A configuration-based approach to integrating dynamic capabilities and market transformation in small and medium-sized enterprises to achieve firm performance

Abstract

This article develops and tests a model integrating dynamic organisational capabilities, market transformation arrangements and firm performance. This model addresses weaknesses in previous empirical research by integrating accumulation and path dependency in measures of dynamic capabilities. Using a sample of 444 small and medium-sized Australian manufacturing firms, the study finds that performance is driven by the successful deployment of dynamic capabilities; such performance is mediated by purposeful market transformation strategies.

Keywords

Dynamic capabilities firm performance market transformation organisational processes organisational learning

Introduction

Over the last two decades the dynamic capability view of strategy, with its focus on capabilities that allow firms to negotiate the dynamics of changing organisational and market contexts, has become an important explanation for sustained competitive advantage (Teece, 2007). Dynamic capabilities are particularly relevant and beneficial to multinationals (Zollo and Winter, 2002), so it is unsurprising that the dynamic capability view is often explored in such firms which engage in complex, corporation-wide knowledge-sharing efforts. While there has been some empirical research (e.g. Corner and Wu, 2012; Døving and Gooderham, 2008) conducted in the context of small and medium-sized enterprises (SMEs), further analyses are required to explore if and how SMEs utilise dynamic capabilities, given their structural resource constraints and simpler organisational designs (Desouza and Awazu, 2006).

Recent work in the SME context has suggested that certain types of capabilities are most associated with growth: namely, marketing and financial management capabilities (Barbero et al., 2011). In addition, research has emphasised the benefits of SME size in terms of effective knowledge management (Bradshaw et al., 2008; Desouza and Awazu, 2006), while the importance of organisational culture as a growth-facilitating capability has been noted (Chirico and Nordqvist, 2010). Leiblein and Madsen found that ‘small firms have incentives and informational advantages over their larger counterparts’ (2009: 712), as they tend to provide a more supportive context for successful innovation commercialisation. However, these studies do not investigate how firms may adapt over time either to, or through, changing markets or the impact that this has on their performance.

One of the key challenges facing SMEs is the acquisition and deployment of resources needed to exploit opportunities, given their relatively limited resource base (Alvarez and Busenitz, 2001; Hadjimanolis, 2000). In this context, dynamic capabilities are the ability to ‘integrate, build, and configure’ (Teece et al., 1997: 516) the resource base over time, in order to respond to changing environments: this is particularly pertinent in terms of improving performance. In essence, SMEs may lack clearly identifiable resources that form the basis of a sustainable competitive advantage, but they may be able to build and reconfigure resources to adapt to changing environments. The proposed link between dynamic capabilities and competitive advantage is rarely conceptualised as direct; often being presented as mediated by the firm’s resources, which form the focus of reconfiguring and adaptation processes (see for example, Bowman and Ambrosini, 2003; Eisenhardt and Martin, 2000; Zott, 2003). This adaptation derives from organisational processes or routines that make change possible in a way that is more than an ad hoc or random response. What these specific processes in SMEs look like, and how they allow for reconfiguring and adaptation and then impact on performance, is something of a gap within the dynamic capability view. Most work to date does ‘not delve into the detailed, micro mechanisms of how these capabilities are deployed or how they work’ (Ambrosini and Bowman, 2009: 37).

In response to this theoretical and empirical gap, this article investigates three specific organisational processes, their constituent factors and their cumulative impact on firm performance via market transformation in the case of SMEs. The first organisational process encapsulates the use of formalised managerial and operational processes such as the use of plans, procedures, instructions and routines embedded in the firm’s activities (Andersen, 2000; Schwenk and Schrader, 1993). The second examines the social integration platform; a learning mechanism formed by learning practices that facilitates knowledge and experience distribution and socialisation between individual members, and enhances the introduction of new knowledge and information (Nonaka et al., 2000; Schulze and Hoegl, 2006; Subramaniam and Youndt, 2005). The third considers how information technology (IT) is embedded in organisational processes that allow for enhanced organisational learning in terms of the exploration and exploitation, sharing and internalisation of new knowledge (Kane and Alavi, 2007). Finally, market transformation is introduced as a link between these organisational processes and firm performance, as this captures both the changes made by firms to adapt to new market needs and the co-creation of markets (Pitelis and Teece, 2009).

This article provides empirical insight into the manner in which specific and measured organisational processes, as the microfoundations of dynamic capabilities, provide a basis for transforming the manner in which a firm engages with the market and the subsequent impact on financial performance. The most significant contribution to the relevant dynamic capability literature being the adoption of measures of real resource commitment and processes in order to measure the presence or absence of they within the sample representing an important development regarding how they have been measured and analysed in the past. Dynamic capabilities are indicated by ‘the capacity of an organization to purposefully create, extend, or modify its resource base’ (Helfat et al., 2007: 5). While this definition is somewhat abstract, in practice dynamic capabilities can be observed only through real and concrete resource commitments and processes; it is these aspects that are measured here as the microfoundations of dynamic capabilities.

Empirical research on dynamic capabilities

The fact that dynamic capabilities exist to ‘create, extend or modify’ operating capabilities (Helfat et al., 2007: 1) suggests that capabilities evolve within a hierarchy (Winter, 2003). However, there is some disagreement as to what may actually constitute the microfoundations of dynamic capabilities. Felin et al. (2012) have suggested that they can be clustered into ‘three core or overarching categories: (1) individuals, (2) processes and interactions, and (3) structure’ (2012: 1357). This approach, and that of the related ‘microfoundations project’ (Abell et al., 2008; Felin and Foss, 2009, 2011, 2012; Felin et al., 2012), have attracted considerable criticism (Hodgson and Knudsen, 2011; Pentland, 2011; Winter, 2011). For example, Winter (2011) takes issue with the role of psychological behaviourism in determining routines and capabilities, and instead provides a perspective that emphasises a hierarchy of routines (Nelson and Winter, 1982; Winter, 2003) and the role of learning (Zollo and Winter, 2002). Nevertheless, there does seem to be a general level of agreement that such microfoundations ‘can be defined as managerial and organizational processes that underpin and enable the deployment of dynamic capabilities’ (Foss et al., 2012: 7).

