How hotel owner-operator goal congruence and GM autonomy influence hotel performance

Article (PDF Available)inInternational Journal of Hospitality Management 61:119-128 · February 2017with 502 Reads
DOI: 10.1016/j.ijhm.2016.11.008
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Abstract
A principal-agent relationship exists between hotel owners and the management companies which often operate their hotels. In addition, they both act as principals to a mutual agent, the hotel's General Manager, who is tasked with trying to achieve each parties' objectives. Extensive research on hotel management agreements which govern the owner-operator relationship has demonstrated that these objectives are often incongruent. However, the property-level managerial and performance implications of their goal incongruence has not been empirically examined. This study analyzes these issues using a matched sample of surveys from both owners and operators across 64 hotels operated under hotel management agreements. Using structural equations modeling, we demonstrate that owner-operator goal congruence positively impacts hotel performance and that this relationship is both mediated and moderated by the hotel General Manager's autonomy.
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To cite this article:
Hodari, D., Turner, M. J., & Sturman, M. C. (2017). How hotel owner-operator goal
congruency and GM autonomy influence hotel performance. International Journal of
Hospitality Management, 61(February), 119-128.
How hotel owner-operator goal congruency and GM autonomy influence hotel
performance
Demian Hodari*
Ecole hôtelière de Lausanne, HES-SO //
University of Applied Sciences Western Switzerland
Route de Cojonnex 18
1000 Lausanne 25
Switzerland
Phone: +41 21 785 11 11
Email: demian.hodari@ehl.ch
Michael J. Turner
The University of Queensland
UQ Business School
Brisbane, QLD 4072
Australia
Phone: +61 7 3346 8071
Email: m.turner@business.uq.edu.au
Michael C. Sturman
Cornell University
School of Hotel Administration
Ithaca, New York
United States
Phone: 607 255 5383
Email: mcs5@cornell.edu
*Corresponding author
Keywords: Hotel Management Agreement, Owner, Operator, Goal Congruency,
Performance, Autonomy
Acknowledgements: The authors appreciate the support of the hotel owners and members of
The Hospitality Asset Managers Association (HAMA) of Asia Pacific, Europe and the Middle
East & Africa, the European Hotel Managers Association (EHMA), and The Master
Innholders who graciously provided the data used in this study. The lead author also
acknowledges the support of the HES-SO // University of Applied Sciences Western
Switzerland which provided the funding for him to undertake this study.
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Abstract
A principal-agent relationship exists between hotel owners and the management companies
which often operate their hotels. In addition, they both act as principals to a mutual agent, the
hotel’s General Manager, who is tasked with trying to achieve each parties’ objectives.
Extensive research on hotel management agreements which govern the owner-operator
relationship has demonstrated that these objectives are often incongruent. However, the
property-level managerial and performance implications of their goal incongruence has not
been empirically examined. This study analyzes these issues using a matched sample of
surveys from both owners and operators across 64 hotels operated under hotel management
agreements. Our analyses demonstrate that owner-operator goal congruence positively
impacts hotel performance and that this relationship is both mediated and moderated by the
hotel General Manager’s autonomy.
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1. Introduction
Hotel owners frequently contract hotel management companies to operate their hotels
through formalized hotel management agreements (HMA) (deRoos, 2010; Melissen, van
Ginneken, & Wood, 2016). Separating hotel ownership and operations through an HMA is
said to benefit both parties. Owners are able to invest in hotel real estate and access the
professional operating expertise of hotel management companies. In turn, these operators can
generate important income streams, expand any brands they may have, and earn profits,
without having to invest in the underlying real estate (Sohn, Tang, & Jang, 2013).
Although both parties have a vested interest in the hotel’s success, their different
sources of income (see Turner & Guilding, 2010b for a review), risk profiles (Eyster, 1988),
and investment strategies (Turner & Guilding, 2014) mean that they often have misaligned
goals (Schlup, 2004; Turner & Guilding, 2013). Such goal incongruence is emblematic of the
well-established agency problem (Eisenhardt, 1989; Jensen & Meckling, 1976) whereby an
agent (i.e., operator) may not always act in the principal’s (i.e., hotel owner) best interest,
especially if their interests are in conflict (Mitchell & Meacheam, 2011). In order to reduce
goal incongruence and improve firm performance, theory suggests that in a single agency
scenario a principal will expend effort to monitor, control and/or influence its agent’s
decisions and actions (Eisenhardt, 1989).
Hotels under management agreement are not, however, subject to a traditional single
agency relationship. Instead, they involve a complex, tripartite, ‘multiple agency’ (Child &
Rodrigues, 2003) relationship between the hotel’s owner, management company, and General
Manager (GM). Within this scenario the GM acts as an agent to two principals - the hotel’s
owner and its operator (Hodari & Sturman, 2014) - while the latter is also the owner’s agent
(Dev, Thomas, Buschman, & Anderson, 2010). As such, there are potential agency problems
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at two distinct levels of the relationship between the two principals (owner and operator)
and between each of them and their mutual agent (the GM).
Hotels operated under a management agreement thus provide a unique context within
which to study managerial and organizational performance implications emanating from a
‘multiple agency’ scenario. Understanding the role of the GM is particularly important as
Child and Rodrigues (2003) specifically note that while the agency theory literature has
principally focused on the relationship between ownership and corporate management, it has
largely ignored the second control relationship between corporate management and others in
the firm (i.e., GMs) who execute its plans and policies.
Although GMs are consistently considered to be fundamental to a hotel’s success (e.g.,
Hodari & Sturman, 2014; Kim, Park, & Wen, 2015), the impact that the two principals’ goal
congruence and control efforts have on GMs’ decision making, and their hotels’ performance,
has not been empirically examined. In fact, while owner-operator goal congruence issues have
been systematically studied with regard to HMA negotiations and contracts (Beals & Denton,
2005; deRoos, 2010; Eyster, 1997), researchers have begun to note the lack of similar
knowledge about the implications of goal divergence and congruence once the management
agreement has been signed and the hotel is operating under this arrangement (Guilding, 2006;
Melissen, et al., 2016).
