marginal effects
 Probability, lossProbability, drawProbability, win
Before9.99%−0.40%−9.59%
After short−1.99%−0.16%2.15%
After long4.51%0.05%−4.56%

The results in Table 6 suggest that on average, during the ten matches before the change, the probability of losing increases by almost 10% while the probability of winning decreases by about the same. In the short term, the marginal effect is small but goes in the opposite direction, with about 2% lower probability of loss and approximately 2% higher probability of winning. However, these effects are statistically insignificant. The long-term effect sees a reversal in performance. The probability of a loss increases by 4.51% compared to normal times, while the probability of a win decreases by 4.56%. In all cases, the marginal effect on the probability of a draw is relatively small. These results provide further evidence to reject Hypotheses 2 and 3.

A more detailed effect is given in Table 7, which shows predicted probabilities for a number of circumstances and for various team performances. The first three panels of Table 7 show the probabilities outside the 50-match event window (normal times). In each of these three panels we calculate the probabilities suggested by the estimated model for a team whose moving average is 0, 1, 2 and 3. The two extremes are for a team that lost all previous ten matches and a team that won all previous ten matches.

Table 7. Predicted probabilities
 Moving averageProbability, lossProbability, drawProbability, win
(A) Three consecutive losses
 00.6840.2020.114
10.4440.2780.278
20.2240.2640.513
30.0840.1740.743
(B) Three consecutive draws
 00.6690.2090.122
10.4280.2800.292
20.2110.2590.529
30.0780.1670.756
(C) Three consecutive wins
 00.6390.2220.139
10.3960.2830.321
20.1880.2500.562
30.0660.1530.781
(D) Probabilities before change
 00.7710.1580.071
10.5490.2540.197
20.3100.2820.409
30.1320.2170.651
(E) Probabilities after change (short term)
 00.6640.2110.125
10.4220.2810.297
20.2070.2580.535
30.0760.1640.760
(F) Probabilities after change (long term)
 00.7260.1820.092
10.4920.2690.239
20.2610.2740.464
30.1040.1940.702

First, small differences exist between the three cases in the first three panels, reflecting the domination of the moving average. Probabilities of wins (losses) are higher (lower) when a team has three previous consecutive wins, compared with three previous draws and three losses, respectively. While these differences are marginal, the difference that one moving-average point makes is large. A team that moves from 0 to 1 sees its probability of winning increase from 0.114 to 0.278, while the probability of loss decreases by roughly the same amount (panel (A)).

In the last three panels of Table 7 we set pt–1, pt–2 and pt–3 equal to zero to see the combined effect of the moving-average performance and the probability of performance before, after and long after the manager change. While there is a change in probabilities for increasing moving-average performance, the expected change for the three event windows is also found. For example, for a team that has recently been doing very well (e.g. MA=2), we see that the probability of a loss is 31% in the ten weeks before the change, goes down to 20.7% during the ten weeks after the change, and then goes up again to 26.1% during the following 30 weeks. At the same time the probability of a win is 40.9% before, going up to 53.5% during the short-term period, and then dropping to 46.4%. To check that our results are robust to the selection of window size, we repeated the estimation using 10–10–50 (ten matches pre-change, ten after change and 50 matches thereafter) and 20–10–50. The results were virtually identical.

In summary, the test results lead us to conclude that Hypothesis 1 is accepted such that longer manager tenure is positively related to performance. This is further evidenced by declining performance as the number of manager changes increases. We reject Hypothesis 2 that, in the short term after change, a decline in performance occurs. Rather, the results across our tests imply that short-term performance returns to the long-run potential of the organization, indicating no particular decline or improvement. Lastly, Hypothesis 3 is rejected as we find evidence that, in the long term after change, organizations continue to suffer from poor performance indicating that much longer tenures are needed to improve results. Overall, these results suggest that manager change to rapidly improve performance in the short and long term is a flawed strategy.