Empirically, progress in respect of understanding the microfoundations of dynamic capabilities has emerged from a range of studies identifying detailed relationships between resources, processes, and (generally) people within a specific context. These studies provide detailed insight into specific scenarios such as an internet firm’s exit strategy (Fortune and Mitchell, 2012), outsourcing activity in internet banking (Weigelt, 2009), and R&D capabilities following a merger in the pharmaceutical industry (Paruchuri and Eisenman, 2012). While SMEs are not a dominant domain for research, they have not been ignored. Notable investigations include new venture creation for a materials manufacturer (Corner and Wu, 2012), operating as suppliers to large firms (Woldesenbet et al., 2012), and firm evolution in the biopharmaceutical industry (Newey and Zahra, 2009).

Taken together, these studies can be grouped into three main categories:

an investigation of specific processes and their interaction (e.g. Karim and Mitchell, 2000; Moliterno and Wiersema, 2007);

a focus on learning processes (e.g. de Waard et al., 2012; Newey et al., 2012); and

consideration of the role of individuals and the decision-making process (e.g. Corner and Wu, 2012; O’Reilly et al., 2009).

This is consistent with the microfoundations focus on organisational processes that suggests researchers should delve into the interactions of specific processes, knowledge bases, resources and possibly cognition (Katkalo et al., 2010; Wright and Stigliani, 2013), in order to understand better how firms adapt through reconfiguring their resource base. It is on this basis that this study takes a number of organisational processes and investigates their role in adaptation both of, and to, the market, and the subsequent link to performance.

Theoretical model

Market transformation capabilities

It might appear obvious that firms exist to meet the demands of their customers through markets and by supplying products; yet only those firms capable of adequately creating or perceiving market needs, and adapting themselves in time to meet such anticipated demand, can develop and sustain profitability (Boso et al., 2013; Teece et al., 1997). Certainly, different definitions concerning dynamic capabilities have highlighted that firms must integrate and reconfigure resources ‘to match and even create market change’ (Eisenhardt and Martin, 2000: 1107), otherwise the dynamic capabilities link to performance is decoupled in that they will not lead to superior performance (Helfat et al., 2007).

We suggest that firms must use their organisational processes either to adapt their product or service offerings to changing markets, or to create new markets for their innovative products or services. For example, Corner and Wu (2012) demonstrate how creating an innovative material led to prospecting for possible users and customers; working with these customers enabled the joint development of highly innovative products. Adaptation to new market demands or the creation of new markets is held to be a hallmark of SMEs as the basis for their nimbleness (Hadjimanolis, 2000). The term ‘market transformation’ has been used in the present study’s model as, taken together, the combination of new products and service releases (substantive and tangible changes in the market offerings of firms) and changes in advertising, distribution and the market locations served (perceptual and positional market changes) represent a transformation of the role of the focal firm in its market (Agarwal and Bayus, 2002; Srivastava et al., 2001). Empirically, this suggests a testable hypothesis:

H1: Firm market transformation concurrent with the development of dynamic capabilities will significantly influence improved performance.

Following on from the prior discussion of microfoundations, a review of the dynamic capabilities literature illustrates that the origins of these capabilities lie in fundamental firm processes that are intentionally routinised and/or bundled to form firm capabilities (Eisenhardt and Martin, 2000; Makadok, 2001; Teece, 2007; Zahra and George, 2002). Zollo and Winter (2002) suggest that this is underpinned by learning. However, this central positioning of systematic learning as providing the foundation for reconfiguring the firm’s resource base presents significant challenges, given the highly ambiguous nature of knowledge and the difficulties in managing knowledge creation and dissemination both within and between organisations (Desouza and Awazu, 2006). Therefore, investigations seeking to integrate the organisational processes and performance relationship often focus on issues relating to the efficiency of firm learning, as this is a vital determinant to create innovative responses to emergent contextual problems (Voudouris et al., 2011). An example is provided by Cohen and Levinthal’s (1990) absorptive capacity construct, which effectively could be synonymous with a firm’s learning ability (Cepeda-Carrion et al., 2012; Huang and Rice, 2009; Santos-Vijande et al., 2012).

With this central role of learning in mind, this article explores three organisational processes as ‘microfoundations’ of dynamic capabilities. These processes are based on perception of firm internal dynamics and prior contributions to theories of organisational learning, absorptive capacity and knowledge integration, with the aim to develop an appropriate set of empirical measures. The processes are as follows:

processual formalisation – the use of well-defined routines, plans, procedures and instructions embedded in firm processes (Cyert and March, 1963; Frezatti et al., 2011, Vega-Jurado et al., 2008);

the social integration platform – a learning mechanism informed by practices that facilitate knowledge and experience distribution and socialisation between individual members, enhancing the introduction of new knowledge and information (Subramaniam and Youndt, 2005; Vega Jurado et al., 2008; Zahra and George, 2002); and

information maturity – the application of IT embedded in organisational processes which facilitates organisational learning in terms of exploring, exploiting, sharing and repositioning knowledge. IT facilitates communication between members while accelerating the operations of routine organisation processes; it also has been shown to assist firms to enter new markets and stay abreast of relevant technology trends (Ashurst et al. 2012; Kane and Alavi, 2007; Robey et al., 2000).

Thus, the following hypotheses are proposed:

H2a: Firm intensity of use of processual formalisation processes, concomitant with market transformation activities, is likely to impact upon performance.

H2b: Firm upon intensity of use of social integration processes, concomitant with market transformation activities, is likely to impact performance.

H2c: Firm upon intensity of use of information technology processes, concomitant with market transformation activities, is likely to impact performance.