This study seeks to make several contributions towards addressing this knowledge
gap. First, we examine how owner-operator goal congruence relates to hotel performance. In
this relationship the pivotal role of GMs is recognized as carrying potentially significant
implications. This is because GMs are typically responsible for day-to-day decision-making
as they implement the hotel owner’s and/or operator’s strategic initiatives. Independently,
both owners and management company executives impact GM autonomy (Hodari & Sturman,
2014; Takeuchi, Shay, & Li, 2008). Autonomy can also be influenced by conflicting
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objectives and demands from multiple superiors (Kahn, Wolfe, Quinn, Snoek, & Rosenthal,
1964; Rizzo, House, & Lirtzman, 1970). We thus investigate whether different degrees of
owner-operator goal congruence have distinct relationships with GM autonomy. Both
autonomy (Braadbaart, Van Eybergen, & Hoffer, 2007) and conflicting demands (Tubre &
Collins, 2000) can impact managerial effectiveness. We therefore examine the link between
GM autonomy and hotel performance. The mediating and moderating roles of GM autonomy
with owner-operator goal congruence on hotel performance are also hypothesized and tested
through our unique matched sample of surveys obtained from both owners and operators
across 64 hotels. Our study also contributes to the agency theory literature by specifically
examining the managerial and organizational performance implications emanating from the
different relationships in a multiple rather than single agency scenario.
2. Literature Review and Hypothesis Development
2.1. The Agency Problem
An agency relationship arises when there is a contract whereby one party (the
principal) appoints another party (the agent) to perform some service on its behalf. However,
because principals and agents often have very different and/or conflicting goals, agents
frequently act in ways which are not necessarily in their principal’s best interest (Eisenhardt,
1989; Zhang, Lawrence, & Anderson, 2015). These agency problems commonly arise when
ownership and management functions are separated (Schulze, Lubatkin, Dino, & Buchholtz,
2001).
Such agency problems may be mitigated to some extent through the monitoring of
agent activities (Heide, Wathne, & Rokkan, 2007). This is difficult for principals, however,
when they suffer from a large degree of information asymmetry vis-à-vis their agent because
this limits their ability to evaluate their agent’s decisions and actions (CuevasRodríguez,
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GomezMejia, & Wiseman, 2012; Sharma, 1997). In such situations, the principal’s
involvement in their agent’s decision making may help to control and/or influence the latter’s
choices and actions and thus their potential for opportunistic behavior.
Agency theory has almost exclusively investigated such behavior-based control
mechanisms in single agency settings involving only one agent and one principal (see Cuevas
Rodríguez, et al., 2012). However, due to strategic alliances and new organizational forms,
there is an increasing prevalence of organizational arrangements involving multiple principals
and agents (Child & Rodrigues, 2003). ‘Multiple agency’ problems arise because agency can
exist at several levels of the relationships between principals, agents and an agent’s key
manager. This also often produces two sets of control relationships because an agent can
report to two principals. As a result, the clear hierarchical lines and formalized decision-
making authority used to achieve top-down operational control and influence in single agency
situations are less clear and likely less effective (Guthrie, Xiao, & Wang, 2008). Child and
Rodrigues (2003) thus question the applicability of previous agency research about the nature
and control of the agency problem in such scenarios and call for empirical work in ‘multiple
agency’ organizational arrangements. Hotels operated under management agreement provide
a unique context within which to study this phenomenon.
2.2. Hotel Management Agreements, Goal Congruence and Agency
The traditional scenario whereby a hotel owner engages a GM results in a single
principal-agent situation (Panvisavas & Taylor, 2008). An HMA, however, implies multiple
principals and agents since not only do owners and operators both act as principals to a single
agent (the GM), but because the operator is also the owner’s agent (Dev, et al., 2010).
Although the GM is usually an employee of the management company, given the position’s
responsibilities and the HMA reporting structure, they are typically responsible to both the
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owner and operator. As a result, the GM is effectively the primary agent acting on behalf of
both principals (Hodari & Sturman, 2014). HMAs therefore create a myriad of opportunities
and incentives for multiple agents to shirk on their efforts; they require extensive and
expensive monitoring by principals and are, unsurprisingly, considered to be the most
problematic of all operating concepts in the hospitality industry (Schlup, 2004).
Studies of HMAs have regularly demonstrated that owners and management
companies have specific and conflicting demands and expectations with regard to their
respective roles, responsibilities and objectives (e.g., Beals & Denton, 2005; Eyster, 1997;
Turner & Guilding, 2013). For example, the vast majority of operator fees are derived as a
percentage of the hotel’s sales and they may spend resources to generate these even if the
owner does not receive a corresponding increase in profit (Turner & Guilding, 2013).
Operators are also strategically focused on the reputation of their brands and hotel-level
decisions may support this at the owner’s expense (deRoos & Wiseheart, 2016). Furthermore,
because they do not share in the corresponding profit, operator decisions may not be aligned
with increasing the property’s real estate value even though asset value appreciation is of
paramount importance to owners (Dev, et al., 2010). As such, operators may invest the hotels’
financial resources in ways that strengthen the brand’s standards and reputation even though
they may not increase the value of the owner’s underlying investment. There is also a
potential ‘horizon problem’ (Turner & Guilding, 2013) because operators tend to emphasize
customer relationships and long-term success of their business while owners are more likely
to have a short-term focus that emphasizes payback and return.
Implications of the divergent interests of owners and operators, and the challenges
arising from this split between ownership and management, have been studied and discussed
most extensively with regard to the contractual relationship achieved through the HMA,
including, for example, the establishment of specific clauses to better align their interests
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(Beals & Denton, 2005; deRoos, 2010; Eyster, 1997; Schlup, 2004). The increasing demand
of owners to have more say in property-level operational and managerial decisions (Beals &
Denton, 2005), as well as pressure for HMAs to include performance-based incentive fees,
guarantees and more generous termination clauses (Bader & Lababedi, 2007; deRoos, 2010;
Gannon, Roper, & Doherty, 2010)demonstrate not only an acknowledgement that the two
parties’ divergent goals require better alignment, but also that as owners they must closely
monitor and control their operators, especially because owners have the ultimate burden to
ensure that their hotels are properly managed (deRoos & Wiseheart, 2016).
Researchers have recently begun to empirically investigate some of the capital
expenditure (Turner & Guilding, 2010a), human resources (Gannon, et al., 2010) and
managerial (Hodari & Sturman, 2014) implications resulting from the owner-operator split.