Discussion and conclusions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Theoretical considerations and hypotheses
  5. Data, research methodology and analysis
  6. Results
  7. Discussion and conclusions
  8. References

This study sought to understand the short-term and long-term performance effects of manager change. We draw several conclusions. First, lower rates of manager change are associated with higher average and cumulative performance. This is consistent with our expectation, following exceptions in Hambrick and Fukutomi's (1991) theory, that giving managers more time at the helm can benefit organizations when managers are pushed by powerful stakeholders to learn and remain vigilant to sustain performance. Sacrificing managers may then be a mistake for two reasons: (1) although short-term performance does not worsen, it does not greatly improve either; (2) in the long term after change, performance deteriorates again. The efficacy of manager dismissal versus manager persistence is therefore questionable. Some deterioration in performance is inevitable over time as human capital resources deteriorate and competitors improve but our findings suggest that manager change compromises recovery.

Second, the expectation that turbulence after manager change would disrupt the organization and further weaken performance did not materialize. However, our results do not exonerate proponents of change from arguments of scapegoating. We find that some reprieve in performance can occur in the short term since performance does not decline at the same rate prior to dismissal; but no material turnaround occurs either. Since periods of performance before and after the short term are negative, significant and mostly of the same magnitude, it implies that in the short term organizations revert to performing in line with their expected long-term potential. Some form of ‘shock effect’ appears to influence the short-term performance of the organization (cf. Audas, Dobson and Goddard, 2002; Bruinshoofd and ter Weel, 2003), yet the efficacy of a dismissal strategy for short-term recovery remains poor.

Third, the change to negative performance in the long-term period afterwards suggests that underlying organizational weaknesses once again take hold and new managers struggle to appreciate the problems afflicting the organization. Two possibilities account for this. The short-term reprieve creates an illusion that fools the manager into believing that organizational problems have been addressed and so the manager learns information that has little long-term value, thereby necessitating a longer learning curve; or, the learning process must restart again as the manager recognizes that much greater problems are endemic in the organization. The lack of recovery is consistent with scapegoating and vicious circle theories.

Implications for theory

The findings are indicative of flaws in Gabarro's (1987) theory of the value of initial organization changes that follow manager change. Not only are short-term changes suboptimal, they do not address the real problems of the organization either. Short-term adaptations create disruptions that temporarily suspend performance decline, but this suspension creates an illusion that masks greater weaknesses. Performance deteriorates again soon afterwards owing to delayed effects from these non-strategic adaptations. Gabarro's (1987) work fails to foresee the negative consequences of short-term experimentation. First, time allows managers to develop and apply idiosyncratic knowledge but change takes away that knowledge;7 second, new managers must then learn about the organization to put appropriate long-term solutions in place. By incorporating the illusion of short-term recovery, the non-strategic nature of short-term organizational adaptation, and the loss of firm-specific knowledge into scapegoating and vicious circle theories, these theories can better explain the short-term effects of manager change.

Similar weaknesses are present in Hambrick and Fukutomi's (1991) model because they assume that new managers have a positive impact immediately when taking post which continues until eventual dysfunction. This theory fails to account for the turbulence that occurs as new managers learn in a trial and error way of the faults in the organization responsible for duress and experiment with changes that imprint their style onto the organization. Our findings suggest that tenure and life-cycle theories need adjustment to account for an initial period of turbulence stemming from the disruption new managers cause as they make erroneous organizational changes and learn inappropriately. By accounting for these effects, tenure and life-cycle theories can then offer a more complete and balanced treatment of the process and outcomes of manager change.

Taken together, our results suggest that disruptive and illusionary effects caused by the short-term adaptations of new managers account for contradictions between the positive and negative effects of manager change and the perpetual spiral of decline that can then emerge. These results embellish vicious circle theory, and require tenure and life-cycle theories to reconsider the complexities of change. Exceptions to life-cycle theory exist whereby the effect of tenure is not purely concave owing to short-term disruption and long-term misdiagnosis and mistreatment that are brought about by failings in the new manager's learning. At present, life-cycle theory oversimplifies the case for change. The loss of firm-specific knowledge also provides an alternative explanation for the value of existing managers, and offers an alternative interpretation of scapegoating theory in this context.

Implications for management

A feature of the football industry is the constantly declining tenure of managers. Our results suggest that this is indicative of vicious circle theory. Sacking the manager precipitates further performance problems because the incoming manager does not understand the weaknesses of the organization. Despite the illusion of a short-term recovery, weaknesses again take hold and performance deteriorates again. Performance declines not because of the disruption itself but because the illusion of reprieve confounds errors made in diagnosing and treating organizational weaknesses. The answer to this is giving managers more time because we find that in general longer tenures are associated with better average and cumulative performance. At the minimum our findings reveal that managers do matter, but to matter positively they need time, far more than current average tenures. Misspecification of the performance consequences of manager change can lead to false hopes of a turnaround and inappropriate decision-making. If managers are to be replaced, mechanisms are needed to retain firm-specific knowledge and new managers must receive ample support to grow into their new roles when the brief reprieve diminishes.

