1

The Relationship of Employee Perceptions of Organizational Climate to Business-Unit

Outcomes: An MPLS Approach

Bruce Cooil, Dean Samuel B. and Evelyn R. Richmond Professor of Management Owen Graduate School, Vanderbilt University 401 21st Ave., S. Nashville, TN. 37203 Phone: (615) 322-3336 ; Fax: (615) 343-7177 ; E-mail: bruce.cooil@owen.vanderbilt.edu Lerzan Aksoy, Associate Professor of Marketing Koç University, College of Administrative Sciences and Economics Rumeli Feneri Yolu, Sariyer 34450 Istanbul, Turkey Phone: (90-212) 338 14 56; Fax: (90-212) 338 16 42; Email: laksoy@ku.edu.tr Timothy L. Keiningham, Global Chief Strategy Officer and Executive Vice President (corresponding author) IPSOS Loyalty, Morris Corporate Center 2, 1 Upper Pond Rd, Bldg D., Parsippany, NJ 07054 Phone: (973) 658 1719 ; Fax: (973) 658 1701 ; Email: tim.keiningham@ipsos-na.com Kiersten M. Maryott, Assistant Professor of Marketing Department of Marketing and Business Law Virginia Commonwealth University Box 844000 Richmond, VA 23284-4000 Phone: (804) 828-3201 ; Fax: (804) 828-0200; E-mail: KMMaryott@vcu.edu Key Words: Employee Perceptions, Organizational Climate, Employee Turnover, Customer Satisfaction, Financial Performance

Acknowledgment: Bruce Cooil acknowledges support from the Dean's Fund for Faculty Research, Owen Graduate School, Vanderbilt University.

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The Relationship of Employee Perceptions of Organizational Climate to

Business-Unit Outcomes: An MPLS Approach

Abstract

There has been an extensive exploration of how organizational climate is related to

various business outcomes, but these studies have generally examined outcomes separately or

developed univariate measures that combine outcomes. These approaches fail to (a)

accommodate the multivariate character of important business results, and (b) facilitate the

firm’s need to achieve success on several dimensions. This research proposes a methodological

approach new to the service domain to address these issues. Using data from a large, multi-

national retail grocery superstore based in continental Western Europe, this study illustrates how

multivariate partial least squares (MPLS) models can be used. MPLS provides three

interpretable factors – Overall Organization Climate, Self Efficacy versus Leader’s Efficacy and

Personal Empowerment versus Management Facilitation of – climate that are important

predictors of three business outcomes: employee retention, customer satisfaction, and scaled

revenue. The use of the MPLS approach in other services domains is also explored.

3

INTRODUCTION

From the inception of scientific management, a primary focus of human resource

management has been on employee perceptions of work environments. Schneider (1994) notes

that Frederick Taylor’s (1911) basic motivation for his “Scientific Management” movement of

the early 1900s was to design work processes so that employees could perform in a climate

conducive to greater productivity. Accordingly, employee perceptions of organizational climate

and work experiences have become one of the most researched aspects of management, with

well over 10,000 papers having been published on the topic (Harter, Schmidt, and Hayes 2002;

Salanova, Agut and Peiro 2005).

The origins of climate research date back to Lewin and his colleagues (Lewin, Lippitt and

White 1939) who emphasized the role of leaders in the creation of climate. Today, organizational

climate can be defined as perceptions attributed to the work environment (Rousseau, 1988)

which is used primarily as a framework to understand how employees experience their work

environment and is distinct from employee satisfaction (Schneider and Snyder 1975).

Furthermore, even though employee perceptions and organizational climate is sometimes used

distinctly, Dean’s (2004) conclusions indicate similar findings regardless of the previous and

distinct use of employee perceptions and organizational climate. The importance of measuring

organizational climate is critical as it has been shown to link to a variety of outcomes and

successful organizational functioning. Researchers have sought to understand how the

dimensions of organizational climate are related to a variety of business outcomes such as

employee retention, turnover, intentions to quit (Dean 2004; Hemingway and Smith 1999;

Mulki, Jaramillo and Locander 2006; Ryan, Schmit and Johnson 1999), customer satisfaction

(Gelade and Young 2005; Heskett, Sasser, and Schlesinger 1997; Schneider, White and Paul

4

1998; Schneider, Ehrhart, Mayer, Saltz and Niles-Jolly 2005), and firm financial performance

such as growth, sales, revenue and profitability (Borucki and Burke 1999; Gelade and Young

2005; Schneider, Ehrhart, Mayer, Saltz and Niles-Jolly 2005).

In the services domain, the Service Profit Chain (SPC) (Heskett et al. 1994; Heskett,

Sasser, and Schlesinger 1997) has been of particular interest. The SPC is typically

conceptualized as a virtuous chain of effects beginning with the internal service quality,

(including workplace design, job design, employee selection and development, employee reward

and recognition) which ultimately facilitates greater customer satisfaction, loyalty and

profitability. This in turn is expected to lead to improved business results (for example, Heskett

et al. 1994; Schneider et al. 2003). The direct implication of these models is that the internal

employee environment, customer satisfaction, and profits are all positively linked.

Studies examining the specific relationships proposed in the SPC have been mixed, with

some finding support for the proposed associations, while others offer contradictory findings.

Heskett, Sasser, and Schlesinger (1997) suggest that improvements in the internal climate

increase the satisfaction of employees, which then ‘reflects’ on customers and vice-versa

resulting in a cycle of ‘good service’. The ultimate result of this cycle was increased

profitability. In their meta-analytic approach, Harter, Schmidt and Hayes (2002) present

compelling evidence that positive employee perceptions are linked to improved organizational

performance. In an extensive study of the retail banking industry in Brazil however Kamakura et

al. (2002) were unable to support the proposed satisfaction-mirror effect proposed by the Service

Profit Chain.

The majority of these studies have focused on discrete links in the chain and studied one

outcome measure (or a univariate composite of outcomes) rather than examining several

5

outcomes concurrently, such as financial performance along with employee turnover and

customer satisfaction. This focus on discrete links is of enormous value, and it has helped

identify the issues and the type of data that was needed for further investigation. There are,

however, several statistical and managerial advantages to studying multiple business outcomes

simultaneously and to modeling their multivariate relationship.

Typically, important business outcomes have substantial and statistically significant

correlations. Consequently, they cannot really be understood or studied if they are modeled in

isolation. Multivariate methods, which accommodate the interrelationships between these

outcomes, are needed if we are to find the common factors that affect their joint distribution.

Furthermore, from a practical application standpoint, managers often try to balance present needs

with future needs which often results in essential tradeoffs between objectives (MIT Sloan

Management Review, 2001). This requires that multiple objectives and outcomes be considered

simultaneously rather than separately.

The purpose of modeling business outcomes is to enhance firms’ future financial

performance. Short- and long-term objectives, however, do not always overlap neatly. Taken to

the extreme, one could imagine a firm spending extravagantly in an effort to create a positive

organizational climate for its employees while giving products away to customers; employees

and customers would be happy (for as long as the firm remained in business), but the impact on

long-term profitability would be compromised. Clearly, a manager’s short-term focus could hurt

a company in the longer term. Furthermore, the nature of these outcomes can exhibit potential

trade-offs (i.e. increasing one outcome could lead to a deterioration in the other).

