Measuring organizational flexibility in community pharmacy: Building the capacity to implement cognitive pharmaceutical services

Article Outline

• Abstract
• Introduction
• Methodology
  • Population and sample
  • Measures and survey development
  • Pilot study
  • Data collection
  • Data analysis
• Results
• Discussion
  • Limitations
• Conclusion
• Acknowledgments
• References
• Copyright

Abstract 

Background

Community pharmacy is undergoing transformation with increasing pressure to build its capacity to deliver cognitive pharmaceutical services (“services”). The theoretical framework of organizational flexibility (OF) may be used to assess the capacity of community pharmacy to implement change programs and guide capacity-building initiatives.

Objective

To test the applicability of an existing scale measuring OF to the industry of community pharmacy in Australia.

Methods

A mail survey was used to test a preexisting scale measuring OF amended from 28 items to 20 items testing 3 underlying factors of operational, structural, and strategic flexibility in the Australian community pharmacy context. The sample was 2006 randomly-stratified community pharmacies. A confirmatory factor analysis was conducted to assess the validity and reliability of the 1-factor models for each underlying construct and the full measurement model.

Results

Responses were received from a total of 395 (19.7%) community pharmacies. The 1-factor models of operational, structural, and strategic flexibility fit the data with appropriate respecification. Overall, the favorable fit of the individual factor constructs suggested that the multiple-factor measurement model should be tested. However, this model did not yield an interpretable response. Operational flexibility covaried negatively to the other factors, whereas structural and strategic flexibility shared covariance. Despite this, the results highlighting the individual factor fit suggest the constructs have application to pharmacy.

Conclusions

The individual OF constructs were useful in the development and initial testing of a scale adapted for community pharmacy. When further developed and validated, the scale could be used to identify group of pharmacies that require individualized assistance to build capacity and integrate services and other new endeavors.

Keywords: Organizational flexibility, Cognitive pharmaceutical services, Community pharmacy, Confirmatory factor analysis, Scale testing

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Introduction 

Community pharmacy is an industry undergoing transformation with increasing pressure to build the capacity to incorporate and deliver cognitive pharmaceutical services (“services”). Literature on implementation has focused predominantly on individual service frameworks,¹ but additional studies noted the need to integrate services into the practice and business of pharmacy.², ³ The next stage of research focused on the design of programs and tools to guide pharmacists in implementation in practice.², ⁴, ⁵ In building on previous research, there is a need to further explore alternative methods because service implementation has not met expectations.⁶, ⁷ In Australia, the gap between service provision initiatives and practical implementation has become clear.⁶ Previous research suggested that a change program is required; however, the capacity of community pharmacy needs to be built to support service provision.², ⁸

In addressing this issue, a comprehensive study was conducted to investigate how capacity could be built in community pharmacy to promote the integration of services.⁸, ⁹ The aim of the overall study was to investigate and identify areas that would build the capacity of community pharmacy to increase the rate of service implementation. Organizational flexibility (OF) theory was used to assess the capacity and capability of organizations to facilitate change. Volberda¹⁰ defined OF as “the degree to which an organization has a variety of managerial capabilities and the speed at which they can be activated, to increase the control capacity of management and improve the controllability of the organization.” He also categorized OF into 4 types based on the variety of managerial capabilities in an organization and the speed at which they can be activated (Table 1). OF was developed for large and predominantly manufacturing firms.¹¹, ¹² As a result, existing assessments are based predominantly on the ability of a firm to produce goods from raw materials and alter elements of their production process in response to market demand.¹¹ Verdu-Jover et al¹³ note that OF research in nonmanufacturing sectors could be insightful in assessing a firm's capacity to change.

Table 1. Types of organizational flexibility

Previous studies have measured OF using quantitative instruments. For example, Sethi and Sethi¹¹ focused on summarizing the 12 types of manufacturing flexibility (such as volume and production flexibility), and Verdu-Jover et al¹³ followed from Volberda's work by developing a measure for the 4 types of OF. The study compared small and large firms in the European Union, and the results suggested that fit between the external environment and flexibility in small, predominantly manufacturing firms could improve business performance.¹³ The theoretical concepts developed from OF and Verdu-Jover et al's measure of OF had not knowingly been applied to small-to-medium enterprises (SME) in a single nonmanufacturing industry in a previous study. Furthermore, although the existence of OF types in Australian community pharmacy had been identified qualitatively (Table 1), it had not been validated quantitatively.¹⁴

Thus, this study specifically aimed to test the applicability of an existing scale of OF to the industry of community pharmacy in Australia. In the Australian market, a measure of OF could be used to initially assess the capacity of change of the industry, classify pharmacies into groups, and target different groups with specific capacity-building initiatives to facilitate service implementation.