These studies have found that the two parties’ conflicting objectives often create challenges
for the management company to implement operational and strategic decisions. These
challenges are often due to increased owner influence (see Beals & Denton, 2005; Eyster,
1997). Owners’ influence, however, extends beyond the corporate boardroom and contract
negotiations; they also influence property-level decisions and performance (Gannon, et al.,
2010; Xiao, O'Neill, & Mattila, 2012), which can reduce operator incentive fees (Schlup,
2004). This has led operators to complain that if their management fee is contingent upon
performance then, “shouldn’t they be given the right to manage the hotel free from the
owner?” (Goddard & Standish-Wilkinson, 2002, p. 8).
As the nexus between owner and operator, GMs are highly subject to the challenges
which arise from their split (Guilding, 2006). Low goal congruence should have a particularly
strong impact on GMs as they act as agents to both owner and operator. This is because goal
incongruence between multiple principals often creates conflicting mandates for agents
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(Buckley & Chapman, 1997) who thus face “wrenching choices among the legitimate
interests of multiple principals” (Shapiro, 2005, p. 279).
Greater goal alignment between principals, meanwhile, suggests that agents will be
less conflicted about what to do, and thus be more effective. One reason for this is that aligned
principals are more likely to send one clear management message to their mutual agent,
thereby reducing the conflict which normally arises when one receives incompatible job
demands from multiple superiors (Kahn, et al., 1964). Similarly, their agent is more likely to
receive a more cohesive and explicit set of tasks and directives, thereby reducing ambiguity
concerning his/her role (Rizzo, et al., 1970). This is important since alliances and outsourcing
can increase a hospitality manager’s level of both ambiguity and conflict (Hodari,
Waldthausen, & Sturman, 2014), both of which have been repeatedly found to decrease
managerial performance (see Tubre & Collins, 2000). Thus, greater goal congruence should
mean that their mutual agent is less divided about which principal to serve since his/her
actions are more likely to simultaneously align with each of the principals’ goals.
Increased goal congruence between hotel owners and operators should therefore result
in a more consistent, cohesive and effective set of decisions with regard to the hotel’s
management. Given this, and the importance of GMs to hotel success, we hypothesize that:
H1: Owner-operator Goal Congruence is positively associated with Hotel
Performance.
2.3. Autonomy and the Hotel General Manager
When multiple principals’ performance objectives are different, or in conflict, then
their mutual agent’s decisions cannot be simultaneously in all principals’ best interests
(Shapiro, 2005). To overcome this, agency theory suggests that each principal will be
incentivized to invest resources into monitoring their agent in order to exert additional control
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and thus better align their agent’s interests with their own rather than those of the other
principal. Active monitoring of an agent’s behavior may, however, decrease managerial
effectiveness and firm performance (Eisenhardt, 1989; Jensen & Meckling, 1976).
While they are formally in charge of their hotel and are responsible for achieving
property-level objectives, GMs of hotels operated under management agreement are
accountable to both the owner and corporate executives in their management company’s
hierarchy (Corgel, Mandelbaum, & Woodworth, 2011). GM autonomy has been found to be
significantly less in chain-managed hotels than in independently-managed hotels where there
is only one principal (i.e., owner) (Hodari & Sturman, 2014). GM autonomy is thus likely to
be contingent on the amount of goal congruence between owners and operators because
greater congruence suggests a reduced need for each party to seek to influence and control the
GM. As such, we hypothesize that:
H2: Owner-operator Goal Congruence is positively associated with GM
Autonomy.
Greater monitoring also erodes the agent’s autonomy to make important decisions
without control, approval and/or interference from higher hierarchical levels (Brock, 2003;
Ouakouk et al, 2014). This is important because the discretion that autonomy provides
managers with can improve their operational effectiveness (Brousseau & Glachant, 2002),
resource allocation decisions (Gong, Shenkar, Luo, & Nyaw, 2007), new product and service
development (Peteraf & Reed, 2007), and firm performance (Yan, Chong, & Mak, 2010). As
such, decreased autonomy can often reduce both managerial and firm performance
(Braadbaart, et al., 2007; Langfred & Moye, 2004). We thus hypothesize:
H3: GM Autonomy is positively related to Hotel Performance.
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Given the overall importance of goal congruence for hotel performance, although we
expect that owner-operator congruence will be associated with greater GM autonomy, we do
not expect GM autonomy to fully capture the effect of such congruence. While goal
congruence should be positively related to hotel performance through its influence on GM
autonomy, goal congruence should also affect hotel performance through other means. As
such, we predict:
H4. The effect of owner-operator Goal Congruence on Hotel Performance will be
partially, but not fully, mediated by GM Autonomy.
In fact, given the importance of goal congruence for the successful performance of a
hotel, we expect that the positive effects of this congruence will be greater than just its direct
and mediated effects. We expect that goal congruence will be more effective when the GM
simultaneously has the autonomy to act. That is, the potential value from the goal congruence
can be better unleashed when the GM has the ability to act on those goals and achieve the
desired results. As such we expect that in addition to its hypothesized direct (hypothesis 1)
and mediated (hypothesis 4) effects, goal congruence’s relationship to performance should
also be moderated by autonomy. Specifically, we posit the following hypothesis:
H5: GM Autonomy will moderate the effect of owner-operator Goal Congruence
on Hotel Performance, such that the positive effect of owner-operator Goal Congruence
on Hotel Performance should increase with higher levels of GM Autonomy.
Figure 1 illustrates the different relationships between goal congruence, autonomy and
performance.
--- Insert Figure 1 about here ---
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3. Methods
3.1. Sample
Online surveys were distributed to hotel owner, manager and asset management
associations during 2015. These included the Hospitality Asset Managers Associations
(HAMA) of Asia Pacific, Middle East & Africa, and Europe, the European Hotel Managers
Association, the Master Innholders, and HOFTEL. Our aim was to have both the hotel’s
owner and manager (as management company representative) answer the survey so that we
could generate matched response pairs for each hotel. Not only did this provide a useful way
to avoid common method bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003) but also
allowed us to specifically examine goal congruence in these hotels.