Researchers have acknowledged these trade-offs and suggested the formation of

composite indices. In their meta-analytic approach Harter, Schmidt and Hayes (2002) choose to

6

focus on each outcome independently but suggest an alternate point of view where they combine

customer satisfaction-loyalty, employee turnover, productivity and profitability measures into a

composite index indicative of “overall performance” (p. 271). This is because they find high

correlations particularly between some of the outcome variables (such as productivity and

profitability). Nevertheless, the formation of such an index does not explicitly factor in the

tradeoffs between these measures and how changes in one variable affect the others.

As a result there is a need to consider various dimensions of organizational climate and

their concomitant relationship with important business outcomes simultaneously rather than

pooling them into a single measure. This research contributes to knowledge by proposing the use

of multivariate partial least squares (MPLS) as a novel means of accomplishing this objective.

We have not come across any research in the service area that employs this statistical technique

as a means of modeling several business outcomes simultaneously in terms of a large number of

potential predictors. Fornell and Bookstein (1982) and Fornell and Cha (1994) have shown how

MPLS provides an important alternative to LISREL when maximum likelihood estimation is

inappropriate or does not provide proper solutions. White, Varadarajan, and Dacin (2003),

Ribbink et al. (2004), Semeijn et al. (2005), and Jagpal (1981) also use partial least squares to

estimate and test structures for multiple response variables from constructs or predictors that are

chosen, a priori, as part of the relevant scientific model. We are proposing that MPLS be used

more generally as a way of finding the most relevant set of predictive factors for a group of

business outcomes when there are a large number of possible predictor variables, especially

when the structure of the relationships is not known, or not completely understood. Like

standard forms of factor analysis, the MPLS factors are functions only of the original items (not

the outcomes). Unlike standard forms of factor analysis, MPLS is designed to provide

7

uncorrelated factors that are good predictors of multiple outcomes, so that the results can be

evaluated directly in terms of predictive performance. Also, it provides a direct empirical way of

discovering the appropriate dimension; the number of factors can be chosen to maximize the

cross-validatory (predictive) R-squared for each dependent variable. Since the goal of MPLS is

to provide predictive factors for a set of outcome variables, that orientation also provides a

context that helps reify the factors that emerge. That is, the task of interpreting the factors is

generally less subjective than it would be in a typical factor analysis because one can use the

correlations of each factor with each outcome, along with the factor loadings, to guide these

interpretations.

MPLS is especially appropriate for finding the underlying factors that are predictive of a

set of correlated outcomes and is conducive to not only the organizational climate area but to a

variety of research questions in service marketing. It is a viable analytic tool for a variety of

service marketing related issues such as when examining the dimensionality and nature of

service quality, e-service quality, service recovery, service climate, servicescape, customer

orientation, relationship management, relational benefits and their impact on a variety of multiple

outcomes including but not limited to satisfaction, commitment, loyalty, attitudes, repurchase,

word of mouth, revenue, sales, profitability etc. This approach provides a way of constructing the

most important predictors for an array of outcomes of these kinds from a potentially

overwhelming set of organizational and operational variables, and thereby helps guide the most

effective management of those outcomes. Partial least squares regression is also especially

appropriate in settings like this where there are a large number of highly correlated predictor

variables (Sundberg 2002; Tobias 1997) and generally performs as well, or nearly as well, as the

8

best shrinkage methods, including ridge regression, in terms of both prediction error and

coefficient estimation (Frank 1989; Frank and Friedman 1993).

To test the viability of the MPLS technique in this context, employee perceptions of

organizational climate are collected from 107 ‘superstore’ locations from a large, multi-national

retail grocery ‘superstore’ based in continental Western Europe. This research provides an

examination of the dimensions of employee perceptions as they relate to three outcome

variables; 1) employee retention, 2) customer satisfaction, and 3) unit-level revenue (using 1-3 as

components of a multivariate dependent variable). In the next sections, we will propose a

theoretical framework regarding the dimensionality of organizational climate, review the

literature to date on the relationship between different dimensions of organizational climate and

the three business outcomes under consideration, discuss and illustrate the superiority of using an

MPLS approach, and present the findings, managerial implications, and suggestions for future

applications of this approach in the services marketing domain.

THEORETICAL FRAMEWORK

Dimensionality of Employee Perceptions of Organizational Climate

Organizational climate is referred to primarily as perceptions attributed to the work

environment (Rousseau, 1988) and is used primarily as a framework to understand how

employees experience their work environment. Research results on the dimensions of

organizational climate, however, have been wide-ranging since the inception of the area.

Initially, it was assumed that the social environment could be described by a limited number of

dimensions such as individual autonomy, structure, reward orientation and consideration,

warmth and support (Campbell et al.1970). Later, other dimensions were added such as role

9

stress and lack of harmony, job challenge, autonomy, leadership facilitation, work group

cooperation, friendliness and warmth (James and James 1989; James and McIntyre 1996). In a

review of the field, Glick (1985) found that psychological distance (Payne and Mansfield 1978),

managerial trust and consideration (Gavin and Howe 1975), open mindedness (Payne and

Mansfield 1978), communication flow (Drexler 1977), risk orientation (Lawler, Hall and

Oldham 1974), service climate (Schneider, Parkington and Buxton 1980), equity (James 1982)

and centrality (Joyce and Slocum 1979) were the most frequently used constructs to describe

organizational climate. Formal scales such as the Business Organization Climate Index were also

developed (Payne and Pheysey 1971).

The definition and dimensionality of organizational climate has also been customized to

specific contexts. Perhaps one of the most popular is that of “service climate.” Schneider (1994)

defines service climate as “practices and procedures that exist in an organization with regard to a

particular outcome. Thus, service climate refers to the practices and procedures existing in an

organization that promote service delivery excellence.”

Given the variety of dimensions and the multitude of definitions proposed, there is both

significant overlap and disparity among these constructs and there is little consensus on a single

definition of what constitutes organizational climate. This is largely because the concept of

organizational climate is very broad and complex, in part because it consists of a variety of

interrelated factors. It therefore becomes difficult to include all relevant dimensions in

diagnosing organizational climate and with the emergence and proliferation of different

dimensions of organizational climate, consensus on a single theoretical framework remains

difficult. One of the earlier meta-analytic studies performed to date is that of Quinn and his

colleagues (Quinn and Rohrbaugh 1981 and 1983; Quinn and McGrath 1985) where they

10

engaged in a series of studies to review the organizational climate literature and compile a list of

dimensions, which they termed the “Competing Values Model.” This model is an empirically

derived and comprehensive framework that encompasses many of the proposed dimensions in

the literature and it has proven to have both face and empirical validity. With this model, Quinn

and colleagues proposed that organizational climate can best be classified by the fundamental

dimensions of internal versus external orientation and flexibility versus control. The framework

suggests four main quadrants, each associated with different managerial ideologies encapsulating

the means by which outcomes may be achieved. The model was termed “competing values

model” to emphasize the potential for opposing values to exist concurrently in organizations to

attain predetermined goals. One of the major advantages of this model is the fact that it derives

its approach from long standing theories in management and organizational psychology during

the last 100 years and hence has gained wide acceptance (Patterson et al. 2005). These four

approaches can be described as 1) the human relations approach, 2) the internal processes

approach, 3) the open systems approach and 4) the rational goal approach.