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Methodology 

Population and sample 

The study was conducted with Australian community pharmacies, a network of approximately 5000 retail outlets with a local population to pharmacy ratio of 4000 inhabitants.¹⁵ Regulation in Australia restricts the ownership of community pharmacies to registered pharmacists and does not allow corporate ownership¹⁶; hence, their structure is more commonly that of an SME.¹⁷ Approximately 75% of pharmacy owners are members of the Pharmacy Guild of Australia (PGA)¹⁸, the community pharmacy owners' representative body.

The sample used for this study consisted of a database containing the details of approximately half of the PGA members (n=2500). Previous national studies used similar sampling frames.¹⁷, ¹⁹ The sample was proportionally stratified based on the geographic location to include representative pharmacies from urban, regional, and rural areas. After the removal of pharmacies with incomplete details (n=102), a random stratified sample was taken from the sampling frame for the pilot study (n=392), and the remaining 2006 pharmacies were used for the main study. Based on the 19% response rate of the pilot survey, the sample size was considered sufficient to yield the minimum response sample of 300 cases to conduct a confirmatory factor analysis.²⁰, ²¹

Surveys were mailed with a covering letter explaining the relevance of the research and requesting a response from the pharmacist-in-charge who is generally the owner and presumed to be in a decision-making position in relation to the management of the pharmacy, the unit of analysis.²² The Dillman method was used for both the pilot and main studies.²³ Two reminders were mailed to the sample at 1 and 2 weeks after the original mailing. Ethics approval for the research was obtained from the Human Research Ethics Committee at the University of Sydney in November 2008.

Measures and survey development 

Verdu-Jover et al¹³ scale measuring OF was applied to the context of pharmacy. The existing scale was adapted to Australian community pharmacy because using an existing scale is an advantageous starting point in survey development rather than the focus group methodology.²², ²⁴ The 28 preexisting items were assessed by the researchers, pretested with 9 practicing pharmacists, piloted, and finally administered to a larger sample in the 3 stages of the study (Fig. 1). At each stage, the items were eliminated or modified to reflect pharmacy practice (Table 2).

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• Fig. 1 

  Scale design process.

Table 2. Item modifications from the original scale to the pilot survey to the survey instrument

Pilot study 

In stage 1, the researchers assessed each of the items. Nine items were eliminated based on the researchers' previous knowledge and practice experience as well as the results of a qualitative study showing the applicability of OF to pharmacy.⁹ Items were eliminated because of their reference to manufacturing concepts that are not applicable, their application to large firms, or their lack of relevance to Australian community pharmacy. The rationale for item deletion or modification is shown in Table 2. The remaining 19 items were then pretested with 9 practicing pharmacists to improve face validity. Slight alterations to the wording of the items were made to reflect the results of the pretest.

The pilot study was conducted with 392 community pharmacies. The definition of services was provided in the survey to establish a common frame of reference consistent with the research context.²⁴ Respondents were asked for their feedback through open questions on the survey, and the items were subsequently modified to reduce ambiguity or clarify the constructs.²⁵

The resulting response from the pilot survey (n=75, 19%) did not provide a sufficient sample to run a confirmatory factor analysis.²⁰ However, as a preliminary test of model fit, the data were analyzed in 1-factor models in an effort to predict any problems with the data. This analysis was conducted using maximum likelihood extraction method and bootstrapping suitable for smaller sample sizes.²⁰ This indicated that a number of the items were problematic because of low or negative loadings on the 1-factor models and prompted the modification of a number of items and the addition of new items. In reflecting on the concepts of OF from Volberda¹⁰, ¹³, the original scale, and a qualitative study, 6 additional items were added. Although there were 3 or more variables per factors, these additional items increased the degrees of freedom in each factor, a more parsimonious approach.²⁰ The final survey used in the main study contained 20 items measuring OF (operational: 7, structural: 6, strategic: 7) (Table 2).

Data collection 

In the main study, a total of 395 completed surveys were returned (19.7%). Demographic data from each pharmacy with regard to their location, their operational structure, and number of services provided were collected to assess the representativeness of the sample. For the purpose of confirmatory factor analysis, the data were checked to assess and replace missing data.²⁰ Forty surveys deemed unusable because of nonresponses to multiple items in the scale were removed from the database, and remaining data were substituted using the expectation maximization method. This model-based method is considered to produce “the best representation of original distribution of values with least bias.”²⁰ This method makes inferences on the missing data based on the completed data and substituting the expected value.²⁶ The final sample size was 355, considered sufficient to conduct a confirmatory factor analysis using AMOS 7.0 (SPSS, Chicago).²⁰