We asked respondents to answer the questionnaire designated for them (with GMs
instructed to respond from the management company’s perspective) and to forward a link
with the other party’s questionnaire to their counterpart (i.e., an owner forwarded it to the
hotel’s GM or vice-versa). Completed questionnaires from a total of 112 management
companies and 89 hotel owners (or their asset manager) were collected.
1
This resulted in 64
matched pairs where both a GM and an owner responded for the same hotel. There were 48
GMs whose response could not be matched with an owner and 25 owners whose responses
were not matched with a GM. A total of 201 individuals thus completed our survey, of which
128 formed the 64 matched pairs.
In order to assess potential concerns associated with non-response bias, we compared
the respondents with matched data with those with unmatched data. We found that hotel
performance as reported by owners (p=0.60), GM Autonomy (p=0.19) and GM Experience
1
Asset managers are employed by the owner to oversee the hotel’s management company. In essence, they
represent the owner on most if not all of the hotel’s issues.
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(p=0.13) were not significantly different across matched and unmatched hotels. We thus
conclude that our relatively small matched sample is representative of our total sample.
We also found that (p<0.05) matched hotels were larger (358 rooms) than those that
did not match (194 rooms) and that owners of unmatched hotels reported higher performance
than those of matched hotels (p<.05). Given this difference, we sought to determine if this
could limit the generalizability of our results. It is possible that our tests for the relationship
between congruence and performance may have been based more heavily on underperforming
hotels; however, further examination of the performance measure suggests that this is not a
substantive concern. The final sample still had a wide range of performance levels (from 1.44
to 5.81). Additionally, performance ratings were somewhat positively skewed (mean of 4.29,
SD of 0.92). Thus, having a somewhat greater number of lower-performing hotels actually
provided data with better distributional characteristics with which to test our hypotheses;
furthermore, we are clearly not lacking representation from higher-performing hotels. Thus,
while it is important to point out the differences discovered in our sample for completeness,
subsequent examination of the data suggests that it should not have a detrimental effect on
subsequent analyses or the generalizability of findings.
3.2. Measures
Goal Congruence. This was constructed by considering the level of agreement
between owners and operators’ ratings about the relative priority of 21 different operational
objectives across five functional areas (Human Resource, Finance, Sales and Marketing,
Property, and Operations) over the following 2 years. A sample item asked “What should be
the relative priority of each of the following financial choices for the hotel over the next two
years?” A scale from 1 to 6 was used (where 6 indicated higher priority).
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The overall measure of Congruence was computed as the Euclidian distance between
each of the individual priority questions. However, to rescale the measure so that higher
values indicate greater congruence, we subtracted the sum from the maximum possible (i.e.,
(6-1)2) value so that:
  

 
Congruence measures for each individual functional area were similarly computed, but
using only the subset of items related to the specific function. These too were reverse-scored
so that higher values denoted more congruence. Note that a small number of individual items
were left blank by some respondents (< 2%). We thus imputed missing values to avoid biases
associated with list-wise deletion and to maintain as much power as possible (Little & Rubin,
2014).
The measures had acceptable (alpha > .70) reliability for each of the functional areas.
These separate scales were computed for descriptive purposes. But the study’s focus is on the
overall level of congruence, and the final 21-item congruence measure had a high level of
reliability (alpha = .91).
GM Autonomy. This was measured for each functional area (HR, Operations, Finance,
Marketing, and Property) and based on an established scale (Hodari & Sturman, 2014). A
sample item included “what is the relative amount of influence the GM has on each of the
hotel’s financial decisions?” One item was asked for each functional area, on a scale from 1 to
5 (5 indicating higher autonomy). The final scale was computed as the average of the 5 items.
The resultant scale had high internal reliability (alpha = .93).
Hotel Performance. This was measured by the owners on 16 different performance
aspects across the functional areas: 4 corresponded to operations; 4 to marketing; 3 to human
resources; 4 to finance; and 2 corresponded to the hotel’s physical property. We incorporated
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non-financial measures of performance. Patiar and Wang (2016) note that while the practice
of using such measures is not yet common among hotels, they are increasingly used in the
wider business environment to monitor business processes and development. A sample item
asked “In your opinion, how successful has the hotel been with regard to (Guest Satisfaction)
over the past 12 months?” Each item was evaluated on a 6-point scale (with 6 indicating
higher performance). A small number of individual items were left blank by some
respondents (< 5%). We thus imputed missing values to avoid biases associated with list-wise
deletion and to maintain as much power as possible (Little & Rubin, 2014). The final scale
was computed as the average of the 16 items and had high internal reliability (alpha = .95).
3.3. Analytical Approach
Analyses were conducted using MPlus 7.4 (Muthen & Muthem 1995) with the
maximum likelihood estimator to conduct the hypothesis tests. To test our model, we
followed an item-parceling strategy (Landis, Beal, & Tesluk, 2000). This method is
particularly appropriate when the study focuses on the relationships between latent constructs
and not specifically about scale items (Williams & O’Boyle, 2008). Previous research has
shown that parceling positive affects fit indices without biasing parameter estimates (Alhija &
Wisenbaker, 2006; Nasser & Wisenbaker, 2003). Specifically, we pursued a random parceling
strategy by creating four parcels of four randomly selected items (without replacement).
Landis et al. (2000) showed that random parceling is an effective strategy for both improving
model fit and facilitating model estimation. To test the robustness of the approach, we
repeated the process of creating random parcels a total of five times; however, there were no
differences across the five models in terms of the statistical significance of any of the path
coefficients.
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To serve as a base case, and to help rule out alternative explanations for the role
autonomy may have, we first conducted a baseline model (Model 1) with both GM Autonomy
and Hotel Performance being predicted by four control variables: GM experience, if the
owner employs an asset manager, if the GM primarily reports to the owner/asset manager (as
opposed to a management company executive), and the number of rooms in the hotel. GM
experience was controlled for as it has been shown to impact both autonomy (Hodari &
Sturman, 2014) and hotel performance (e.g., Guerrier, 1987). We wanted to control for the
presence of asset managers because they may impact hotel performance (Singh, Kline, Ma, &
Beals, 2012), and because failing to control for their presence could provide alternative
explanations for our hypothesized relationships because an asset manager represents an
additional individual in the owner-operator GM relationship. It is our understanding that in
Europe, it is usually the owner that employs the GM while in the U.S. the GM is employed by
the operator, and given the nature of the study we believed that the difference in to whom the
GM reports could be a potentially important variable to consider. Finally, the number of
rooms was used as we suspected that because of previous research, hotel size could impact
hotel performance (Claver-Cortés, Molina-Azorín, & Pereira-Moliner, 2007). Note that
because of the skewed distribution of hotel size, expressed in rooms, we used a logarithmic
transformation of rooms to reduce the leverage of high values and to make the distribution of
room sizes more approximate of a normal distribution. Tests of the five hypotheses then
required a variety of additional analyses that built upon this base model.