Each of the four approaches in the Competing Values framework has been associated

with a number of organizational climate dimensions. Patterson et al. (2005) propose that the

dimensions associated with the human relations approach are employee welfare, autonomy,

participation, communication, emphasis on training, integration and supervisory support. They

also indicate that formalization and tradition should be part of the internal process approach and

that flexibility, innovation, outward focus and reflexivity should be associated with the open

systems approach. Finally, clarity of goals, efficiency and pressure to produce, quality and

performance feedback are proposed as part of the rational goal approach.

11

This research relies on the Competing Values framework as a theoretical basis for

generating the organizational climate dimensions and draws upon the work of Patterson and

colleagues (2005) to help determine the dimensions to be included in the study. Since the focus

of this research is on employee perceptions, the dimensions that are most relevant to employees

have been selected from the four approaches.

Organizational Climate and Relationship with Business Outcomes

As demonstrated in the literature review, the organizational climate literature (Quinn and

Rohrbaugh 1983; Patterson, et al. 2005) indicates that organizational climate is a

multidimensional construct. Many of the studies in this area have focused on single links (e.g.,

employee perceptions of climate to customer satisfaction, or to financial performance) between

these different dimensions and business outcomes and have found relationships of varying

strength (e.g., Harter and Creglow 1999; Keiningham, Aksoy, Cooil, Peterson, and Vavra 2006;

Schneider et al. 2003). Before we turn to the superiority of employing the MPLS approach, we

review the literature on the relationships between organizational climate and each of the three

business outcomes that are considered in this study; employee retention, customer satisfaction

and financial performance.

Overall, research on the relationships between organizational climate and business

outcomes are the result of three key associations:

1. Organizational climate to employee-specific outcomes (turnover, productivity, etc.; in

this study we consider turnover);

2. Organizational climate to customer satisfaction;

3. Organizational climate to financial performance.

12

Below is a brief summary of these relevant streams of research.

1. Organizational Climate and Employee Turnover

Employee turnover is of considerable importance to human resource managers.

Consequently, it has been the focus of a large body of research for the past half century. Much

of this research posits that positive employee perceptions of climate are an antecedent of

employee retention. March and Simon (1958) introduced a seminal theory of organizational

equilibrium based on the idea that positive job associations reduced the desirability of leaving an

organization. More recently, Peterson (2004) presented a theoretical two-period model of

employee persistence, which posited that employee expectations, motivation, and satisfaction in

Time 1 and Time 2 were mediated by organizational experiences, while expectations,

motivation, and satisfaction in Time 2 were proposed to link directly to employees’ turnover

decisions.

Generally, empirical research has also verified that there is a positive link between

positive employee perceptions of climate and employee retention. For example, Ryan, Schmit,

and Johnson (1996) examined the relationship between positive job associations and employee

retention for a large automobile finance company, finding a positive correlation. In a

pharmaceutical personal selling context, Mulki, Jaramillo and Locander (2006) find that a

salesperson’s perceptions of ethical climate is a significant predictor of trust in supervisor and

turnover intentions. In another study, Hemingway and Smith (1999) examine the withdrawal

behaviors (turnover and absenteeism) of nurses and find that specific dimensions of

organizational climate were related to turnover and absenteeism. Furthermore, specifically in the

services context, support was found for the relationship between service climate and voluntary

turnover (Sowinski, Fortman and Lezotte 2008).

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Therefore, we would expect the relationship between favorable organizational climate

and employee turnover to be negative.

2. Organizational Climate and Customer Satisfaction

A number of researchers have sought to examine the various links in the SPC model

(Hallowell and Schlesinger 2000; Gelade and Young 2005; Kamakura et al. 2002; Loveman

1998; Rucci, Kirn and Quinn 1998). Schneider, White and Paul (1998) examine how service

oriented behaviors by employees can promote reporting of positive customer service

experiences. Heskett, Sasser, and Schlesinger (1997) focus specifically on the relationship

between positive employee perceptions and customer satisfaction and suggest that the

satisfaction of employees ‘reflects’ on customers, and vice-versa. The ultimate result of this

chain of effects is increased profitability. Several investigators have attempted to demonstrate

the existence of the satisfaction mirror. While there is research that would appear to support

such a conclusion (Schlesinger and Heskett 1991), other research offers contradictory findings

(for example, Kamakura et. al. 2002). Nonetheless, the majority of employee perception

research appears to show a positive relationship with customer satisfaction (Crotts, Dickson, and

Ford 2005; Dean 2004; Payne and Webber 2006; Schneider and White 2004)

However, research that specifically examines aspects of service climate almost always

finds a positive relationship with customer satisfaction. Schneider, Ehrhart, Mayer, Saltz and

Niles-Jolly (2005) develop a framework from service-focused behavior to customer satisfaction.

They find that there are significant mediated relationships between service climate and customer

satisfaction. In a study of the SPC in the retail banking sector, Gelade and Young (2005) find

that favorable climate evaluations are associated with increased customer satisfaction. In another

study of the banking industry, Johnson (1996) finds that all service climate dimensions are

14

significantly related to at least one facet of customer satisfaction. Solnet (2006) finds that service

climate is closely linked to customer satisfaction in the Australian hotel industry and Liao and

Chuang (2004) find that service climate explains between store variance in customer satisfaction

in a restaurant context. Finally, Rogg, Schmidt, Shull and Schmitt (2001) find that organizational

climate mediates the relationship between human resource practices and customer satisfaction. .

Therefore, we would expect the relationship between organizational climate and

customer satisfaction to be positive.

3. Organizational Climate and Financial Performance

There have also been a range of studies that examine the links between organizational

climate and firm performance, including financial outcomes such as growth, revenue and

profitability. For example, Schneider et. al (2005) propose a framework that ultimately links

organizational climate to sales. In the banking industry Gelade and Young (2005) examine the

links between organizational climate and sales performance. Harter, Schmidt, and Hayes (2002)

present compelling meta-analytic evidence that generalizable relationships large enough to have

substantial practical value between unit-level employee satisfaction–engagement and business-

unit outcomes are found. Additionally, in an extensive study of thirty-five companies over eight

years, Schneider et al. (2003) found that employee perception data was linked to financial

outcomes (return on assets; ROA) and market performance (earnings per share; EPS). In

particular, positive employee perceptions of “overall job” “security,” and “pay” were positively

and significantly correlated with ROA and EPS. Borucki and Burke (1999) find that service

related antecedents lead to store performance. Using the retailing context, they find that service

climate variables predict the service performance of sales personnel and ultimately the store‘s

financial performance. Ozcelik, Langton and Aldrich (2008) recently found that leadership

15

practices, that foster a positive emotional climate in an organization, are significantly linked to

revenue growth. In the high technology industry, organizational social climates of trust,

cooperation, shared codes and language were found to be positively linked to a firm’s capability

to exchange and combine knowledge, and the quality of this relationship is predictive of firm

revenue from new products and services and firm sales growth (Collins and Smith 2006). On the

other hand, Paradise-Tornow (1991) found a strong negative correlation between management

culture (including service climate) and financial performance. Despite the contradictory findings

of Paradise-Tornow (1991), positive employee perceptions of organizational climate are

expected to be positively linked to unit revenue, since large-scale longitudinal studies report a

positive relationship between employee perceptions and firm financial performance. Therefore,

we would expect the relationship between organizational climate and financial performance to be

positive.