In the short term, new managers can make rapid changes to playing style (strategy) and training schemes to tease out the intrinsic talent of employees. However, such changes should not result in overconfidence that organizational problems have been resolved. New managers need time to learn of the true faults that created poor performance in the first place, but this depends on managers undertaking a thorough process of diagnosis. In time, managers should sufficiently train, renew and improve the human capital of the organization. As Brady, Bolchover and Sturgess (2008) show, exceptional performance does not come from acquiring more expensive talent but rather from managers' ability to coax contextual talent from employees. Together with our findings, manager change will not rapidly rehabilitate performance and thus organizational stakeholders must be patient. Prematurely replacing managers will only worsen the organization's distress as it descends into a cycle of decline.

Longer incumbent tenures are associated with better average and cumulative performance. Thus, managers' performance should be evaluated in terms of progress on diagnosing and treating weaknesses prior to dismissal. Managers should receive sufficient time at the helm to demonstrate progress at overcoming the causes of poor performance. Only then can the tenure of a manager be accurately judged and a decision made as to whether manager change is strategic and in the best interests of the organization. When organizations become gripped by weaknesses, manager change masks these problems, which further exacerbates poor performance. The driver of manager change should be managers' ineffectiveness in tackling organizational weaknesses; otherwise change is simply an act of scapegoating and could result in a vicious circle developing. Longer tenures allow managers time to develop idiosyncratic knowledge; but change removes that resource and prompts a long recovery period as new managers struggle to learn about and adapt to the organization.

Limitations and future research directions

Several limitations affect our work. First, our findings may not readily generalize to other industries. Although football organizations are commercial, profit-making entities, they are influenced more readily by shareholder, fan and media pressure and face unusual industry and organization-level constraints. Second, we do not distinguish the type of manager change by insider versus outsider succession. Insider succession and retirement are rare events in the football industry, and thus limited data availability precludes such a test. Third, we do not account for other possible determinants of performance, such as human capital. The quality of coaching staff and the playing staff may affect performance. Still, Brady, Bolchover and Sturgess (2008) found that acquiring expensive talent alone does not automatically provide exceptional performance. Exceptional performance rather depends on the skill of the manager in unlocking individual player and team talents. But a greater wealth of talent might maintain performance during acute injury periods for example.

Fourth, the relationship between tenure and performance may be quadratic. A positive relationship between tenure and performance may only be to a point. At a certain level of tenure, diminishing returns may set in, but the short tenures of football managers prevent us from temporally capturing any downside effects of increased tenure. Fifth, prior expectations and alternative performance measures may affect the tenure of a manager. Such variables are difficult to model and a lack of data compromises such a test. Similar to prior studies, we appropriately assume that the board of football teams want their teams to be as highly ranked as possible, and failure to rank sufficiently highly is the chief reason for manager change (Koning, 2003). But expectations may accelerate or decelerate dismissal and therefore our inability to model expectations represents a limitation. We advocate further research to address these limitations.

Given the vicious circle that appears from our findings, it is highly unlikely that blame for poor performance is fully attributable to managers. New managers inherit organizational problems of which they have little knowledge. Time is needed to properly learn and diagnose these problems to make appropriate changes. If these managers are routinely replaced each time performance declines, then managers alone cannot be blamed for poor performance as the decline becomes perpetual. Breaking the cycle requires giving managers time. But at some point manager change may be necessary. Change should not result from performance duress alone but rather should result from persistent failure to respond to weaknesses underpinning performance duress. Further research is needed to resolve the time-frame required for managers to make a difference; criteria to establish when managers have failed to understand and treat organizational weaknesses; and mechanisms to safeguard firm-specific knowledge when manager change takes place.