Test of Hypotheses 1-3
The first three hypotheses consider the relationships between GM Autonomy, Goal
Congruence, and Hotel Performance. We used correlation analyses to look at overall effects.
We also examined the parameter estimated from the structural equations model (SEM). The
17
SEM model included a path from Goal Congruence to GM Autonomy, as well as a path to
Hotel Performance. The model also included a path from GM Autonomy to Hotel
Performance.
Test of Hypothesis 4
Hypothesis 4 predicts that GM Autonomy will partially mediate the effects of Goal
Congruence on Hotel Performance. For this analysis, we tested the significance of the indirect
effect using bootstrapping procedures (Preacher & Hayes, 2004; Shrout & Bolger, 2002)
based on sample of 5,000 draws.
Test of Hypothesis 5
Hypotheses 5 considered the hypothesized moderation of autonomy and goal
congruence in the prediction of hotel performance. To test moderation, we examined the
interaction of GM Autonomy with the Congruence measure. To test the interaction, the latent
moderated structural equations approach was used (Klein & Moosbrugger, 2000) using the
XWITH command in Mplus (Muthen & Muthen, 2015).
It is important to note that for the latent moderated structural equations approach,
model fit indices generally used to interpret the fit of SEMssuch as CFI, TLI, RMSEA, and
χ2have not been developed for LMS models. Instead, to determine if the model with the
interaction has better relative fit, we conducted a log-likelihood ratio test (Satorra & Bentler,
2010). The test statistic for a log-likelihood ratio test is calculated using the following
equation:
D = −2[(log-likelihood for model without interaction)−(log-likelihood for with interaction)],
which follows a chi-square distribution with the degrees of freedom equalling, in this case,
one.
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4. Results
4.1 Descriptive statistics
Table 1 provides descriptive statistics for the survey’s variables. In terms of controls,
the mean level of GM experience was 13.99 years, which indicates that our GM sample had
substantial managerial experience and therefore the necessary inferential ability to suitably
complete the questionnaire. Nearly half (48%) of our surveyed hotels had an asset manager.
About 13% of the GMs reported to the owner. The mean hotel size was 358.31 rooms. As
expected, there was a reasonably large degree of difference between our smallest hotel (30)
and largest hotel (3,700), which is why in our subsequent regression models we use the Log
of room size. Logging room size resulted in smoother normalized distribution of scores
around the mean of 5.34 and standard deviation of 0.85.
Table 1 shows that the overall mean for GM autonomy was 4.16 (std. dev. 0.95) on a
5-point scale, with 5 indicating higher autonomy. Moreover, the individual item level
autonomy means ranged from 3.86 (for property) to 4.42 (for operations).
Overall Goal Congruence had a mean of 3.31 (std. dev. 0.35) on a scale from 1 to 6
(where 6 indicated higher goal congruence). While it can be difficult to interpret mean scores
with any degree of statistical accuracy, normatively a mean only around the mid-point (as is
the case here) serves to demonstrate that amongst the sampled hotels, owner-operator goal
congruence can only be described as moderate.
The mean level of hotel performance was 4.29 (std. dev.0.92) (on a scale of 1 to 6; 6
indicating higher performance) and there was a relatively wide range of performance scores,
from 1.44 to 5.81.
---Insert Table 1 about here---
19
4.2. Hypotheses 1-3
Both correlation analysis (see Table 2) and the SEM model results support Hypothesis
1. Goal Congruence was significantly correlated with Hotel Performance (see Table 3: r = .35,
p < .01). The SEM model (see Model 2 in Table 3) had generally good fit (CFI = .91; TFI =
0.90; RMSEA = 0.09; SRMR = 0.08), and was significantly better fitting than the baseline
model (Δχ2= 15.66; D = 15.66; dr = 2; p < .01). Furthermore, as shown in Model 2, after
controlling for the effects of GM experience, presence of an asset manager, if the GM reports
to the owner rather than the management company, and hotel size, Goal Congruence had a
positive effect on Hotel Performance (B = 0.89; p < .01).
Similar results were found for the effect of Goal Congruence on GM Autonomy. Not
only is the correlation between the two significant (r = .30, p < .05), but the effect remains
significant in the regression analysis after partialling out the variance attributable to the
control variables (B = 0.94; p < .01).
The third hypothesis predicted that GM Autonomy would be positively related to
Hotel Performance. The correlation between these two variables was indeed significant (r =
.41; p < .001), and the variable remained significant in the SEM analyses (Model 2: B = .32; p
< .05).
---Insert Table 2 and Table 3 about here---
4.2. Hypothesis 4
Results from the regression indicate support for Hypothesis 4. As described above,
Goal Congruence had a positive effect on GM Autonomy (B = 0.94). Further, GM Autonomy
had a positive effect on Hotel Performance (B = 0.32). Together, this indicates an indirect
20
effect of Goal Congruence on Hotel Performance of 0.30 (0.94 * 0.32) (Preacher & Hayes,
2004). The bootstrapping estimate of the indirect effect (based on 5,000 draws) indicates that
this effect is positive (i.e., significantly greater than zero) as hypothesized (p < .05). Given
that, even after controlling for GM Autonomy, the effect of Goal Congruence on Hotel
Performance remained significant (B = .89, p < .01), this indicates that the hypothesis of
partial mediation is supported.
In other words, we find support that Goal Congruence affects Hotel Performance both
directly and indirectly through its influence on GM Autonomy. The indirect effect is indeed
statistically significant, but the effects of Goal Congruence are not fully explained by its
effects on GM Autonomy. That is, if there are two GMs with the same level of Autonomy, the
GM at the hotel with higher Goal Congruence would still be predicted to have higher levels of
performance.