Advantages of the Proposed MPLS Approach

The MPLS model provides a method of finding uncorrelated linear factors, based on

employee perceptions that are designed to be predictive of multiple outcomes. In this way,

MPLS provides a natural compromise between multivariate regression analysis (where all

possible variables measuring employee perceptions are considered as candidate predictors) and

principal component analysis. At one extreme, multivariate regression models do not provide

much insight about the underlying ways in which perceptual constructs may be related to

outcomes, even when an appropriate model selection criterion is used to select a subset of

perceptual variables. At the other extreme, principal component analysis, or more general

exploratory factor analysis, may help identify important factors that differentiate among firms or

business-units, but they will not generally provide the best predictors of multiple outcomes.

16

Also, even if the goal were just one of minimizing prediction error, partial least squares methods

generally perform quite well in empirical studies and simulations (Stone and Brooks 1990) and

this is especially true of MPLS (Frank and Friedman 1993). Finally, in contrast to many factor

analytic approaches, there is a direct way to determine the optimal number of MPLS factors.

PLS has been used widely in the analysis of chemical engineering process data, sensory

evaluation and in chemometrics. More recently it has been used to identify genomic and

protemic variables that are most directly related to clinical outcomes (Boulesteix and Strimmer,

2006). In these areas the method provides a way of describing the most relevant factors that are

related to an outcome variable of interest, when there may be hundreds or thousands of potential

predictor variables. Applications where investigators actually apply MPLS to study more than

one dependent variable are less common, but these do include studies of brain imaging

(McIntosh, Bookstein, Haxby and Grady 1996) where several dependent variables are used to

represent pixel content, and when chemometrics is employed as a method of calibration in

spectroscopy, where spectral measurements are used to predict the concentrations of several

constituents (Bro 1996).

In the next section we use the MPLS approach to find those dimensions of the employee

perceptions of organizational climate that are most predictive of business outcomes.

Methodology

The data for this analysis came from a large, multi-national retail grocery ‘superstore’

based in continental Western Europe. Most climate research employs an aggregate unit of

analysis, such as the work group, store, department or organization. The rationale behind

aggregation lies in the belief that organizational collectives have their own climate and this can

be identified through differences and similarities between units (James 1982). Aggregation

17

allows the creation of a higher-level construct where individual differences are distilled to a

single collective perception. Hence, given the unit of analysis, most research in this area focuses

on aggregate (“organizational”) climate that is distinct from individual (“psychological”) climate

(James and Jones 1974; Schneider, Smith and Goldstein 2000). In any case, business outcomes

can only really be meaningfully defined at the store level, and so any predictive model requires

that the employee perceptions also be aggregated at this level.

Measures in this study consisted of employee perceptions of organizational climate

collected at each of 107 ‘superstore’ locations in 2002. The questions were part of a battery of

employee perception questions developed by a large research firm specializing in employee

perception research, and consist of 1) questionnaire items generated based on the Competing

Values Framework and (2) firm-specific questions based upon internal interviews with

employees and managers. As a whole, the survey was designed to capture employee-related

organizational climate dimensions (see Table 1) and all items were measured using a four-point

Likert scale to discern employee perceptions of organizational climate (1 = strongly disagree, 4 =

strongly agree).

[Insert Table 1 Here]

Unit-level data were acquired in a way that was consistent with the literature that studies

the relationship between employee perceptions and business outcomes (e.g., Harter, Schmidt,

and Hayes 2002; Schneider, Parkington, and Buxton 1980; Schneider, White, and Paul 1998).

Mean values of the employee perception variables were acquired from each store (n=107).

These means were based on an average response rate of 80%, or approximately 360 employees

per store. Customer satisfaction levels, staff turnover (expressed as the proportion that left

during the year) and store revenue for each store were also supplied by the retailer and appended

18

to the file. Customer satisfaction levels represent the mean overall satisfaction level for each

store based on a 10-point Likert scale. A third party research provider collects this information

on behalf of the firm.

THE MULTIVARIATE PARTIAL LEAST SQUARES MODEL

We use a multivariate partial least squares analysis to find latent factors based on

employee perceptions of climate that provide a way of predicting each of three business outcome

variables that are measured during the subsequent year: employee retention, customer

satisfaction, and revenue per employee (as a way of adjusting for store size). This approach is

appropriate when working with correlated dependents, and customer satisfaction is significantly

and positively correlated with the other two outcomes. Surprisingly, employee retention and

revenue per employee do not have a significant direct correlation, although the partial correlation

between these two variables does approach significance.

Correlations and Partial Correlations (with p-values)

Employee Retention Customer Satisfaction Customer Satisfaction 0.21 (0.033)

0.26 (0.009)

Revenue per Employee

-0.10 (NS) -0.18 (0.064)

0.33 (0.000) 0.36 (0.000)

Interestingly, all partial correlations are nominally larger in absolute terms than the

corresponding direct correlations, but otherwise reflect the same pattern of relationships (in each

case, these partial correlations reflect only the removal of the effect of the third business

outcome).

19

The multivariate partial least squares (MPLS) approach provides a way of finding

uncorrelated factors that are important and interpretable predictors for all three of the dependent

variables although these factors generally will not be orthogonal (i.e., the inner product of two

PLS vectors will not generally be zero)1. This distinguishes MPLS from principal component

analysis and other types of factor decompositions that are based on the covariances or

correlations among only the predictor variables, because such factors are not chosen for, and may

not have, any predictive value. Let Y represent the (n x 3) matrix of dependent variables, and X

be the (n x k) matrix of employee perceptions (n=107 stores, k=31 perceptions). In MPLS, the

first (n x 1) latent factor, z = Xγ, is chosen to maximize the sum of squared covariances between

z and Y (subject to a length restriction on γ), i.e., the (k x 1) weights γ that define z= Xγ, are

chosen so that,

γ’X’YY’Xγ = sup α [α’X’YY’Xα], such that α’α =1. (1)

Consequently, the vector of weights, γ, is simply the largest eigenvector of X’YY’X. The

subsequent latent factors maximize the sum of squared covariances between “deflated” versions

of X and Y derived by projecting each into the subspace of X, that is orthogonal to the earlier

factors. That is, the (k x 1) vector of weights γp+1 for the factor zp+1 = X p+1γ p+1 , p = 1, 2, …, k, is

the eigenvector of the matrix Xp+1’Y p+1Y p+1’X p+1, where,

ppp

ppp X

zz

zzIX

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

−=+ )( '

'

1 , ppp

ppp Y

zz

zzIY

⎥⎥⎥

⎦

⎤

⎢⎢⎢

⎣

⎡

⎟⎟⎟

⎠

⎞

⎜⎜⎜

⎝

⎛

−=+ )( '

'

1 , (2)

with z1≡ z, X1≡ X, and Y1≡ X (Burnham, Viveros and MacGregor 1996). Here we are

assuming X is of rank k, and if we extract all k factors in this way, the regression on those factors

1 For a discussion of the difference between “uncorrelated” and “orthogonal” see Rogers, Nicewander, and Toothaker 1984, and Frank and Friedman 1993, pp. 113-114.