Method

Data

This article examines the hypotheses proposed earlier by utilising longitudinal data drawn from the Business Longitudinal Survey (BLS), which is available from the Australian Bureau of Statistics (ABS). The purpose of the BLS was to provide primary statistical information concerning the growth and performance characteristics of Australian industrial firms. The BLS provides four-year panel data (1994–1995 to 1997–1998, to coincide with the Australian financial years ending 30 June). It is remarkably comprehensive, providing data on company finances (revenue, expenditure, debt and assets), profiles on employment, training and education, and information on the use of management practices at the firm level. Firm-level microdata were released under the Australian Census and Statistics Act 1905 as a confidentialised unit record file (CURF, CD ROM Catalogue No. 8141.0.30.001). The BLS has been utilised previously in numerous studies of organisational and industry dynamics (e.g. Liao and Rice, 2010; Liao et al., 2011; Rice et al., 2012).

While the survey and data collection of the BLS was held in four waves, this study utilised the latter three years of the BLS data (from 1995–1996 to 1997–1998) to undertake the statistical analysis. Not all data was gathered in all years, and the data relevant for this study were present in the years 1995–1996 to 1997–1998. The panel data supplied by the ABS is restricted to Australian SMEs with fewer than 200 employees.

Sample

The sample size of the entire BLS CURF exceeds 9700 firms. We chose to limit the investigation to manufacturing firms (approximately 2400 in total), as certain relevant questions were asked only of this group. Furthermore, firms in the manufacturing sector generally exhibit real, formalised and tangible operational processes required to produce physical goods – issues of relevance for this study (McMahon, 2001). CURF was screened further due to some evident sampling and response errors (see details in ABS, 2000; McMahon, 2001), excluding firms with evident inconsistencies in their financial reporting, missing data with regards to firm age, sales and employee numbers, in order to maximise what the study judged to be full and complete firm-level responses. In addition, firms that were not self-identified as ‘legally organized’ were screened out (Hughes and Storey, 1994; McMahon, 2001). Finally, those firms that began or ceased operations during the survey were screened out. In the final analysis a subsample of 444 Australian manufacturing firms remained: the researchers were confident that these had reported their operational and financial information accurately.

Overall, the benefit of such a screening approach was that the selected firms within the subsample were comparable in terms of their operational and financial status, and that the operational and financial information provided by the respondents during the BLS survey years was able to be seen as effectively reflective of the operational and strategic initiatives undertaken by those firms.

Measures

The following measures were drawn or calculated from the raw data of the BLS CURF. In addition, justification of the measurement in unit dissimilarity was applied to each of the measures by use of the standardised score.

Information maturity. This study developed a measure, information maturity (INF), via two variables, including e-commerce capabilities (electronic commerce capabilities, INF1) and e-management capabilities (electronic managerial processes capabilities, INF2), in order to estimate the information intensity of a firm’s operational and managerial processes (Nonaka and Konno, 1998; Sabherwal and Becerra-Fernandez, 2003; Sher and Lee, 2004).

These two measures are drawn from the questions in the survey relating to IT practices. The first variable, INF1, describes the e-commerce practices used by firms through the purchase and sale of good and services via the internet, including ‘making payments’, ‘placing purchase orders’ and ‘receiving invoices’; as well as ‘the establishment of a website’, ‘promotional activities for marketing’, ‘receiving payments’, ‘sending invoices’, ‘receiving sales orders’ and ‘coordinating delivery arrangements’. The second variable, INF2, assesses the use of IT applications to support and facilitate managerial activities, including ‘interactive or online lodging of forms and tenders’, ‘business to business (B2B) data transfer’, ‘gathering information’, ‘business networking’ and ‘management intranet applications’.

Latent trait analysis was undertaken for information maturity, primarily utilising the Thurstone Scaling Approach. The following formula were developed to re-rate the above mentioned items into INF1 and INF2. Where necessary, question items were arranged into, and reported by, a dummy variable form by transforming the response to 0 (meaning the absence of a process) or 1 (presence) to ensure that dichotomous categories were indicated. This arrangement was necessary, as the direct use of dummy variables as endogenous variables for exploring a latent construct contravenes the assumption of normality that requires endogenous measures on the basis of non-nominal scales or quantities to be utilised in multivariate statistics.

I n f o r m a t i o n M a t u r i t y = W e i g h t_{I T p r o m o t i o n} \times \sum_{i = 1}^{m} I t e m W e i g h t_{i} \times D_{i}

D is used to indicate the dummy variables corresponding to the question items in the BLS CURF related to specific information intensity. ItemWeight is the Thurstone score (noted as the weightings of importance) assigned in advance to each IT-related question item. Weight _{IT promotion} is the weighting used to measure the degree of IT development for improving organisational processes; i represents the index of the dummy variables; and m represents the number of the dummy variables used to calculate the specific information intensity.

Using Thurstone scaling (with the mathematical form of ∑ ItemWeight_i × D_i) postulates that the means of the percentiles of those dummy variables on this scale follow a normal distribution that is reflective of the proportions of the respondents’ reactions to those questions in a dummy form – dependent on their own knowledge, background and experience (Dunn-Rankin, 1983). In other words, statistical analysis of the latent traits for a unidimensional data structure (e.g. the measures of INF1 and INF2 to the factor of information maturity in this study) can be re-rated by means of calculation of the sum (or the weighted sum) from the associated dummy variables (e.g. the above-mentioned IT-related dummy variables drawn from the BLS CURF).

The item weights (ItemWeight_i) developed by the use of Thurstone scaling serve as indicators of the importance or strength of influence of each question item in explaining a specific aggregate issue. In general, it is necessary for item weights to be defined in advance (often based on expert validity in the pre-survey processes) for planning and undertaking effective data analysis using the Thurstone scaling approach. While there is no prior published research using BLS data which has used Thurstone rescaling for information maturity (this is also true for the formalisation construct in the following section), the present study follows the assumption of equivalent importance for each IT-related item to estimate information intensities within organisations, hence setting all of the associated item weights to 1.