4.3. Hypothesis 5
Finally, we predicted a moderating effect of GM autonomy on goal congruence in the
prediction of hotel performance. As indicated in Model 5, the interaction of Goal Congruence
and GM Autonomy is indeed significant (B = 0.65, p < .01). The change in the model’s log-
likelihood was also statistically significant (D = 5.742, df=1, p < .05). It is also worth noting
that, even with the inclusion of the interaction score, the effects of GM Autonomy on Hotel
Performance remain positive and significant (at p < .01).
5. Discussion
Although there are many implicit assumptions about the managerial and performance
implications of the hotel owner-operator split, the extant research literature is largely
inconclusive about this. This study’s results support our assertions that goal congruence leads
21
to superior hotel performance, causes greater GM autonomy which in turn causes greater
hotel performance, and still increases hotel performance even after controlling for the effects
of GM autonomy. Furthermore, our findings demonstrate that while goal congruence is
important because of both these direct and indirect effects, its true value may only be realized
when it is simultaneously present with higher GM autonomy. This indicates that when GMs
are better able to implement plans to achieve the goals that are shared by both the hotel owner
and management company, they can best achieve greater hotel performance. These results
both support and extend findings from previous studies about the agency relationship and
implications pertaining to the split between hotel owners and operators.
Previous studies on how the relative power of owners and operators can shape HMAs
have shown that there is often substantial disagreement during the negotiation of HMA terms,
largely because the two sides have different and often conflicting objectives (deRoos, 2010;
Turner & Guilding, 2010). Our finding that owners and operators have only moderate goal
congruence corroborates previous researchers who have found that owner-operator goal
congruence is not particularly strong (Eyster, 1988; Panvisavas & Taylor, 2008; Turner &
Guilding, 2010; 2013). More importantly, it furthers our understanding about the owner-
operator relationship by demonstrating that this goal disparity extends beyond the HMA
negotiation phase and into when the hotel is actually operating under the management
agreement. This finding is particularly important because, while HMAs are supposedly
written to ensure alignment between owner and operator goals (e.g., deRoos, 2010), success
on this front appears to be rather limited. Our results thus call into question operators’
frequent claims that HMAs do align the two sides’ interests. Furthermore, given that the
operator is the owner’s agent, and the two sides have little goal congruence, our finding is
consistent with, and supported by, the predictions of agency theory, which suggests that
22
agents do not always act (or want to act) in the principal’s best interest (Eisenhardt, 1989;
Sharma, 1997).
Although owners and operators may have little goal congruence, those relationships
that do include greater congruence appear to benefit both sides. This is because we found that
that goal congruence impacts a hotel’s performance, with greater congruence being
significantly positively related to hotel performance. This happens directly (hypothesis 1),
indirectly through GM autonomy (hypotheses 2, 3, and 4), and with greater effect when GM
autonomy is greater (hypothesis 5). Combined, these results add to the growing stream of
research that has focused on the relationship between a hotel owner and its management
company (e.g., Panvisavas & Taylor, 2008; Renard & Motley, 2003; Turner & Guilding,
2013). More specificially, our results, from what we believe is the first such study of its kind,
empirically demonstrate the positive performance implications of greater degrees of owner-
operator goal congruence.
Based on our findings we argue that this goal congruence-performance relationship
may be largely due to the tripartite relationship that a GM has with the property’s owner and
operator, especially since the impact of congruence on performance has greater effect when
the GM has increased autonomy. While researchers often suggest that GMs are vital to a
hotel’s performance (e.g., Giousmpasoglou, 2014), our study supports this empirically by
demonstrating that hotels in which GMs have more autonomy, and thus a greater role in
shaping the hotel’s plans and policies, outperform hotels where they have less autonomy. Our
findings thus confirm previous research which has found GMs to have varying degrees of
autonomy (Hodari & Sturman, 2014; Takeuchi, Shay, & Li, 2008) and extends this in an
important new direction by demonstrating, for the first time, that not only is GM autonomy
related to hotel performance, but that it also both mediates and moderates the effect of goal
congruence on such performance. This may be because GMs are tasked with making and
23
implementing both operational and strategic decisions that help determine the hotel’s
direction and success. As such, our results also confirm the notion that decreased autonomy
can reduce managerial and firm performance (Braadbaart et al 2007 and Langred & Moye,
2004).
Our study also demonstrates that within a ‘multiple agency’ scenario (Child &
Rodrigues, 2003), principals with greater goal congruence provide their agents with more
autonomy than do principals with less congruence. Given our finding on the positive impact
that congruence has on performance, we suggest that in a multiple-agency scenario, managers
who receive a more unified directive, with a minimum of conflicting objectives, are able to
pursue a more cohesive plan which results in superior operating performance. Furthermore,
our finding that as GM autonomy is increased, goal congruence’s positive effect on hotel
performance also increases further supports the notion that managerial autonomy is important
in order to successfully implement the goals of multiple principals, and especially those which
are important to the various principals.
The traditional top-down operational control often prescribed for single agency
settings does not, therefore, seem to be necessarily as beneficial in a multiple agency scenario.
Our findings thereby provide some support for Child & Rodrigues’ (2003) suspicion that
some of the managerial recommendations emanating from traditional single agency research
may not be applicable in situations of multiple agency. In fact, greater control, as
demonstrated through reduced GM autonomy, was found to negatively impact hotel
performance. Our findings are, meanwhile, supported by some agency theorists who have
suggested that active monitoring can in fact decrease managerial effectiveness and firm
performance (e.g., Eisenhardt, 1989; Jensen & Meckling, 1976).
Our findings may be explained by the notion that when multiple principals seek to
control or influence their mutual agent, they provide conflicting “mandates” which prevent
24
the agent from pursuing a coherent and/or cohesive set of operational and strategic choices,
which in turn negatively impacts performance. Instead, we suggest that multiple principals
who are themselves aligned with regard to firm objectives, may not only be less concerned
with controlling their mutual agent’s decisions, but also more likely to help provide a context
within which managerial decisions can help the firm achieve internal alignment. This, in turn,
not only influences the firm’s performance, but also both principals’ economic returns.