20

is equivalent to a multiple regression of each dependent variable on all k predictors. In this

analysis, we select only the first three factors because this choice actually maximizes the

predicted R-squared value across all possible regressions of each dependent variable on a

common set of factors. These results are summarized in Table 2. Clearly this is a serendipitous

result, and typically one would have to settle for a common set of factors that only maximized

some function (e.g., the sum) of the corresponding set of predicted R-squared values of the

dependent variables (in each row of the table). Predicted R-squared is calculated as the cross-

validatory R-squared value achieved when each observed value of Y is predicted by fitting the

regression without that observation:

Predicted R-squared = ⎥⎥⎦

⎤

⎢⎢⎣

⎡

−−

−=⎥⎦⎤

⎢⎣⎡−

∑∑

=

=n

1i2

n

1i2

)i(

)yy()yy(

1)Total(SSPRESS1

)

, (3)

where PRESS (Allen 1971 and 1974) represents the leave-one-out predicted residual sum of

squares based on the cross-validatory predictions )(iy) . This is an asymptotically optimal

criterion for prediction-based model selection (Stone 1977; Yang 2005).

[Insert Table 2 Here]

THE MPLS MODEL FOR BUSINESS OUTCOMES

Dimensions of Organizational Climate

The relationship between these findings and the Competing Values framework is

important to note. Table 3 summarizes how these three latent factors are correlated with the

original employee perception variables. Recall that while these factors are uncorrelated, they are

not orthogonal. In contrast, the principal components would be both orthogonal and

21

uncorrelated. The correlations reported in Table 3 can be used as factor loadings to calculate

factor scores.

[Insert Table 3 Here]

The reification of these factors is relatively straightforward.

1. Overall Organizational Climate: Component 1 comprises positive employee perceptions

of all aspects of the work experience. This construct is significantly correlated with all

positive perceptions and most highly correlated with clarity of goals, integration,

involvement and welfare some of which include perceptions of well-defined

objectives/tasks, necessary qualifications and solidarity, assimilation, usefulness of

meetings, and the supervisor’s predilection to share the vision of senior management, and

to manage teamwork well. This factor appears to resemble overall employee perceptions

of organizational climate as it is typically described in the literature.

2. Self Efficacy versus Leader’s Efficacy: Component 2 encompasses personal preparedness

versus supervisor’s leadership. It is a contrast between perceptions relating to personal

efficacy (clarity of goals such as qualifications, task definition and clear daily

expectations and not needing more training today) versus the supervisor’s leadership

(especially sufficient experience with the team and leadership through communication,

by example, and ability to build confidence and competence). In other words it is a factor

that provides a contrast between the “self” (self efficacy) versus “other” (efficacy of the

leader).

3. Personal Empowerment versus Management Facilitation: Component 3 involves a

contrast between personal empowerment through training versus facilitating managerial

22

resources. It might be more completely described as “preemptive training to prevent

errors versus good management through supervisory support including knowledge,

management of teamwork, and communication,” where communication is interpreted

more broadly to also include supervisor’s availability, leadership by example, tendency to

consult coworkers and finally task definition, and sufficient meetings.

Each of these three components has aspects of both the rational goal approach (e.g.,

clarity of goals, pressure to produce, and performance feedback) and the human relations

approach (e.g., employee welfare, empowerment, emphasis on training, and supervisory support)

(Patterson et al. 2005). Component 1 is a factor that provides an overall organizational climate

dimension. The MPLS approach however has provided a deeper understanding of the

relationships by identifying components 2 and 3 in addition to an overall organizational climate

dimension. Substantively this adds to our knowledge of the area since these two additional

factors emerge when the dimensions are related to the outcomes simultaneously. It is interesting

to note that components 2 and 3 are similar in that they both provide a contrast between the

“self” (individual efficacy, resources and empowerment) versus “other” (leadership efficacy and

managerial facilitation and enabling) as it relates to organizational climate dimensions. The

primary difference is that the personal aspects of component 3 relate primarily to training, the

unacceptability of errors, and empowerment whereas component 2 tends to have weaker but still

positive correlations with personal items, such as qualifications to reach objectives, task

definition and clear daily expectations. Both components 2 and 3 have significant correlations

with the variables related to the supervisor’s ability to communicate and manage, but component

2 has much stronger negative correlations with these variables, and it has even larger absolute

(and significant) correlations with a broad set of leadership variables, especially the supervisor’s

23

leadership qualities, the abilities to build competence and confidence, to share success, and an

organization’s ability to make an employee want to “overdo.”

Relationship of Organizational Climate Dimensions with Business Outcomes

The first part of Table 4 shows how these three factors are related to the three business

outcome dependent variables. In this study, the dependent variables are business outcomes that

occurred and were measured following the employee perception measurements. So, we are

studying the general main effect relationships between employee perceptions with each of the

three business outcomes during a subsequent period. All significance levels referred to in this

paper are two-sided. These correlations may also be regarded as standardized multiple

regression coefficients because the PLS factors are uncorrelated by design (although not

orthogonal). For each dependent variable, the predicted R-squared value (see equation (3)) of the

multiple regression on factors is substantially smaller than the corresponding adjusted R-squared,

as is typically the case with linear models (Efron 1986).

[Insert Table 4 Here]

The correlations show an interesting pattern of relationships. Employee retention is

positively related to all three factors (adjusted R-squared is 31%), but the most significant

predictors are Overall Organizational Climate and Personal Empowerment versus Management

Facilitation, each of which account separately for over 10% of the standardized variance in

employee retention (predicted standardized variance is the squared direct correlation, or 13% and

17% for these two factors, respectively). Overall Organizational Climate is marginally

significant as a predictor of customer satisfaction and it accounts for less than 5% of the variance

in customer satisfaction. Self Efficacy versus Leader’s Efficacy is also positively correlated with

24

all business outcomes, but the most significant correlations are with customer satisfaction, and

revenue per employee.

The most striking finding is that Personal Empowerment versus Management Facilitation

has the largest positive correlation with employee retention among all factors, and significantly

negative correlations with both customer satisfaction and revenue per employee. As a predictor

of revenue per employee it accounts for 24% of the variance of this business outcome. So the

model indicates that although employee retention depends more on employee perceptions of the

value of personal empowerment through training versus management facilitation, customer

satisfaction (and revenue per employee to a lesser extent) depends more on management

facilitation relative to training, where in each case the training and management variables are

contrasted so that they are uncorrelated with Overall Organizational Climate. That is, given a

fixed level of Overall Organizational Climate, employee training (especially training that

empowers, meets needs and expectations, and preparation that prevents errors) is more important

to employee retention, but managerial facilitation and resources (especially supervisor’s

knowledge, and ability to manage teamwork) are more important to customer satisfaction and

revenue per employee.

Employee Retention

All three employee perception constructs are positively correlated to retention, although

the correlation with the second factor, Self Efficacy versus Leader’s Efficacy, is only marginally

significant (p<0.1) Overall Organizational Climate accounts for 13% of the standardized

variance in retention (p<0.001), while the third factor, Personal Empowerment versus

Management Facilitation accounts for 17% of the standardized variance in employee retention

(p<0.001).