IT promotion weightings (Weight _{IT promotion}) are further defined as the weightings used to measure the degree of IT-related development. These weightings are derived by assessing those aspects of firm development in IT-related activities in the BLS CURF which had changed in comparison with the corresponding values in the previous survey year (1996–1997). For example, for INF1 the relevant IT promotion weightings are calculated by assessing changes in the developmental factor relating to the use of ‘internet applications for ordering and purchasing activities’, in order to ascertain changes to the use of e-commerce applications in firms. Similarly, the IT promotion weightings of INF2 were obtained by observing changes in the use of ‘IT applications in administrative system’ as a measure of the degree of change in the firms’ use of IT applications to assist managerial processes.

Processual formalisation. This study defines two variables, formal managerial intensity (PF1) and formal strategic intensity (PF2), to measure firm intensities regarding processual formalisation in terms of their use of well-planned routines (Cyert and March, 1963; Wiesner and Millett, 2012; Zahra and George, 2002) and strategic processes (Chakravarthy and White, 2002; Maritan, 2007) that contribute to firms’ capability development. These two measures are drawn from the questions in the BLS CURF relating to the use of formal managerial planning processes. PF1, the present study’s measure of the use of financial planning routines, is derived from questions relating to firms’ use of ‘budget forecasting’ and ‘regular income and expenditure reports’. PF2, the measure of the use of strategic planning processes, is gained from items measuring the use of ‘formal strategic plans’ and ‘comparison of performance with other businesses’.

As has been discussed, this study uses Thurstone scaling to re-rate these items (reported in the BLS CURF as dummy variables) into an interval scale form. In addition, we were very interested in capturing in the measures some degree of path dependency and accumulation that, we expect, is an important aspect of the processes’ presence in firms. The absence of such accumulative measures has been noted as a key weakness of much research in dynamic capabilities, much of which draws on cross-sectional data (Arend and Bromiley, 2009). As discussed previously, it was felt to be important to measure both the presence of dynamic capabilities and the accumulative effects of their presence in previous years of firm operation (Helfat et al., 2007; Zollo and Winter, 2002; Zott, 2003).

The measure incorporating these path-dependent impacts also sought to reflect the declining influence of prior processual formalisation activities over time (for example, we would expect that t-1 measures are more influential on current year activities than t-2, and so forth). In econometrics, the equational form that reflects this phenomenon generally would be a time-based function reflecting a decline in influence over time, following a downward trend in a linear-log fashion (Barr and Saraceno, 2005). Therefore, the processual formalisation measure was transformed by dividing the derived accumulation measures by the linear-log downward trending factor of log(j+2)/log(2) at each time lag.¹

The following formula was developed to integrate the corresponding measures of firm processes (i.e. PF1 and PF2) with a measure relating to the accumulation of benefit arising from their previous use.

F o r m a l i s a t i o n I n t e n s i t y = \sum_{j = 0}^{n} \sum_{i = 1}^{m} \frac{I t e m W e i g h t_{i} \times D_{i j}}{l o g (j + 2) / l o g (2)}

D is used to indicate the dummy variables corresponding to the question items in the BLS CURF related to the specific formalisation intensity. ItemWeight is the Thurstone score that is assigned to each formalisation-related question items drawn forth the BLS CURF. log(j+2)/log(2) is defined as the linear-log downwards trending factor to be reflective of the possible decaying effects of path dependency over time. j is used to indicate the specific time lag on an annual basis; n is the number of time lags that has been set to be 2 (i.e. from the financial years 1995–1996 to 1997–1998, since the time lag of the financial year of 1997–1998 is 0). i represents the index of the dummy variables, and m represents the number of the dummy variables used to calculate the specific formalisation intensity. The item weights (ItemWeight_i) for each formalisation-related item drawn from the BLS CURF are assumed to be of equivalent importance, hence an item weighting of 1 was assigned for each of the above items.

Social integration platform (SIP). This factor draws on the questions in the BLS CURF regarding the training and educational approaches adopted by firms that are aimed at facilitating effective knowledge distribution among and between employees. Four measures were adopted: the use of structured courses and programmes (SIP1), formal seminars and workshops (SIP2), on-the-job training (SIP3) and job rotation/duty exchanges (SIP4). These measures relate to questions of absorptive capacity, knowledge integration and learning (Cohen and Levinthal, 1990; Zahra and George, 2002), with a particular focus on the developmental mechanism of human resource knowledge stock (Graversen and Friis-Jensen, 2001; Wolter and Veloso, 2008). These variables were measured originally in five-degree intervals, with the levels of ‘none’ (value = 1), ‘up to 25%’ (2), ‘26–50%’ (3), ‘51–75%’ (4) and ‘76–100%’ (5). Each interval provided a measure of the proportion of employees participating in each of the education and training activities noted. As these measures were only reported in the final year of the survey (1997–1998) in the BLS CURF, they are included as a cross-sectional measure of their contribution to the efficiency of the market transformation and firm performance.

Market transformation. The central mediating construct in the model is a measure of market transformation (MT), which refers to the variable group in the BLS CURF that describes those actions undertaken by firms in terms of their market-based activities in the penultimate year of the survey (1996–1997). Four measures of market transformation broadly consistent with the Schumpeterian view of market disruption activities were adopted: changes in range of products/services (MT1), changes in advertising (MT2), changes in distribution of goods/services (MT3) and changes in market targets (MT4) (Brouwer, 1991; Cooper, 1984; Damanpour, 1991; Schumpeter, 1974[1943], 1982[1939]; Sivadas and Dwyer, 2000).

