Environment-quality management coalignment across industrial contexts: an

empirical investigation of performance implications

Fuentes Fuentes, María del Mar; Lloréns Montes, F.Javier; Molina Fernández, Luis M.; Albacete Sáez, Carlos; Gutiérrez Gutiérrez, Leopoldo.

University of Granada, Spain.

Abstract In recent years, many firms have decided to implement Quality Management (QM), persuaded by its numerous benefits and its competitive potential. In fact, in Europe the number of firms that have obtained and registered quality systems has grown spectacularly. Nevertheless, to what point can the industry’s structural elements limit the implementation of quality management and the performance obtained? Our paper introduces the strategic and industrial visions to determine how environments with different structural characteristics require different degrees of QM implementation to achieve financial, operational and employee performance. To test our goals, we gathered a sample of 273 European managers. We presented 16 different factors of competitive structure to members of the sample and elicited their likelihood of implementing different QM practices. The results show that the degree of implementation of QM practices depends on the structural characteristics of the environments in which the firms develop their activity. It is thus possible to establish the most appropriate configurations for each kind of sector studied. Key words: Quality Management, industry, environment, performance

I. INTRODUCTION

In recent decades, there has been a great effort to describe the factors that explain the success or failure of the processes of implementing improvement programs based on quality management (QM). More recently, efforts have concentrated on analyzing the relationship between quality management practices and organizational performance on various levels and identifying the relationships among QM practices and the direct and indirect effects of these practices on various performance levels [1]. However, very little research has been devoted to understanding quality initiatives in different industrial contexts and linking them with implementation efforts and quality outcomes. Further, the few studies that analyze a comprehensive range of total quality management practices invariably concentrate on firms within a particular industry group, rather than comparing different industry groups within the same study [2 ]. The focus adopted for such study stems from ideas in contingency literature. Theoreticians of strategic

contingency hold that a good adaptation between environment and strategy will produce better performance. The logic of this assertion is simple: the firms that offer their customers products of better quality will obtain greater financial and personal compensation for the organization than their competitors [3]. In contrast, firms with low performance should realign their strategies with the environment [4]. Arguments developed in strategic literature and the conclusions of the research suggest the hypothesis that aligning the QM implementation elements with structural characteristics of the environment yields higher performance. This article tries to fill this specific gap by investigating links between QM practices and the structure of the industrial sector through two related research questions: (i) are QM practices contingent with respect to the sectorial structure to which the organization belongs? (ii) what factors affect practices in the sectorial structure? and (iii) does coalignment favour improved performance?

II. METHODOLOGY

A. Data collection

The study concentrated on several sectors. Each questionnaire was sent to the CEOs from a random sample of 1,550 firms. After two rounds of follow-up reminders, 273 useful questionnaires were received, representing a 17.61% response rate. B. Measures

Structural characteristics of the environment. We use the perceptions of industry managers to identify the different competitive environments. A list of items was generated to measure the structural characteristics of the competitive environment. These variables were measured by requesting the level of agreement on a scale of 1 (strongly disagree) to 7 (strongly agree), for the items that appear in Table 1.

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Table 1. Measuring the structural characteristics.

1. The customers perceive many differences (physical, image or technological) among the products/services of the different firms in the sector.

2. There are many alternative suppliers of materials or services.

3. Firms need high financial resources to enter the sector and compete in it.

4. Customers do not have a market of substitute products/services for those sold in our sector.

5. There is a strong threat from international competition.

6. Rivalry among the firms is very high (in prices, advertising, etc.).

7. The number of competitors is very high compared with other sectors.

8. A high number of new competitors has entered the sector.

9. A high number of competitors has left the sector.

10. Fewer than 5 firms have the greatest slice of the market share.

11. Sales in the sector are shared out among a high number of customers.

12. Our sector is one of the most profitable.

13. The suppliers impose their conditions regarding price, service, delivery, etc.

14. Personnel costs have increased greatly.

15. The remainder of costs of product manufacture or service provision have increased greatly.

16. The customers have very little power to impose their own conditions.

Quality Management. QM practices have been measured using the scale as [5], which is composed of 39 items divided into the following seven dimensions: Leadership, Continuous improvement, Internal and external cooperation, Customer focus, Management process, Employee fulfilment, and Learning Performance. Our study conceptualizes performance as a measure of organizational effectiveness which, according to [6]. We grouped the items in three categories: financial performance, operational performance, and employee performance. The variables were evaluated on a scale of 1 (extremely bad) to 7 (extremely good) relative to the levels prior to QM implementation. This kind of subjective measurement enables greater comparison between different

kinds of industry and situations. The internal consistency of the scales, as well as the items finally considered, appears in Table 2.