6. Conclusion
This study is innovative for several reasons. First, it examines a unique agency
scenario where there exists multiple principals and agents with diverse and potentially
conflicting goals. It thus answers calls for empirical investigations into ‘multiple agency’
scenarios since these are notoriously difficult to study (Child & Rodrigues, 2003). Particularly
novel is the fact that one principal (operator) is also the other principal’s (owner’s) agent
(Dev, Thomas, Buschman, & Anderson, 2010), thereby creating a tripartite scenario of
interaction between multiple principals and multiple agents. Our study thus adds to the
agency theory literature in that while previous studies into the principal-agent relationship
have largely focused on the relationship between owners and corporate management, it has
lacked similar depth in examining the relationship between corporate management and those
within the firm that execute its plans and policies (Child & Rodrigues, 2003).
Our study has also contributed to the agency theory literature by demonstrating that in
a multiple agency scenario it is important for principals to have congruent objectives. Agency
theory research has long demonstrated that agents may diverge from principals’ goals, and
that the latter must control this. Our study has, however, also demonstrated that in multiple
agency situations the principals must also ensure that their own goals are congruent as this
results in greater performance, especially because of the effect this has on the principals’
25
mutual agent. Thus while the link between principals and agents objectives was already
firmly established, this study has demonstrated the importance of such congruence between
multiple principals because such congruence, unlike increased control (and decreased
managerial autonomy), had a positive impact on performance. Not only does such congruence
improve performance, but it also means less need to control the mutual agent, thereby
implicitly reducing the principals’ monitoring costs. Thus, the fears that the various principals
have about an agent acting opportunistically (or in favor of another principal) due, at least in
part to information asymmetry, while perhaps warranted, seem to be best attenuated by
focusing on goal congruence rather than increased control and decreased managerial
autonomy.
Third, it contributes to the hospitality management’s goal congruence and autonomy
literatures by specifically studying their relationship, something lacking in the extant
literature. It also provides a much needed examination of how GM autonomy and owner-
operator goal congruence can impact hotel performance, the latter being particularly
important given the important role HMAs play in the modern hotel industry structure.
In terms of our sample and methodology, it is the first study that has been able to
gather and analyze the views and information from the two primary stakeholders in hotel
management agreements hotel owners and management companies. Gathering their views
for the same properties meant that this study was uniquely able to match responses in order to
analyze goal congruence from a joint analysis. The SEM approach we used allowed us to test
our hypothesized paths in the context of the full model as indicated by our hypotheses, as well
as most appropriately tested for the significance of the indirect effects. The use of the latent
moderated structural equations approach (Klein & Moosbrugger, 2000) is also the most
current approach for testing for moderation in an SEM model, thus allowing us to test for the
26
hypothesized moderator while simultaneously appropriately handling the measurement error
associated with the latent interaction construct.
Managerial Implications
This study demonstrates the need for owners and management companies to agree
upon a core set of common goals for their hotels; as such, congruence is linked to superior
operating performance. While each party will clearly have their own objectives, an ability to
better align these will end up better serving each party as superior operating performance
should ultimately result in higher fees for most operators and greater asset valuations and
returns for owners. The study’s findings provide several recommendations to help hotel
owners, management companies and GMs achieve such congruence and performance.
Firstly, it highlights scope for hotel owners to make better decisions prior to HMA
negotiation with regard to the selection of a suitable hotel management company that has
property-level goals which are well aligned with their own. Similarly, we suggest that
management companies heed Gannon et al.’s (2010) warning that they often do not do enough
to select owners with similar objectives. Even though HMAs may be written to help align the
two parties’ interests, they may not help prevent discord if they already disagree about the
property’s challenges and opportunities, and plans to address these. A healthy discussion
should hopefully lead to better and more aligned objectives which should benefit not only the
hotel’s performance but also both parties’ economic returns.
An owner may, as well, realize from these discussions that it should in fact defer to the
management company’s plans, which could in turn also help to align their objectives. We thus
also suggest that management companies fully commit to ensuring that their hotels’ owners
not only know management’s plans for the property, but also the underlying reasons for these
decisions as this may help achieve owner support. Lack of such support may mean that the
27
benefits of pursuing the operator’s property-level strategy may be undermined or even
negated.
While raising one caveat to this research, the findings have an additional and
potentially important implication. Due to the cross-sectional nature of this study’s data, the
measured variables have been captured at a single time point. As such, we have not been able
to determine whether owner-operator goal congruence is a static phenomenon or if it is
dynamic over time. Inference from related research, however, suggests that it may be dynamic
(see Turner & Guilding, 2013). Should this be the case, hotel owners with HMAs already in
place (sometimes long-lasting) might be able to improve their hotel’s performance by
working toward bridging any goal incongruence with their operator (and vice versa for the
operator).
If goal congruence is dynamic, it might be beneficial for hotel owners and operators to
work toward further instilling a greater degree of collaboration and flexibility into their
relationship instead of potentially leaning toward a strict enactment of HMA clauses.
Normative understanding of the hotel industry, for example, suggests that in some hotels their
HMA may, figuratively speaking, never leave the owner and/or operator’s file cabinet. In
other words, HMA contractual clauses are not relied upon nor enacted by either party. Instead,
a flexible operating arrangement full of trust is enacted so that there is much give-and-take,
which results in a great deal of decision-making that falls outside of what was negotiated into
the HMA. By working together in this way, the relationship between a hotel owner and
operator could be seen as drawing closer toward the sort of relationship which exists between
the parties to a strategic alliance, who themselves are not engaged in an agency relationship
but who nevertheless often have a manager as their mutual agent.
The above point leads us to make a similar argument for providing GMs not only with
a clear set of unified objectives, but also with greater autonomy as this has been shown to be a
28
predictor of better hotel performance. GMs are thus recommended to, as much as possible,
flag any incongruent goals so as to not only reduce their role ambiguity and/or conflict, but
also the likelihood of under-satisfying their principals.
Limitations
Although this study has demonstrated clear relationships between performance and
both goal congruence and GM autonomy, we do recognize that this may be because when
hotels are performing well, owners and operators may accept that the current goals, as well as
GM’s decision-making, are correct, and thus there is greater congruence between the
principals and more autonomy may also be granted to their mutual agent. However, the end
result remains the same: hotel performance is clearly related to both goal alignment and GM
autonomy. While our study was able to match the responses of both owners and operators
from individual hotels, and therefore provide us with matched samples, which as far as we
know is the first to do so in hotels and specifically those under HMA, it would have benefited
from a larger overall sample of matched pairs. We thus acknowledge that our findings should
be interpreted with some caution until additional studies with greater or different samples are
undertaken. It should be noted however, that the sample size achieved is considered sufficient
for a meaningful statistically powerful analysis to be undertaken.