These measures were presented originally in the survey as nominal variables: ‘not applicable’ (value = 1), ‘no major change’ (2), ‘increased’ (3) and ‘decreased’ (4). To investigate market transformation in selected firms consistent with the linear analysis adopted in this study, these values were rescaled into Likert-like scales with four-degree intervals as follows: ‘increased’ (value = +4), ‘remained the same’ (+3), ‘decreased’ (+2) and ‘not applicable’ (+1). These were rescaled as we have presumed, a priori, that the presence of market transformation activities by industrial firms is generally and linearly the cause of emergent firm performance. It may appear at first to be odd that ‘not applicable’ was rescaled with a relatively lower rating than ‘decreased’: this was done as our interpretation of the questionnaire was that ‘not applicable’ would be a response provided when an activity was completely absent within a firm: an outcome that would be relatively ‘worse’ than an outcome where an activity was declining, but still ongoing.

Firm performance. This study measures firm performance (FP) via two variables: sales growth (FP1) and expected sales growth (FP2) (Bird and Beechler, 1995; Charan, 2004). These two variables were extracted from the BLS CURF for the latter two financial years in the survey (1996–1997 and 1997–1998) by utilising the following formula suggested by Helfat et al. (2007):

S a l e s G r o w t h_{t} = (S a l e s_{t} - S a l e s_{t - 1}) / S a l e s_{t - 1} .

t is used to indicate the specific time period; for this study it is based on an annual basis.

While various measures of firm performance have been proposed, including return on assets, return on equity and measures of relative firm productivity in this study, firm growth in sales revenue is the measure of firm performance. This reflects recent suggestions that growth in firm size is the most convincing proxy measure of sustainable firm growth, as it captures the ability to meet the needs and expectations of customers (Collins and Clark, 2003) while facilitating cash flow that ensures the viability of future productive activities into the future (Charan, 2004; Helfat et al., 2007; Miller et al., 2008).

This study also adds a measure of expectations for future sales revenue growth as an important aspect of firm performance. This is drawn from a question in CURF relating to expected percentage change in sales in the following year. It has been noted that the potential for optimistic growth expectations is an important antecedent element in firm performance (Hmieleski and Baron, 2008); in addition, managerial intentionality and perceptions of future outcomes may have an impact on the resourcing decisions required to create the internal and external resource allocations necessary for growth (Powell et al., 2006). These growth expectations could be used by managers to identify the strategic initiatives necessary to spur anticipatory investments in research and development (R&D), manufacturing and pre-emptive market activities. Accordingly, we suggest that firm growth expectations play an essential role in generating a developmental and growth orientation necessary (albeit insufficient) to determine both current and future success (Charan, 2004; Miller et al., 2008).

The proposed theoretical model can be represented as follows, with the Market Transformation (MT) construct mediating all relationships between Organisational Performance (OP) and the organisational processes constructs (Figure 1).

Figure 1.

Proposed theoretical model.

Analyses

The primary analytical technique to validate and test the proposed theoretical model is path analysis with latent variables (PALV, undertaken with AMOS 7.0 with the maximum likelihood (ML) method) in structural equation modelling (SEM). Some pre-analytical tasks relating to exploratory factor analysis in the investigation of the detailed data factor structures for the above-mentioned measures were conducted using SPSS 15.0. PALV is a causal modelling technique that integrates both path analysis and confirmatory factor analysis (CFA). Thus, a PALV model is an analytical method rooted in a latent model for the examination of causal relationships among latent variables, while also providing estimates for associated observed variables (Raykov and Marcoulides, 2001). Since a PALV model is established inferentially by means of a pre-proposed theoretical model, a statistically fitted PALV model can identify the possibility of the existence of such defined causal correlations between specific latent and observed elements in both theoretical and statistical terms (Bollen, 1989). With regards to the significance level, the study generally applied a level of 0.05 to the tests. In addition, the hypothesis tests were conducted in a right-tailed t-test of the mean, consistent with the theoretical model and hypotheses that were pre-defined.

Results

In presenting the evidence rigorously, this study first explored the data factorial structures for both the SIP factor and the MT factor. It then proceeded to assess the validity of latent constructs and model fit to ensure the statistical applicability of the theoretical model. Finally, the hypotheses raised in the study were tested to ascertain the causal relationships in the theoretical model.

Investigations on the implied data factorial structures

Exploratory factor analysis (EFA) was used to ascertain the data factorial structure of SIP and MT (Hair et al., 1995; Kim and Mueller, 1978) The factors of FP, PF and INF were excluded as they only involved two observed variables. The results of EFA established via the extraction method of Principal Component Analysis with Varimax with Kaiser Normalisation Rotation are shown in Table 1. In addition to the diagnoses indicating a better than mediocre level for both MT and SIP in factorial extraction (Kaiser-Meyer-Olkin [KMO] indices are at 0.664 and 0.641 levels, respectively; Bartlett’s tests appear to be statistical significant), a two-order data factorial structure in MT and SIP factors are revealed.

Table 1.

Results of exploratory factor analysis.

Factors	Sub-factors	Variables/items	Cumulative variance
MT	Changes in the width of market activities (MTW)	MT1: Changes in range of products/services	32.27%
	Changes in the width of market activities (MTW)	MT4: Changes in market targets
	Changes in the depth of market activities (MTD)	MT2: Changes in advertising	64.40%
	Changes in the depth of market activities (MTD)	MT3: Changes in distribution of goods/services
KMO index: 0.664
Bartlett’s test of sphericity: χ² = 133.39, d.f. = 6, p = 0
SIP	Formal programmes (SIPF)	SIP1: Structured courses and programmes	36.70%
	Formal programmes (SIPF)	SIP2: Formal seminars and workshops
	Informal activities (SIPI)	SIP3: On-the-job training	72.61%
	Informal activities (SIPI)	SIP4: Job rotation/duty exchanges
KMO index: 0.641
Bartlett’s test of sphericity: χ² = 257.16, d.f. = 6, p = 0

Note: Extraction method: principal component analysis; rotation method: Varimax with Kaiser normalisation.