25

Customer Satisfaction

Customer satisfaction is also directly linked to all three latent factors of employee

perceptions, although its weakest correlation is with Overall Organizational Climate. This is a

positive correlation of marginal significance (p<0.05 only for the one-sided test for positive

correlation).

Customer satisfaction has stronger relationships with each of the other latent factors

(p<0.001), especially Self Efficacy versus Leader’s Efficacy. Customer satisfaction actually has

a negative correlation with the last factor Personal Empowerment versus Management

Facilitation. Taken together the last two factors show that, conditional on fixed Overall

Organizational Climate, customer satisfaction is positively correlated with employee perceptions

relating to clarity of goals such as clear daily expectations, well-defined objectives/tasks and

responsibilities, feeling qualified to reach objectives, but it is negatively correlated with the

perception that training really met expectations and needs (this has a negative relationship

through each of the last two factors). Perhaps this last effect is due in part to the unmet

employee expectation that training should better prepare them to serve customers.

Revenue per Employee

Overall Organizational Climate, which emerges as our first factor and indicates overall

positive employee perceptions, is not significantly linked to revenue per employee. Given that

the majority of studies have shown a positive relationship between other primary measures of

organization climate and financial performance, this finding is unexpected. Skeptics of the

financial implications of improving a firm’s organization climate may see this as support of the

common lament by consultants that “culture is not a strategy.” This finding may also indicate

26

that a positive organizational climate is necessary to achieve financial success, but not the

“cause” of such success (i.e., it is the price of entry).

Based upon the analysis, however, this finding is perhaps best explained by the other two

latent factors. Interestingly, through the other two latent factors, employee perceptions does

have nearly as strong a relationship with revenue per employee, as it does with employee

retention. The strongest factor is the negative correlation of scaled revenue with Personal

Empowerment versus Management Facilitation. Here the indication is that the employee’s

unwillingness to concede that “to err is human” (i.e., the employee is unwilling to accept failure),

and the supervisor’s knowledge and ability to manage teamwork, are all positively linked to

revenue per employee. This indicates that it is not simply the positive overall climate per se that

results in positive financial outcomes, but commitment of employees to succeed, and managers’

ability to get the most of their teams.

COMPARISONS WITH OTHER FACTOR ANALYTIC APPROACHES

The remaining parts of Table 4 summarize the performance of factors from two other

common approaches: principal component (PC) analysis and maximum likelihood factor

analysis with varimax rotation. The loadings of these factors are provided in the Appendix.

In the PC analysis, only the first four factors account for significantly more standardized

variance than an individual item (i.e., only the first four characteristic roots are greater than 1,

p<005). In contrast, maximum likelihood estimation (MLE) indicates that only the first three

factors account for more standardized variance than an individual item. Nevertheless, the first

three MLE factors are almost identical to the first three principal components--the factor scores

from the first PC have a 0.99 correlation with those of the first MLE factor (and score

27

correlations are 0.98, and 0.90 between PC and MLE versions of the second and third factors,

respectively). Consequently it would be reasonable to use only the first three factors when

considering alternative approaches, and the loadings (see the Appendix) and correlations for the

PC analysis in Table 4 are nearly the same for unrotated MLE factors. The varimax factor

analysis, referred to at the bottom of Table 4, is probably the next most popular approach. The

varimax rotation (Kaiser 1958) of the maximum likelihood factors is an orthogonal rotation

intended to simplify the interpretation of factors by reducing the number of large loadings.

Comparison of Predictive Performance

As shown in Table 4, the varimax factors are nominally more useful than the principal

components for predicting each of the outcomes, and the difference is quite substantial in the

case of revenue per employee, where R-squared (adjusted) is nearly twice as large. Of course,

the MPLS factors are designed to be good predictors and those R-squared (adjusted) values are

generally from 40% ( revenue per employee) to 130% (employee retention) larger than the

values corresponding to models based on the varimax factors. Note that any extraction of three

orthogonal factors by maximum likelihood estimation (including varimax, quartimax, or

unrotated solutions) would provide the same overall performance in terms of adjusted and

predicted R-squared that is shown in the last section of Table 4, and the superiority of the MLE

approaches relative to the PC approach is presumably due primarily to the difference in the third

extracted factor (even though the correlation of 0.90 between the factor scores of the third PC

and third MLE factors indicate that they are quite similar).

Relative Advantages of Each Approach

Table 4 illustrates one of the primary advantages of MPLS relative to standard types of

factor analysis. Overall, MPLS provides better predictors and better models for outcomes

28

overall. Presumably this also makes the selection and reification of MPLS factors more

objective because the selection is based on the joint predictive value of a set of factors and the

correlations of the factors with the outcomes, along with their loadings, helps direct their

interpretations. On the other hand, some standard forms of factor analysis may be more

appropriate if the goal is primarily to identify latent variables that explain the most variance in

the original space of items. In this setting, the goal of subsequent model development may not

be intended to develop the best predictive models, but instead it could be used as a way to study

how various outcomes are related to the latent variables (factors) that best summarize the

underlying differences among observations in the space defined by the original set of items.

As an illustration, below is a comparison of methods in terms of the variance explained

by each factor in the space of the 31 items considered in this study. Relative to MPLS, only the

varimax solution explains less variance in the first factor.

Percent Standardized Variance by Factor

Factor 1 Factor 2 Factor 3

MPLS 43 19 5.6

Principal Components 53 11 5.4

MLE Unrotated 52 10 4.2

MLE Varimax 31 18 17

MLE Quartimax 52 8.6 5.7

29

GENERAL DISCUSSION AND CONCLUSIONS

The analysis reported here advances the empirical research on the relationship between

the components of employee perceptions of organizational climate and business outcomes (i.e.,

employee turnover, customer satisfaction, and revenue) in several ways. This study also

illustrates the advantages of the MPLS approach, an approach that, to the best of our knowledge,

has not been used in the service science domain to date. It is potentially a useful statistical tool

whenever the goal is to find interpretable and uncorrelated factors that are also good predictors

of a multivariate dependent variable. In this study, a cross-validation confirms that the first

three MPLS factors are jointly the best set of predictors for all outcomes.

Using data on items that were designed to capture employee perceptions of organizational

climate, along with data on three business outcomes, MPLS was used to construct three latent

factors from the climate variables that have substantial and significant relationship with the three

business outcomes. The first factor is described as the Overall Organizational Climate

dimension. It is positively correlated with all positive perceptions and most highly correlated

with the perceptions of well-defined objectives/tasks, necessary personal resources, and with

perceptions relating to teamwork.

The second factor, Self Efficacy versus Leader’s Efficacy contrasts personal dimensions

with those aspects related to how the employee interacts with management and the perceived

quality of that management. The third factor, Personal Empowerment versus Management

Facilitation, contrasts positive perceptions that training met expectations (even if more daily

training may be needed), of being empowered, and the unacceptability of error, and the

supervisor’s overall knowledge and her/his ability to manage teamwork.