While relevant theory was used to derive each hypothesis with requisite directional
implication, causality cannot be determined from the cross-sectional survey methodology. As
a result, the potential for reverse or reciprocal causality cannot be ruled out. In consequence,
further research on this question using a longitudinal methodology and examining one or
more hotels and goal congruence (and the other variables of interest) at two time points would
allow empirical testing of the direction of causality, as well as potentially shedding light on
the process of achieving better goal congruence. It would also be valuable to supplement the
29
asset-manager rated performance metrics with objective measures of hotel performance
(RevPAR, profitability, etc.), but such data was unable to be collected as part of the current
research effort. Of course, getting such data may prove quite challenging. It is worth noting
that to the best of our knowledge no research to date has been able to get matched surveys
from both GMs and the owners of their hotels. The uniqueness of this data allowed us to test
previously untested propositions, and so although causality cannot be definitively determined,
this research nonetheless helps explain how the evolving relationships between owners,
operators and managers play an important role in the performance of a hotel property.
Suggestions for further research
While unique in that it matched owners and operators, our sample would have been
preferable had it been larger. Nonetheless the 64 owner-operator matches had sufficient power
to support all of our hypotheses. This only demonstrates the importance of autonomy and
congruence for understanding hotel performance. It would, however, be valuable for future
research to employ our approach to further investigate issues in the interaction between
owners and operators. We also note that our study may have provided different results if
responses from the management company had been provided by individuals from the
corporate hierarchy who oversee the hotel. GMs, as the management company employees
specifically tasked with achieving a hotel’s objectives, are, however, considered to be the
most informed about the firm’s objectives for the hotel. It would thus be interesting to analyze
their views with those of the management company’s corporate executives in a future study,
and also with objective measures of performance such as customer satisfaction ratings,
RevPAR, profitability, and related measures. It should be noted, however, that accessing a
suitable number of such persons, and matching them with hotel owners, securing permission
to collect such objective measures, would likely prove even more difficult than the approach
30
we took in this study. Furthermore, most such executives oversee multiple hotels and may not,
as such, be as knowledgeable about the specific goals the company has laid out for them.
Researchers could also examine goal congruence between owners and GMs of hotels
unencumbered by management in order to determine if owner-GM goal congruence differs
from owner-management company congruence. Findings could help clarify whether the
potential agency problem is in fact greater in a multiple versus single agency setting.
Similarly, we suggest investigating whether the addition of a third party, the asset manager,
could also alter GM autonomy and/or goal congruence. Thus, studies with greater sample
sizes and different objectives could distinguish between these four scenarios in order to
demonstrate how the involvement of multiple stakeholders impacts GM autonomy, firm
performance and owner-operator goal congruence.
31
Table 1
Descriptive Statistics of Variables in Study
Std
Variable
Cronbach’s
Alpha
devn
Min
Max
GM Experience (Years)
7.12
1
32
Asset Manager Present
0.50
0
1
Reports to Owner
0.33
0
1
Rooms
570.00
30
3700
Log(Rooms)
0.85
3.40
8.22
GM Autonomy (Overall)
0.93
0.95
1.2
5
Human Resources
1.13
1
5
Finance
1.03
1
5
Sales and Marketing
1.08
1
5
Property
1.10
1
5
Operations
1.00
1
5
Goal Congruence (Overall)
0.91
0.34
3.58
5
Human Resources
0.79
0.47
3.06
5
Finance
0.73
0.40
3.29
5
Sales and Marketing
0.71
0.33
3.63
5
Property
0.75
0.53
3.10
5
Operations
0.70
0.38
3.36
5
Hotel Performance
(Overall)
0.95
0.92
1.44
5.81
32
Table 2
Correlations
1
2
3
4
5
6
7
1. GM Experience
2. AM Present
.11
3. Reports to Owner
.32
.11
4. Log(Rooms)
.04
-.17
-.24
5. GM Autonomy
.23
.28
-.17
-.08
(.93)
6. Goal Congruence
.08
.14
-.02
-.22
.30
(.91)
7. Hotel Performance
.12
-.15
-.22
-.03
.41
.35
(.95)
Notes: N = 64. Correlations ≥ .24 are significant at p < .05.
33
Table 3
Structural Equations Model Results
Model 1
Model 2
Model 3
Outcome Variable
Predictor
GM
Autonomy
Hotel
Performance
GM
Autonomy
Hotel
Performance
GM
Autonomy
Hotel
Performance
GM Experience
0.044
(0.017)**
0.036
(0.018)*
0.039
(0.016)*
0.016
(0.016)
0.039
(0.015)*
0.022
(0.014)
AM Present
0.48
(0.22)*
-0.35
(0.24)
0.40
(0.21)
-0.58
(0.22)**
0.41
(0.23)
-0.55
(0.22)*
Reports to Owner
-1.04
(0.37)**
-0.97
(0.39)*
-0.86
(0.34)*
-0.47
(0.36)
-0.87
(0.40)*
-0.68
(0.40)
Log(Rooms)
-0.16
(0.14)
-0.16
(0.15)
-0.075
(0.13)
-0.030
(0.13)
-0.077
(0.16)
-0.024
(0.13)
Goal Congruence
0.94
(0.34)**
0.89
(0.36)**
0.94
(0.45)*
0.92
(0.63)
GM Autonomy
0.32
(0.14)*
0.40
(0.14)**
GM Autonomy x
Goal Congruence
0.65
(0.22)**
R2
0.23
0.14
0.31
0.37
0.31
0.51
CFI
0.90
0.91
NA
TFI
0.88
0.90
NA
RMSEA
0.10
0.09
NA
SRMR
0.16
0.08
NA
Chi-square
207.55
191.89
NA
df
124
122
121
Log-Likelihood
-621.83
-614.001
-611.13
Notes: * < .05; ** p < .01; *** p < .001. Unstandardized coefficients are reported with standard errors underneath in parentheses.
34
Figure 1:
Relationships between congruence, autonomy and performance
35
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