Based on analysis of the MT factor, the results of factor extraction show that four variables (MT1~MT4) can be classified into two sub-factors at a level of total 64.40 percent cumulative explained variance. These two sub-factors are labelled as: changes in the width of market activities (MTW), of which MT1 and MT4 are involved; and changes in the depth of market activities (MTD), which includes MT2 and MT3. These sub-factors are consistent with the fundamental attributes of those activities relating to the market transformation in business operations.

In terms of the SIP factor, with a total cumulative explained variance at a level of 72.61 percent in factorial extraction, two additional sub-factors have been identified. The first, formal programs (SIPF), consists of the observed variables of ET1 and ET2; while the second, informal activities (SIPI), involves ET3 and ET4. Based on these EFA results emerging from the theoretical model, the study assesses the corresponding measurement model to evaluate construct validity, while further building the latent structural model for later hypothesis tests.

Assessment of construct validity

An assessment of the measurement model was undertaken using SEM analysis, through the first-order CFA hypothetical model, to assess the model’s statistical adequacy based on internal consistency, convergent validity and discriminant validity (Chin, 1998; Cronbach and Meehl, 1955). These assessments were undertaken to validate the composite reliability (CR) and the average variance extracted (AVE) of the model’s constructs. A recommended CR threshold of 0.6 indicates the presence of sufficient internal consistency (Bagozzi and Yi, 1988); an AVE of 0.5 is often used as a benchmark that shows competent convergent validity (Fornell and Larcker, 1981), while in practice, a construct’s significant convergent validity also can be identified when its observed variables’ item factor loadings are all above 0.5 and statistically significant (Anderson and Gerbing, 1988; Dunn et al., 1994; Nunnally, 1978). For discriminant validity, a qualified test exists relating to the square root of AVE of a construct being greater than the correlation between itself and others (Chin, 1998).

According to the theoretical model and the results of EFA, a first-order CFA hypothetical model was built for the assessment of construct validity. The results, documented in Table 2 and Table 3, show that all of the relevant diagnostic thresholds regarding internal consistency, convergent validity and discriminant validity are met, and thus considered statistically adequate. The CR measures indicate that all latent constructs present qualified internal consistency, with all of the values within the range of 0.603~0.682. In addition, convergent validity for each construct is considered competent, as all of the item factor loadings for each construct exceed the benchmark and present as strongly significant (p<0.001). For discriminant validity, the correlations between any two constructs are less than the corresponding square root of AVE presented in the specific construct, hence indicating the sufficient discriminant validity for each latent construct. Overall, the results of CFA suggest that the theoretical constructs informing the study are confirmed by, and fit, the sample appropriately.

Table 2.

Results of first-order confirmatory factor analysis.

Constructs and measurement items		Item factor loadings
Organisational processes (OP)
Social integration platforms (SIP):
Formal programmes (SIPF)	SIP1: Structured courses and programmes	.70^***
Formal programmes (SIPF)	SIP2: Formal seminars and workshops	.65^***
Informal activities (SIPI)	SIP3: On-the-job training	.77^***
Informal activities (SIPI)	SIP4: Job rotation/duty exchanges	.57^***
Processual Formalisation (PF):
PF1: formal managerial intensity		.51^***
PF2: formal strategic intensity		.70^***
Informationisation (INF):
INF1: e-commerce capabilities		.58^***
INF2: e-management capabilities		.84^***
Market Transformation (MT):
Changes in the width of market activities (MTW)	MT1: Changes in range of products/services	.75^***
Changes in the width of market activities (MTW)	MT4: Changes in market targets	.65^***
Changes in the depth of market activities (MTD)	MT2: Changes in advertising	.68^***
Changes in the depth of market activities (MTD)	MT3: Changes in distribution of goods/services	.74^***
Firm Performance (FP):
FP1: Sales growth		.67^***
FP2: Expected sales growth		.67^***
CFA diagnoses
χ² = 61.507, d.f. = 56, p = .285, Goodness of Fit (GFI) = .981, Adjusted Goodness of Fit (AGFI) = .965, Root Mean Square Error of Approximation (RMSEA) = 0.015

***

p<0.001; ** p<0.01; * p<0.05.

Table 3.

Measures of composite reliability and correlations between constructs.

First order latent construct	SIPF	SIPI	PF	INF	MTW	MTD	FP
SIPF	CR = .630
	AVE^0.5 = .678
SIPI	.547	CR = .629
		AVE^0.5 = .682
PF	.573	.259	CR = .603
			AVE^0.5 = .650
INF	.429	.153	.332	CR = .682
				AVE^0.5 = .724
MTW	.040	.213	.282	.201	CR = .667
					AVE^0.5 = .710
MTD	.105	.202	.174	.147	.704	CR = .675
						AVE^0.5 = .714
FP	191	.190	.247	.219	.420	.373	CR = .625
							AVE^0.5 = .675

Model validation

For analysis in SEM, following the suggested arrangements of Jöreskog and Sörbom (LISREL software) and Hu and Bentler (1999), some cut-off criteria are considered to be satisfied:

test statistics of χ² are at an insignificant level or χ²/d.f.<2 since considering the complexity of the model;

Goodness of Fit Index (GFI) and adjusted GFI (AGFI) are at least at the level of 0.9;

Incremental Fit Index (IFI) is considered over 0.90;

the threshold of Comparative Fit Index (CFI) is the 0.95 level;

the Root Mean Square Error of Approximation (RMSEA) is considered better with lower values,

while often a benchmark of 0.05 is used to identify a close model fit, a 0.08 level is usually viewed as a reasonable fit (as per Bollen and Long, 1993);

the absolute values of standardised residual covariance between any two observed variables should be less than 1.96 in absolute value.