Data analysis 

Confirmatory factor analysis is a technique used to test a scale designed to explain a set theoretical framework. This analysis evaluates an existing theory and prior research in relation to a specific model for a new sample.²¹ Although that theory explicitly states the number of factors present, it is parsimonious to initially test the 1-factor models before the full measurement model.²⁷ In this case, the 3 factors suggested by the theory were operational, structural, and strategic flexibility. The nature of the fourth type, steady state, translated to negative (disagree) responses across all of the items because of their lack of change to improve their capacity.¹³ As a result of this, Verdu-Jover et al¹³ determined that the construct of steady state would not be part of the scale and was not been tested statistically. Before the scale could be tested in a full measurement model, the 3 factors were assessed separately to ascertain the data fit and content validity using estimation method of maximum likelihood.²¹

The goodness-of-fit indicators, standardized residuals more than |2.5|, and modification indices greater than 4 were used to assess the validity of the models.²⁰, ²¹, ²⁷, ²⁸ Subsequent changes to the models were made to improve the fit for each factor. The goodness-of-fit indices used were the χ² test, the root mean squared residual (RMR), adjusted goodness-of-fit index (AGFI), and root mean square error of approximation (RMSEA). In the χ² test, the probability value should be nonsignificant to indicate data fit, however, the test is sensitive to sample size, and once n is greater than 200, there is a higher likelihood that the probability will be significant.²⁷ In addition to this test, indicators of RMR<0.08, AGFI>0.95, and RMSEA<0.10 also signify goodness of fit without the sensitivity of the χ² test.²⁰, ²⁹ Finally, the items for each construct were tested for internal reliability using Cronbach's alpha.³⁰

The models were respecified to improve the goodness of fit between the data and model and eliminating variables not statistically and conceptually measuring the underlying construct.²⁰ The constructs were all indicated by 4 or more measured variables to ensure that they were statistically identified.²⁰, ²¹ The 3 factors of operational flexibility, structural flexibility, and strategic flexibility were then put into a multiple-factor measurement model to test the relationships among the factors.

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Results 

More than 97% of responding pharmacies were offering at least 1 service, but this measure did not indicate the extent to which the services were offered. Sixty-nine percent (n=246) of respondents were pharmacy owners. Of the remaining respondents, 102 (29%) were pharmacists-in-charge, and 7 indicated they were “other.” The representativeness of the sample was assessed in comparison with previous surveys conducted in Australia finding that it was representative in terms of metropolitan versus nonmetropolitan pharmacy location (Metropolitan: 55% vs 60%, χ²=1.042; P=.307).¹⁵ It was not possible to calculate the representativeness based on the delivery of services in each pharmacy because there was no available data for comparative purposes.

The confirmatory factor analysis showed that the individual factors made up by the items of the amended 20-item scale could fit the data. Fit requires respecification of the models to eliminate low loading variables (<0.20), a commonly used technique to achieve model fit.²¹ However, some suggest that standardized estimate loadings |0.5| can be candidates for deletion.²⁰ The decision was made not to delete these items based on the theoretical framework being used.¹³, ¹⁰ This resulted in reduction of variables for each model, improving the measures of fit and/or the loadings of the variables (Table 3).

Table 3. Confirmatory factor analysis model respecification

Specifically, the individual model for strategic flexibility was accepted with an AGF of 0.998 and a Cronbach's alpha of 0.67. The models for structural and operational flexibility fit the data. The loadings and Cronbach's alpha suggested that these 2 constructs could be improved. In respecifying the model for structural flexibility, the goodness-of-fit indices were not improved, but the Cronbach's alpha increased slightly, improving the internal reliability (original model: AGF: 0.985, alpha: 0.550; respecified model: AGF: 0.982, alpha: 0.572). Operational flexibility was respecified to fit the data (original model: AGF: 0.923, alpha: 0.417; respecified model: AGF: 0.993, alpha: 0.442), but with an alpha below 0.60, the low internal reliability was apparent in both the original and respecified models.³⁰ This signified that individually operational, structural, and strategic flexibility fit the data (Table 4).

Table 4. Confirmatory factor analysis factor loadings: 1-factor models

From a practical perspective, each individual factor result illustrated characteristics of operational flexibility for community pharmacy. Operational flexibility highlighted the daily operations of the pharmacy rather than emphasizing a future orientation exemplified by the predominant use of 1-year plans and outsourcing for services, such as Home Medicine Reviews.^a This confirmed the short-term orientation associated with operational flexibility, but did not emphasize the high responsiveness to market demands inherent in this type.¹⁰ Structural flexibility in pharmacy focused on the broader use of employees' skills to provide services as well as the use of business relationships with others. The structure of the pharmacy and the way in which people are organized to perform tasks could also be easily modified. The orientation of these pharmacies was a medium term of 2-5 years, and the use of staff capabilities was altered to incorporate service provision. Strategic flexibility in pharmacies showed the integration of new initiatives, such as service provision in daily operations. In seeking new opportunities, these pharmacies engaged with other organizations, such as governmental agencies or other health care professionals. Adaptability was crucial in response to a changing external environment without disrupting the daily operations. This further elaborates on qualitative-based assessments of the types of OF in community pharmacy.⁹