30

It is important to note that these last two dimensions are factors that emerged in addition

to the overall organizational climate dimension. Hence when all climate items are included

within an MPLS analyses approach, two new factors become evident. Both factors have a

common underlying similarity. They contrast the employee with the management /leader. The

second component contrasts perceptions of self efficacy with perceptions of the leader’s efficacy.

The third component contrasts personal empowerment through training with the management’s

ability to facilitate processes in the work place. Hence both these factors have an inherent “self”

versus “other” contrast which is different and distinct than the “internal” (human relations)

versus “external” (rational goals) focus emphasized by the Competing Values Framework

(Patterson et. al 2003).

As a result, by focusing on those constructs that are jointly predictive of several business

outcomes, this research provides a different multivariate view of employee perceptions both in

terms of primarily demonstrating a novel methodological approach (MPLS) in addition to

indicating the presence of two new factors. This approach also allows one to directly compare

the strength and direction of the relationships between each perceptual dimension and each

outcome. Overall Organizational Climate is significantly and positively correlated with both

retention and customer satisfaction but not revenue. This finding would appear to indicate that

organization climate alone is not enough in positive financial outcomes. Rather, other latent

factors which can be thought of as specific components of the employee-management interaction

appear to affect firm revenue. Self Efficacy versus Leader’s Efficacy is also positively and

significantly related to all three business outcomes but most strongly related to customer

satisfaction and revenue per employee. The third factor Personal Empowerment versus

Management Facilitation is positively linked to retention. It is however negatively correlated to

31

customer satisfaction. We conjecture that this effect may result from high employee standards

for customer service but that it is represented as the concomitant effect of unmet employee

expectations for training on how to best serve customers. Furthermore, this third factor is also

negatively correlated to revenue. The results for factor 3 are interesting and show that while

employee retention depends more on perceptions of the value of training vis-à-vis managerial

facilitation, customer satisfaction and revenue depends more on managerial facilitation relative

to personal empowerment through training. In other words, given a fixed level of organizational

climate, training is more important to employee retention whereas managerial facilitation is more

important to customer satisfaction and revenue per employee. Hence companies that emphasize

one goal over another can invest accordingly.

Our findings identify multiple factors that make up the employment experience. When

the goal is to improve a range of outcomes, in addition to the individual links that effect business

outcomes, it is critical to employ methods that use all concurrent relationships to construct a

model. Since managers must balance various short-term and long-term objectives

simultaneously, it is important that they understand the differential effects of various employee

perception constructs on total business performance.

Finally, this illustration of MPLS demonstrates that it has a number of advantages relative

to typical methods of factor analysis. First, it can be used to summarize a large number of

potential predictors in terms of a low-dimensional summary of the most predictive factors for a

group of outcome (dependent) variables. Second, the selection of the number of factors can be

made objectively by cross-validation to maximize predictive R-squared values. Third, the

predictive focus of the analysis makes the task of reifying the factors less subjective than it

would be in a typical factor analysis because they can now to be viewed in terms of their

32

relationship to the outcomes, as well as to the variables of which they are composed. Fourth, the

analysis provides an assessment of each factor and of the overall analysis in terms of cross-

validatory summaries and significance testing. (see Table 4). Thus, MPLS provides an important

tool for developing multivariate predictive models, and it has potential whenever the goal is to

jointly model more than one business outcome in terms of key factors that may depend on a large

number of organizational or operational variables.

LIMITATIONS AND FUTURE RESEARCH

One limitation of this study is that it is not possible to conclusively generalize these

findings, as they deal with only one retailer in a single market category. While the dynamics of a

retailer maybe generalizable to other industries, it is not possible to make conclusive

generalizations to all industries. Hence, future research can attempt to replicate the same

methodology in other contexts. Furthermore, the data collected and used as part of the analysis

here is cross-sectional employee perception data. It is not clear however if and how the results

would hold up in other time periods. Therefore, future research can also attempt to put the results

and methodology to test using longitudinal data on employee perceptions. Our analysis has only

identified the presence of statistically significant relationships between employee perception and

business outcomes in a subsequent year. Therefore, inferences regarding causality cannot be

made. In addition, there are several studies that test the direction of causality between service

climate, employee attitudes, service quality and firm performance (Schneider, White and Paul

1998; Schneider, Hanges, Smith and Salvaggio 2003).

When considering the ultimate effect that employee perceptions have on various business

outcomes, we looked at only direct relationships (as simultaneous dependent variables). There

33

may be mediating variables that affect these relationships. For example, the Service Profit Chain

(Heskett et al. 1997) posits several mediator variables that constitute a chain of effects between

employee perceptions and business outcomes. Moreover, Schneider et. al (2005) posit

hierarchical relationships where the effects are mediated by different variables. Therefore, to

determine the robustness of these findings, further longitudinal studies are needed that examine

mediating variables and include other variables such as industry and cultural effects. For

example, Schneider, Salvaggio and Subirats (2002) have shown these types of relationships to be

moderated by variables such as service climate strength. That study found that there is an

interaction of service climate and climate strength in predicting customer satisfaction. Hence

future research should include such moderating variables to gauge their impact.

The items measuring organizational climate focused solely on an “inward” outlook

including employee perceptions of the work environment. There is however also an “outward”

focus in organizational climate that examines employees perceptions with regards to a more

outward /customer focus. Although this outward focus was not within the scope of the study,

future research should include this dimension to analyze the impact on firm performance

outcomes.

34

Table 1

Items Comprising the Employee Survey

Autonomy

May Question ProcessesAllowed to Speak

Integration Solidarity

Attention to AssimilationInvolvement

Everyone’s ConsultedSupervisor Support

AvailableMakes You Confident

Communicates WellShows by ExampleBuilds Competence

Shares Senior-Mgmt VisionShares Success

Manages Teamwork WellWith Team Long Enough

KnowledgeableConsults Coworkers

Training Need More Training Today (Reversed)Training Met Expectations and Needs

EmpoweredWelfare

Fair WorkloadError is Human

Clarity of Goals

Objectives DefinedClear on Daily Expectations

Tasks/Responsibilities DefinedQualifications to Reach Objectives

Tools ProvidedEfficiency

Sufficient MeetingsMeetings Are Useful

Performance Feedback Know Evaluative Criteria

Provides FeedbackEffort and Pressure to Produce

Makes You Want to Overdo

35

Table 2

Predicted R-squared for Each Dependent Variable Regressed on from 1 to 5 or More Factors

Predicted R-squared (%) for Each Dependent Variable

Number of Factors Employee Retention

Customer Satisfaction

Revenue per Employee

1 7.0 0.0 0.0

2 6.1 7.1 0.0

3 22.5 12.4 21.5

4 21.9 9.0 20.5

5 or more <12.9 0.0 <17.0

36 Table 3

The Correlations of the Factors with Each of the Itemsa

Factor 1 Factor 2 Factor 3

% Standardized Varaince 43 19 6 Reification

Overall Organizational

Climate

Self Efficacy vs. Leader’s

Efficacy

Personal Empowerment vs. Management

Facilitation Autonomy

May Question Processes 0.55 -0.32 0.10 Allowed to Speak 0.67 -0.24 -0.16

Integration Solidarity 0.72 -0.29 0.12

Attention to Assimilation 0.74 -0.13 0.18 Involvement

Everyone’s Consulted 0.75 -0.44 0.01 Supervisor Support

Available 0.63 -0.49 -0.32 Makes You Confident 0.64 -0.65 -0.20

Communicates Well 0.59 -0.64 -0.30 Shows by Example 0.57 -0.66 -0.29 Builds Competence 0.63 -0.69 -0.10