A PALV model was built according to the causal relationships defined in the theoretical model and the EFA results. The model fit diagnoses show that the model’s specification is considered satisfactory. The test statistics of χ2 (see Table 4, Model 1) is statistically insignificant at a 0.05 significance level (d.f. = 68, χ² = 85.221, p = 0.072). Other indices (including GFI = 0.975, AGFI = 0.961, IFI = 0.984, CFI = 0.983 and RMSEA = 0.024) also reach the benchmarks of competent model goodness of fit. In terms of the criterion of residual covariance, all of the standardised residual covariances in any observed variable pair present a level less that 1.96 in absolute value. While the sample size is deemed sufficient (n = 444), residual covariances are considered normally distributed: that is, reflective of the model’s statistical correctness.

Table 4.

Assessment of model fit and structural path coefficients.

Diagnoses and measurements	Mode1 1	Model 2	∆χ²
Model fit
χ²	85.221	83.725	1.496^ns
d.f. (p)	68(.072)	67 (.081)	1 (.221)
GFI	.975	.975
AGFI	.961	.961
IFI	.984	.984
CFI	.983	.984
RMSEA	.024	.024
Path coefficients
Market transformation to firm performance (MT→FP)	.38^***	.34^***
Organisational processes to market transformation (OP→MT)	.36^***	.34^**
Organisational processes to firm performance (OP→FP)	n.a.	.11^ns (p = .231)

***

p<0.001; ** p<0.01; * p<0.05; (ns) p>0.05.

Note: Model 1: excluding the path ‘OP→FP’; Model 2: Including the path ‘OP→FP’.

Proposed model selection

According to the theoretical model, we presume that market transformation activities mediate the relationship between organisational processes and firm performance; the anticipated direct effect between these two constructs is recognised (Spicer and Sadler-Smith, 2006). We investigated these possible direct and mediated relationships. Regarding the direct effect of OP on FP, two models are provided (Table 4), where model 1 (the hypothetical model according to the research framework) excludes the direct effect of OP on FP (OP→FP); model 2 is then presented, referred to as the ‘competing model’ including the direct path of OP→FP.

The test results overall show that these two models do not differ from each other. The chi-square difference test statistics appear to be insignificant at a 0.05 significance level (∆χ² = 1.496, d.f. = 1, p = 0.221), indicating that the null hypothesis – that model 1 and model 2 are statistically appropriate – is not violated. In addition, in model 2, the estimate of the path of OP→FP presents at an insignificant level (standardised regression weight = 0.11, p = 0.231), showing that the unmediated path between OP and FP in model 2 should be disregarded from a statistical standpoint. As a result, this study refers to model 1 as the proposed model (Figure 2).

Figure 2.

Proposed model (in the standardised mode).

Hypothesis tests

In order to test the hypotheses, the direct and indirect effects on FP from MT and OP were assessed. H1 suggested a direct relationship between firm performance and market transformation. The test results (see Figure 2) show that the standardised path coefficient of MT→FP is 0.38 (p<0.001), indicating that firm performance is immediately and positively influenced by the variance in market transformation. H1 gains support from the sample.

H2a, H2b and H2c suggested that firm managerial activities and arrangements, including PF, INF and SIP, enhance organisational processes, and consequently firm performance. The results indicate that in OP construct, all of the sub-factor loadings are strongly statistically significant (standardised weights: OP→ISP = 0.82, OP→INF = 0.55 and OP→PF = 0.73 with p<0.001), suggesting that the variance of OP is shared collectively by the sub-factors of SIP, INF and PF. That is, the latent structure of organisational processes is proven to be an aggregation of social integration platforms, information maturity and processual formalisation. H2a, H2b and H2c, which suggested that firm efforts in managing organisational processes by means of enhancement in social integration platforms, information maturity and processual formalisation positively contribute to firm performance, are supported within this sample. However, this is evident only when mediated by market transformation.

Discussion

The main intent of this study has been to provide rigorous empirical evidence to link organisational processes to firm performance, while demonstrating the essential role of market transformation in establishing this nexus. Based on the evidence, SME firm performance does indeed vary positively in line with commitments to effective organisational processes, with this impact mediated by the presence or absence of market transformation activities. Consistent with the hypotheses, the results suggest that efficacy in managerial and organisational processes, supported by the effective deployment of IT resources and applications, processual formalisation and social integration platforms, confer an adaptive advantage in markets which in turn, leads to superior performance.

A critical contribution of the article lies within extending the literature relating to the manner in which firms develop dynamic capabilities. In particular, the present model reshapes the dynamic capabilities approach, to some extent, by establishing the common mediating role of market transformation. The study has found strong evidence that such market engagement mediates between the external and internal dynamics of the firm. Considering the relationships between firm performance and market transformation, the results indicate that efforts in market transformation enhance performance; this also implies that economic motives are driven by a pull force resulting in market changes. Axiomatically, private firms cannot survive without desirable market offerings (e.g. products and/or services); since market demand continuously evolves, firms are required to change to meet the emergent demands of customers or to ensure the creation of new markets that align with their offerings. Such findings are supportive of theories developed by Eisenhardt and Martin (2000) and Zott (2003), who proposed models that link resource transformation and reconfiguration to firm performance. While their contributions are conceptually important in bridging the link between dynamic capabilities and performance, there are limits in their work concerning the manner in which dynamic capabilities may have an impact on performance via transformation of the manner in which the firm engages with the market. This proposed mediated nexus, between organisational processes as the microfoundations of dynamic capabilities and firm performance, points to the necessity of a firm’s dynamic capabilities operating in conjunction with tangible steps to engage in market transformation. These proactive measures to reorganise internal resources in anticipation of emerging market dynamics have been shown to be influential in the creation of firm performance.

Conclusion

Clearly, concrete resource commitments and the effectiveness of organisational processes underlie a firm’s deployment of dynamic capabilities. This study provides tangible measures of particular organisational processes in its model in terms of information maturity, processual formalisation and social integration platforms. These organisational processes, which are defined and measured in dynamic form, may be expected to contribute to firm evolution in terms of internal systems, and hence market presence and position (and thus, transformation).