Overall, the fit of the individual factor constructs suggested that the multiple-factor measurement model could be tested,²⁷ the subsequent step in scale development.²⁰ The full measurement model did not yield an interpretable response because of the statistically insignificant covariance among the 3 factors. Operational flexibility covaried negatively to the other factors (strategic: −0.14, structural: −0.03), whereas structural and strategic flexibility shared a covariance of 1.03. Despite this, the previous results highlighting the individual factor fit suggest the constructs have application to pharmacy but that the overall scale requires scale modification and validity testing to extend its relevance in community pharmacy research.

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Discussion 

This study demonstrated that facets of the amended scale are insightful in understanding the current state of community pharmacy. The benefit to community pharmacy in using frameworks from the management literature fosters awareness of business issues that are inherently a part of pharmacy but can often be overlooked during the process of dispensing and providing professional advice on medications.³, ¹⁷, ³² This research presented a novel approach to adapting a management theory which is not without its challenges.

The findings suggest that further development is needed to increase the usability of the scale in the community pharmacy context. Theoretical modifications relevant to community pharmacy should take into account the items from the respecified individual factors models as well as incorporating other evidence, specifically in the areas of structural and operational flexibility to develop and trial new items. This could include the unique operating environment of community pharmacy that bridges the health care and retail environments and their structure as SMEs.²

The value of developing a scale of this nature and applying the theoretical framework is its potential benefit as a measure of the current capacity of community pharmacy and highlights areas in which it can be improved to facilitate service provision. It is essential that the capacity of pharmacies to take on an extended role, including service provision incorporating both clinical and management capabilities, is established in parallel with policy development and funding negotiations at national levels.², ⁷, ⁸, ¹⁷ By grouping pharmacies according to their level of capacity, specialized programs can be developed and targeted to increase the rate of service implementation.

Limitations 

One of the inherent limitations in all confirmatory factor analyses is the restrictions placed on findings to fall within a priori parameters.²¹, ³³ Theory development and testing should allow for the possibility that the concepts may vary in different contexts. de Vaus³⁴ notes that initial theories are seldom unquestionably confirmed in later research and changes encouraged by testing theoretical frameworks in other contexts or with other samples allow theory development to progress.

Both the sample and the context of this study create limitations that should be acknowledged. Firstly, the study sample was PGA members. Although this represents most of the community pharmacies,¹⁸ it could have created a potential bias in its focus on pharmacy owners and not pharmacists in general. The survey was addressed to the pharmacist-in-charge who is generally the owner and the assumed decision maker for the pharmacy to overcome this limitation. Secondly, the survey had a low response rate, which could be because of the number of research projects being conducted in Australian community pharmacies simultaneously.³⁵ This was cited by both respondents in the open comments section and informal feedback and nonrespondents as a major inhibitor to their participation in research. Finally, only 1 data sample was collected and tested. The potential to split the sample and run exploratory and confirmatory analyses was limited by the low response rate. However, considering the scale used had previously been validated, confirmatory factor analysis is the appropriate technique.²¹

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Conclusion 

OF constructs were useful in the development and initial testing of a scale, which could potentially be used to understand the challenge of service implementation and the related capacity and integration issues in community pharmacy. The results of these initial attempts to amend the scale has identified that further scale development is required.²², ²⁴, ³⁴ Although items were deleted or rewritten to incorporate these issues, this did not apparently sufficient to adequately reflect the reality of pharmacy practice. This study has shown that a theory originally developed for large manufacturing firms cannot be easily transferred to an environment incorporating nonmanufacturing aspects, such as health care and service provision. However, the individual theoretical constructs were broadly applicable in the community pharmacy context and have provided useful insights and areas for further development.

Further development could guide the building of capacity in community pharmacies to integrate services and other new endeavors. Currently, pharmacies are at different stages of evolution and require different levels to support to transition to the role of a service provider.² Professional associations could consider developing different support programs for individual groups of pharmacies and adapting content for their specific needs. In the long term, it will most probably be the ability to gradually incorporate and bundle new services in the practice of pharmacy that will support the viability of the industry.⁷, ¹⁷, ³⁶

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Acknowledgments 

This research project is funded by the Australian Government Department of Health and Ageing as part of the Fourth Community Pharmacy Agreement through the Fourth Community Pharmacy Agreement Grants Program managed by the PGA.

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• a Home Medicine Review involves a physician-generated referral for a consumer-focused service conducted by a community pharmacist aimed at optimizing the quality use of medicines and consumer understanding.³¹