Shares Senior-Mgmt Vision 0.73 -0.33 -0.16 Shares Success 0.61 -0.63 -0.10

Manages Teamwork Well 0.70 -0.44 -0.37 With Team Long Enough 0.31 -0.74 -0.24

Knowledgeable 0.52 -0.46 -0.43 Consults Coworkers 0.66 -0.62 -0.26

Training Need More Training Today

(Reversed) -0.21 -0.63 0.22

Training Met Expectations and Needs

0.61 -0.25 0.57

Empowered 0.73 -0.26 0.24 Welfare

Fair Workload 0.78 0.04 -0.09 Error is Human 0.04 -0.20 -0.50

Clarity of Goals Objectives Defined 0.91 -0.04 0.04

Clear on Daily Expectations 0.67 0.23 -0.11 Tasks/Responsibilities Defined 0.87 0.23 -0.25

Qualifications to Reach Objectives

0.80 0.24 -0.02

Tools Provided 0.80 0.06 -0.03 Efficiency

Sufficient Meetings 0.60 0.11 -0.25 Meetings Are Useful 0.73 -0.30 0.06

Performance Feedback Know Evaluative Criteria 0.58 -0.38 0.07

Provides Feedback 0.68 -0.48 -0.06 Effort and Pressure to Produce

Makes You Want to Overdo 0.66 -0.66 0.02

37 aAbsolute correlations greater than 0.2 correspond to more than 4% of explained variance and are significant at the 0.05 level. Significant positive correlations are in bold; significant negative correlations are shaded grey.

38Table 4

How Organizational Climate Factors Relate to Business Outcomes a

Employee Retention

Customer Satisfaction

Revenue per Employee

Relation to MPLS Factors

Correlations (& Standardized Regression Coefficients)

Overall Organizational Climate 0.36 0.17 c 0.10 d Self Efficacy vs. Leader’s Efficacy 0.17 c 0.40 0.27 Personal Empowerment vs. Management Facilitation 0.41 -0.28 -0.49

Adjusted R-squared (%) 30.8 24.3 30.3 Predicted R-squared(%) 22.5 12.4 21.5 Overall Significance (p-value) <0.001 <0.001 0.045

Relation to Principal Components

Correlations (& Standardized Regression Coefficients)

Overall Climate 0.19 c 0.00 d 0.02 d Job Clarity vs. Effective Supervision 0.30 0.33 0.16 c Personal Input vs.Competent Direction 0.00 d - 0.25 b -0.33

Adjusted R-squared (%) 10.0 14.4 11.2 Predicted R-squared(%) 6.3 7.2 6.7 Overall Significance (p-value) 0.003 <0.001 0.002

Relation to Varimax Factors

Correlations (& Standardized Regression Coefficients)

Empowered, Clear Goals and Efficiency -0.10 d -0.10 d -0.06 d Common Vision & Good Support 0.30 -0.18 c -0.32 Autonomy, Integration & Informed Direction 0.25 b 0.38 0.34

Adjusted R-squared (%) 13.3 16.9 21.6 Predicted R-squared(%) 9.7 10.1 17.6 Overall Significance (p-value) 0.001 <0.001 <0.001

aAll correlations and models are significant at the 0.005 two-sided level, unless noted otherwise (all significance levels are two-sided). Since the factors in each group are mutually uncorrelated, these correlations are also the standardized regression coefficients for each factor when each standardized outcome is regressed on the three standardized factors. b p < 0.05 c p < 0.1 d Not significant (p > 0.1).

39Appendix

The Correlations of Factors from Standard Analyses with Each of the Itemsa Principal Components MLE Varimax Factors 1 2 3 1 2 3 % of Standardized Variance 53 11 5 31 18 17 Reification Overall

Climate

Job Clarity vs. Effective Supervision

Personal Input vs.

Competent Direction

Empowered, Clear Goals

& Efficienncy

Common Vision &

Good Support

Autonomy, Integration, & Informed Direction

Autonomy May Question Processes 0.62 0.15 0.54 0.32 0.52 0.68

Allowed to Speak 0.71 0.17 0.24 0.26 0.36 0.63 Integration

Solidarity 0.76 0.21 0.26 0.08 0.37 0.75 Attention to Assimilation 0.69 0.31 0.08 0.26 0.21 0.86

Involvement Everyone’s Consulted 0.86 0.06 0.25 0.30 0.26 0.68

Supervisor Support Available 0.82 -0.18 -0.17 0.27 0.72 0.35

Makes You Confident 0.89 -0.31 -0.01 0.49 0.65 0.33 Communicates Well 0.85 -0.34 -0.12 0.22 0.66 0.21 Shows by Example 0.84 -0.38 -0.16 0.10 0.13 0.04 Builds Competence 0.89 -0.33 0.01 0.27 0.51 0.47

Shares Senior-Mgmt Vision 0.81 -0.02 -0.18 0.33 0.61 0.39 Shares Success 0.84 -0.31 0.00 0.25 0.16 0.49

Manages Teamwork Well 0.86 -0.19 -0.33 0.50 0.48 0.34 With Team Long Enough 0.64 -0.52 -0.10 0.37 0.50 0.36

Knowledgeable 0.72 -0.30 -0.34 0.05 0.30 0.66 Consults Coworkers 0.90 -0.29 -0.07 0.17 0.31 -0.48

Training Need More Training Today

(Reversed) 0.08 -0.52 0.55 0.38 0.55 0.21 Training Met Expectations

and Needs 0.58 0.15 0.20 0.74 0.25 0.31 Empowered 0.73 0.22 0.34 0.86 0.29 0.17

Welfare Fair Workload 0.67 0.39 -0.19 0.82 0.46 0.11

Error is Human 0.18 -0.02 0.35 0.64 0.31 0.42 Clarity of Goals

Objectives Defined 0.81 0.42 -0.02 0.89 0.26 0.16 Clear on Daily Expectations 0.50 0.56 -0.05 0.83 0.43 0.15

Tasks/Responsibilities Defined 0.68 0.54 -0.28 0.85 0.15 0.40

Qualifications to Reach Objectives 0.59 0.62 -0.01 0.85 0.37 0.21

Tools Provided 0.68 0.42 -0.13 0.75 0.46 0.10 Efficiency

Sufficient Meetings 0.50 0.30 -0.20 0.76 0.53 0.12 Meetings Are Useful 0.77 0.04 0.05 0.64 0.47 0.26

Performance Feedback Know Evaluative Criteria 0.67 0.03 0.28 0.78 0.09 0.24

Provides Feedback 0.83 -0.12 0.01 0.78 0.16 -0.03 Effort and Pressure to Produce

Makes You Want to Overdo 0.88 -0.29 0.05 0.22 0.61 0.19

40aAbsolute correlations greater than 0.2 correspond to more than 4% of explained variance and are significant at the 0.05 level. Significant positive correlations are in bold; significant negative correlations are shaded grey.

41

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