Although we find some agreement with Hambrick and Fukutomi (1991) that manager change can provide new organizational impetus, we also find several differences. Longer incumbent tenures appear to render long-term benefits to the organization more readily than change. Exceptions depend on competitive, owner and media pressure on football managers to remain vigilant, and the presence of firm-specific knowledge. The football industry is characterized by short tenures and so we cannot verify this exception by testing for a curvilinear effect. On the other hand, exceptions might surface due to person–organization fit.8 Fit can exist when the new manager's prior knowledge is highly suitable to the circumstances of the organization, or the new manager's leadership style is particularly suitable. Accordingly, research is needed to identify exceptions to Hambrick and Fukutomi's (1991) life-cycle.

Our primary contribution is the discovery of illusionary effects. Such illusionary effects can lead to spirals of decline whereby performance seems to improve, falls again over a period of time, which leads to change, and subsequently the restart of this cycle. Far greater academic attention must be paid to this concept as it is currently not considered in existing theories of manager change. Without question, poor strategic decisions could occur due to illusionary effects, such as unnecessary manager change, commitment of significant financial resources and increases in debt to fund acquisitions. Future research agendas should prioritize these illusionary effects.

Footnotes
  1. 1We are grateful to an anonymous reviewer for this point.

  2. 2Although we cannot exhaustively review the literature here, Giambatista, Rowe and Riaz (2005) and Kesner and Sebora (1994) offer comprehensive literature reviews of manager change.

  3. 3We use the term ‘paradigm’ in line with existing studies (e.g. Hambrick and Fukutomi, 1991; Henderson, Miller and Hambrick, 2006). These studies posit that managers have fairly rigid views. While these can change, they do not change readily because managers' understanding of how to conduct organizations is shaped considerably through education and experience over time. Although Popperian theory specifies that paradigms are not rigid but change through falsification, for consistency we use the term in line with prior studies.

  4. 4Football organizations can take part in domestic and international cup competitions but these results are not included because not all teams gain access. Thus, the data would not apply evenly to the teams in our data set.

  5. 5A points-based model has limitations: league positions are often separated only by 1 or 2 points and in the event of equal points league position is separated by goal difference. The main problem is one of expectations. Teams can possess different expectations that may increase or decrease the likelihood of dismissal. Cup wins might mitigate dismissal as might current league position, but this is complex because expectations can change during the season owing to performance. Thus, it is difficult to model even static expectations. Still, our interest lies with the effect of manager change, not necessarily the reasons for it.

  6. 6These are Manchester United, Liverpool, Arsenal, Leeds, Chelsea, Everton, Aston Villa, Tottenham and Southampton. Leeds and Southampton are no longer ‘top’ teams due to relegation from the Premier League. However, this occurred after the time-frame of this data set and is immaterial to the data analysis.

  7. 7Some knowledge is retained in coaching staff but it is not as rich as that of the manager because of his broader understanding of players' contextual and intrinsic talents and team talent (Brady, Bolchover and Sturgess, 2008). Also, manager change normally leads to change in coaching staff as the new manager brings in his own people.

  8. 8We are grateful to an anonymous reviewer for this suggestion.

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  2. Abstract
  3. Introduction
  4. Theoretical considerations and hypotheses
  5. Data, research methodology and analysis
  6. Results
  7. Discussion and conclusions
  8. References
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Mathew Hughes is a Lecturer in Entrepreneurship at Nottingham University Business School. His research interests include top manager change and organizational performance, organizing for firm-level entrepreneurship, innovation, social capital and network behaviour. His work appears in such journals as British Journal of Management, Long Range Planning, Industrial Marketing Management and R&D Management.

Paul Hughes is a Lecturer in Strategic Management at Loughborough University Business School. His current research interests include strategic planning and improvisation in strategic decision-making, strategic resources, information processing and information use in organizations, the strategic management process within organizations, adherence to strategy and strategic failure. Paul's work has been published in widely regarded journals such as the Journal of Business Research, British Journal of Management and Industrial Marketing Management.

Kamel Mellahi is a Professor of Strategic Management at the University of Sheffield Management School. He has written extensively on change management, organizational failure, non-market strategies and international business strategies. He has authored over 50 papers that have appeared in such journals as Strategic Management Journal, Journal of International Business Studies, British Journal of Management, Journal of World Business and Long Range Planning.

Cherif Guermat is a Reader in Finance at the University of the West of England. His research interests include international business, risk management and asset pricing. Some of his work has appeared in Journal of World Business, Journal of Business Finance and Accounting, International Journal of Forecasting, Journal of Risk and Journal of Forecasting.