From a statistical perspective, this study has revealed a two-order hierarchical structure within both the factors of market transformation and social integration platforms. With reference to market transformation, two sub-factors have been explored: ‘changes in the width of market activities’ – referring to a firm’s market evolutionary strategy and activities aimed at extending market scope (for example, through the increase in product/service range) and extending market targets; and ‘changes in the depth of market activities’ – referring to market operational foci in terms of enhanced access and information through means including advertising and enhanced distribution. In terms of ‘social integration platforms’, the study has identified two sub-factors: ‘formal programmes’ and ‘informal activities’. The former is used to describe a firm’s learning activities undertaken in the form of planned courses conducted either externally or internally; the latter is linked to learning-by-doing approaches intended to extend employee knowledge at the workplace.

Regarding the central role of market transformation, it is important to note that the model testing a direct path from organisational processes to firm performance is not significant: the relationship required the mediator of market transformation to produce significant results. This has implications regarding the common definitions of dynamic capabilities. Teece et al. (1997) and Eisenhardt and Martin (2000) and more recently, Teece (2007) all present definitions that specifically highlight the need to meet (or create) market emergence and change. Alternate definitions capture the core notion of adapting and reconfiguring the firm’s resources base, but fail to incorporate explicitly the notion of market transformation. For example, Helfat et al. suggest that ‘a dynamic capability is the capacity of an organisation to purposefully create, extend, or modify its resource base’ (2007: 4). In theory, the market component within the definition is not required, as Helfat et al. (2007) go on to suggest that dynamic capabilities require essentially the same (VRIN: Valuable, Rare, Inimitable, Non-substitutable) conditions to be met as those posited by Barney (1991), whereby the notion of value highlights the need to meet market demands appropriately.

In respect of organisational process, it is certainly not proposed that these factors are necessarily the most important, or only, set of factors affecting the development and deployment of dynamic capabilities. However, they do represent a set of commonly occurring processes that will apply to many organisations, and the results highlight how changes over time subsequently lead to market transformation. This cumulative effect observed in the data is a reflection that organisational learning occurs to inform the development of dynamic capabilities.

In terms of the context of the study – namely, SMEs – the fundamental contribution demonstrates that they select fundamental organisational processes which act as microfoundations to dynamic capabilities, allowing for effective market transformation that does achieve performance improvements. In doing so, the study has complemented the many qualitative and conceptual papers within the SME domain (for example, Woldesenbet et al., 2012) to demonstrate how SMEs can configure often simple resource stocks and activities in such a way to achieve many of the complex procedural processes that evidence the presence of dynamic capabilities within organisations.

Limitations of the study and suggestions for future research

This study deliberately and carefully screened data from the BLS to ensure that the selected sample drawn from Australian manufacturing SMEs could be considered robustly reported by firm respondents. While the federal legislation under which these surveys are undertaken requires truthful responses by managers, attention to the task of responding to the surveys may vary. In screening for absent and illogical responses, we feel that we considerably improved the quality of the data. Further, we screened firms according to their reported operational and accounting data to ensure that the sample included only firms that we considered were operating in a ‘normal’ and generally financially sustainable manner.

Although the research is important and provides some of the most notable evidence yet of the performance relevance of dynamic capabilities, some questions exist regarding the ability to generalise the research due to its context within the Australian manufacturing industry. Questions may exist regarding the peculiarities of the Australian manufacturing context during the survey phase, although in many respects the Australian SME manufacturing sector is quite similar to others in developed economies in Asia, Europe and North America in terms of its relative contribution to employment and economic productivity (Cooke, 2002). The lack of a unique setting (other than possibly the geographical setting) is, in fact, the strength of the data. The firms included are the type of manufacturing firms that could be found in any town or city in an advanced economy, as the firms come from a variety of industry sectors and are exclusively SMEs. Nonetheless, replications of the study in other jurisdictions may be valuable.

Another notable limitation emerges from the use of secondary data. As researchers we were completely constrained by the questions asked by the ABS. This is counterbalanced to some degree by the quality of the data which, if necessary, is coercively gathered under legislative powers provided to the ABS (hence eradicating non-response issues), while also requiring candid and correct responses under risk of prosecution for incorrect responders. Nonetheless, other, wider measures of business processes, market transformative activities and indeed firm performance may provide more items of relevance in future research studies.

One of the strongest empirical contributions of this study arises from its exploitation of longitudinal data, so avoiding some of the pitfalls when estimating a cumulative effect with cross-sectional data. The two sets of observed measures used to measure the degree of information maturity and processual formalisation of firms were developed using the Thurstone Scaling Approach. In particular, the measures of processual formalisation were developed with the notion of path dependency in mind; the presence of which suggests the existence of an accumulation effect on the impact and value of organisational processes over time. These re-scoring approaches may provide valuable strategies to future researchers seeking to operationalise measures that are innately cumulative and path-dependent.

In conclusion, this study presents an important addition to existing empirical research into the dynamic capabilities approach for SMEs, as well as highlighting a critical mediating variable which may lead us to partially rethink the way that we conceptualise and define dynamic capabilities. Through this study, we are able to provide a much clearer picture of the nexus between firm performance and dynamic capabilities. The present model may also be useful to researchers in understanding those formative elements of dynamic capabilities which, in turn, provide the building blocks of performance and sustained competitive advantage.

Footnotes

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

Notes

Author biographies

John Rice is Professor of Management at the UNE Business School at the University of New England in Australia. His research interests include innovation, sustainability and strategic management.

Tung-Shan Liao is Assistant Professor at Yuan-Tze University Business School. His research has investigated the micro-foundations of dynamic capabilities and the emergence of the Taiwanese manufacturing sector.

Peter Galvin is a Professor of Strategic Management at Curtin University. He is presently Editor-in-Chief of Journal of Management & Organization. His research interests cover innovation, firm capabilities, boundaries of the firm and organization design.

Nigel Martin is Senior Lecturer at the College of Business and Economics, Australian National University. His research interests span strategic management, business sustainability, technology management and computer security and privacy.

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