Table 2. Internal consistency of final scales.

Quality Management

Scale title Initial items

Items deleted

Construct Reliability

Variance extracted

Leadership 5 2,4,5 0.85 0.75

Cooperation 8 2,7 0.94 0.72

Customer focus 4 2 0.85 0.66

Continuous improvement

4 4 0.86 0.68

Process Management

8 2,3,4,5,6,8 0.88 0.78

Employee fulfilment

5 1,4 0.95 0.86

Learning 5 2,3 0.87 0.69

Performance

Scale title Initial items

Items deleted

Construct Reliability

Variance extracted

Financial 2 - 0.97 0.94

Operational 6 1,2,3 0.79 0.56

Employee 2 - 0.78 0.64

III. DATA ANALYSIS

The concept of adaptation understood as deviation with respect to a profile can be used in many different situations but, as [7] affirms, it is especially useful to contrast the effects of coalignment between environment and strategy. If we can obtain a profile of strategic dimensions for units with high performance in a specific environment, any deviation from this profile will imply a negative effect on performance. To confirm the relation between deviation from the profile and performance, we use the correlation between both variables, which should be negative and statistically significant. The methodology followed for contrasting the two hypotheses advanced here follows the schema of [8] and is developed in four major phases: (1) identification of kinds of environment; (2) relation between kinds of environment and QM; (3) measurement of the degree of coalignment and (4) contrast of the effects of coalignment on performance.

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A. Identification of kinds of environment The different environments in which coalignment is analyzed have been established by applying the cluster technique to the 16 variables relating to the structural characteristics of the sector. The number of groups was determined by the combined use of different hierarchical and non-hierarchical methods. The ANOVA indicated significant differences at a level of 5% in the five environments for all the variables except E9. Focusing on the most distinctive characteristics of each group (Table 3) and grounding our analysis in the relevant literature, we have attempted to identify each group with a definite kind of environment. Table 4 presents a summary of the main characteristics.

Table 3.Centers for the final clusters (subsample).

Clusters ANOVA

Items of environment 1 2 3 4 5 F-

statistic Significance

E1 3.82 5.39 4.32 4.75 4.05 5.904 0.000

E2 4.27 6.02 3.47 5.21 4.40 23.858 0.000

E3 6.18 5.73 5.08 5.16 4.21 7.963 0.000

E4 2.45 3.43 5.14 2.16 2.55 34.200 0.000

E5 6.27 5.20 4.18 5.60 4.36 7.935 0.000

E6 6.91 6.63 4.90 5.96 5.21 18.588 0.000

E7 5.27 6.12 2.73 4.54 4.36 56.085 0.000

E8 5.09 5.41 2.19 3.00 3.66 43.923 0.000

E9 1.36 2.57 2.56 2.63 2.72 1.933 0.106

E10 6.45 3.84 5.97 6.12 1.81 94.639 0.000

E11 6.36 6.08 4.90 4.39 4.66 9.092 0.000

E12 5.36 3.20 3.49 3.19 3.09 5.713 0.000

E13 3.00 4.33 3.13 3.77 3.28 4.576 0.001

B. Relation between QM and the environments The ANOVA performed shows that there are significant differences to a level of 5% in all the dimensions of QM for each kind of environment (Table 5). After identifying the differences in QM as a function of the structural characteristics of the sector, the next step is to show whether these differences favour the performance achieved, that is, whether there is an appropriate coalignment between the degree of implementation of the QM elements and the structural characteristics of the environment. Before we do this, we must measure the coalignment.

Table 4. Characteristics of the kinds of environment.

Table 5. Averages of QM dimensions in each environment.

Kinds of environment Anova

QM dimensions 1 2 3 4 5 F-

statis Sign.

Leadership 6.22 5.85 5.31 5.35 5.26 3.22 0.01

Cooperation 5.62 5.65 4.98 5.06 4.98 4.09 0.00

Customer focus 6.42 6.28 5.75 5.85 5.72 3.32 0.01

Continuous improvement 5.87 5.86 5.42 5.64 5.26 2.79 0.02

Process management 5.40 5.41 4.74 5.09 4.77 2.81 0.02

ENVIRONMENT 1

- High profitability - High competitive rivalry - International competition - High degree of concentration of firms - Low differentiation of products/services - Large number of customers - Great need for capital

ENVIRONMENT 2

- Significant increases in cost of personnel and production - High number of suppliers - High number of competitors and new entrants - Large number of customers - High differentiation of products/services - Significant bargaining power for customers - High competitive rivalry

ENVIRONMENT 3

- Low number of competitors and new entrants - Moderate competitive rivalry - Few suppliers - Few substitute products

ENVIRONMENT 4- Low concentration of customers - Many substitute products - Considerable concentration of firms

ENVIRONMENT 5

- Low concentration of firms - Low profitability - Little need for financial resources - Moderate competition

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Employee fulfilment 5.33 5.32 4.47 4.50 4.60 5.83 0.00

Learning 5.57 5.51 4.78 4.93 4.95 3.17 0.01

Operatively, to measure coalignment, we calculate its opposite, that is, misalignment or deviation with respect to the ideal profile calculated with the calibration sample. For this we use the Euclidean distance, as Van den Ven and Drazin (1985) proposed:

2

1)( cjej

n

jXXntMisalignme −= ∑

=

(1)

where: Xej = score or the dimension j of QM for each firm in the study sample; Xcj = average score for the dimension j of QM for each firm in the calibration sample; j = 1, n where n is the number of dimensions of QM. C. Contrast of the effects of coalignment on performance The effects of misalignment with respect to the ideal profile on performance were measured by calculating the correlations between the measure of “misalignment” and performance. As we expected, to satisfy the hypothesis, this correlation must be negative and statistically different from zero for the study sample in each of the environments. Table 6 illustrates the results of the correlations between performance and misalignment in each environment.

Table 6. The relationship between coalignment measure and performance.

Financial performance Operacional performance

Kinds of environment Correlation p Correlation p

1 - 0.770 0.043 - 0.643 0.062

2 - 0.212 0.184 - 0.060 0.729

3 0.203 0.104 - 0.107 0.406

4 - 0.297 0.034 - 0.233 0.114

5 - 0.197 0.175 - 0.197 0.602

Employee performance

Kinds of environment Correlation p

1 - 0. 098 0.801

2 - 0.254 0.123

3 - 0. 366 0.003

4 - 0.511 0.000

5 - 0.332 0. 016

IV. DISCUSSION AND CONCLUSIONS

The results of our research allow us to draw several conclusions. First, the degree of implementation of QM elements depends on the structural characteristics of the environments in which firms develop their activity. It is thus possible to establish the configurations most appropriate for the kind of sector considered. Our empirical research showed that for different kinds of sectors, the coalignment of QM with the structural characteristics of the sector yields higher performance levels. Thus if the ideal profile is established from the firms with the best results, the rest of the firms can direct their efforts in the same way. Second, orientation to the customer and leadership are crucial variables for achieving the right coalignment with the structural characteristics of any sector. They form part of the ideal profile of the firms with greater financial and operational performance and employee performance. These dimensions, along with continuous improvement, require greater implementation in the firms analyzed in each kind of environment. On the other hand, employee fulfilment is the dimension least implemented in each kind of sector and thus does not stand out in any of the ideal profiles. Finally, the degree of competitive rivalry seems to be one of the most significant structural factors for the degree of implementation of QM elements. Firms that operate in environments with greater rivalry show a greater implementation of QM elements. The higher the intensity of market competition, the more aggressive a business must be in discovering customer needs and enhancing customer satisfaction. Though the study provides some useful insights into the role of organizational environment in the relationship of quality management and performance, certain limitations should be recognized. For example, we should not overlook the limitations derived from using subjective measures for the different variables considered. Ideally, the relations described should be compared using objective information, particularly in the case of performance.

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