Flexible Use of Resources in the Process Development on a Firm Level and its Impact on Innovation Performance; A Meta-Analysis

University of Groningen

Faculty of Economics and Business

Strategy & Innovation Management

Nettelbosje 2 9747 AE Groningen

The Netherlands

Flexible Use of Resources in the Process Development on a Firm Level

and its Impact on Innovation Performance; A Meta-Analysis

June, 2015

MSc Thesis - Strategy & Innovation Management by:

Martin Šafařík - s2748320 m.safarik@student.rug.nl Kraneweg 4 9718 JP Groningen +31 6 44381629 Word count: 13 102

Innovation performance literature suggests that flexible use of processes within a company may be one of the correct answers to overcome substantial and uncertain changes in the environment. In order to understand the factors affecting flexibility, identification of different dimensions of flexibility is necessary. Resources as a source of competitive advantage and their flexible usage represent one of such dimensions. Research, however, provides contradictory findings on the relationship between resource flexibility and innovation performance. The goal of this study is to find out the impact of resource flexibility on innovation performance on a firm level. On the basis of literature review, yielding antecedents of resource flexibility, a meta-analysis was conducted to assess the generalisability of the relationship between the resource flexibility and innovation performance. By examining a final set of 18 papers, four meta-factors were detected - resource flexibility, slack resources, functional flexibility, and numerical flexibility. The results show positive significant effect on the relationship of resource flexibility and innovation performance. Additional moderator analysis revealed one successful moderator on functional flexibility, and three successful moderators on numerical flexibility. At the end of the study theoretical and managerial implications are discussed and future research directions are proposed.

1. Introduction ... 1

2. Literature and Theoretical Background ... 3

2.1. Innovation Performance ... 3 2.2. Flexibility Dimensions ... 4 2.3. Resources ... 5 2.3.1. Resource Flexibility ... 5 2.3.2. Slack Resources ... 6 2.4. HRM Practices ... 7 2.4.1. Functional Flexibility ... 7 2.4.2. Numerical Flexibility ... 8

3. Data Collection and Methodology ... 9

3.1. Data Collection ... 9

3.2. Distillation Process ... 10

3.3. Data Analysis ... 10

4. Results ... 12

4.1. Results of the Main Meta-Analysis ... 12

4.2. Moderators ... 13

5. Conclusions and Implications ... 15

5.1. Theoretical Implications ... 15

5.2. Managerial Implications ... 16

5.3. Limitations ... 17

5.4. Future Research Directions ... 18

6. References ... 18

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1. Introduction

As the product innovation processes are carried out in extremely uncertain and dynamic environments, many researchers and practitioners have been trying to find an answer to the question of what is the best approach to handle these conditions. An important concept in innovation performance literature, with an increasing number of emerging studies, is the concept of flexibility. It has been defined as a firm’s ability to adapt to substantial and uncertain changes in the environment that requires quick reactions and has a significant impact on innovation performance (Aaker & Mascarenhas, 1984; Verdú-Jover et al., 2005). Nevertheless, the flexible structure does not seem to have a strictly positive impact on a firm’s innovativeness. Therefore, researchers and practitioners have been trying to empirically test the relationship between flexibility and innovativeness, introducing often contradictory results. The increasing attention of the concepts of innovation performance and flexibility and the presence of contradictory results comes with a calling for summarizing the empirical findings that have appeared.

As it has been stated in literature, the concept of flexibility is multidimensional, since a firm can be more flexible in some activities and less flexible in others (Suarez et al., 1996). In order to that, Biazzo (2009) introduces three dimensions of flexibility on a project level in his study; 1) the organisational dimension, 2) the informational dimension, and 3) the temporal dimension.

Despite the extensive work that has been carried out by Biazzo (2009), the three introduced dimensions do not cover the whole construct of flexibility and other dimensions can be found. For instance, resources are of high importance for firms as they have a potential to become a source of competitive advantage (Eisenhardt & Schoonhoven, 1996; Sahlman & Stevenson, 1986). Also, resource availability and their allocation during the new product development have an undoubtable impact on innovativeness of a firm (Liu et al., 2009; Su et al., 2011; Wei et al., 2014).

Putting the concepts of flexibility and resources together composes a dimension of flexibility – resource flexibility. It is characterised as the range of alternative uses to which a resource can be applied, the costs and difficulty of switching from one use of the resource to another, and the time required to switch one use of the resource to another (Suarez et al., 1996). The flexible use of resources can be represented by different types of resources, such as physical capital resources, human capital resources, and organisational capital resources (Barney, 1991).

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of an entrepreneurial strategy making. In contrary, Kraatz & Zajac (2001) found that resources that provided a company with competencies in the past can create competency traps when environmental conditions change. Competency traps may make an actor resistant to explore new possibilities and opportunities (Alvarez et al., 2013).

Despite of the fact that Biazzo (2009) introduced the three dimensions on a project level, this study takes a step back and embraces the subject on a firm level. Mixing firm and project level is improper as it may produce ambiguous results, because those two levels may have different predictor-performance measures (Pattikawa et al., 2006). A firm level was selected for following reasons. First, most of the literature on resource flexibility and innovation performance originates on the firm or organisational level. Secondly, in practice managers are able to apply the results from the organisational perspective in an easier way. Thirdly, to the best of author’s knowledge this is the first systematic quantitative effort that has been done on this level on the inconsistent results of this particular dimension of flexibility and innovation performance. To conduct the meta-analysis the protocol of Hunter & Schmidt (1990) is followed.

To sum up the above mentioned, this study aims to contribute to literature in innovation performance literature in following ways. First, to answer Biazzo’s (2009) calling for unifying the inconsistent and contradictory results regarding the relationship between flexibility and innovation performance. Second, in order to do so, the goal is to introduce resource flexibility dimension and explore its impact on innovation performance on a firm level.

Therefore the research question is:

What is the impact of resource flexibility on the innovation process on a firm level; and what impact does it have on innovation performance?

For purposes of this study a final number of 18 studies were detected containing 40 possible variables that met the requirements. These studies, which were published between 1996 and 2014, resulted in four meta-factors (resource flexibility, slack resources, functional flexibility, and numerical flexibility).

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2. Literature and Theoretical Background

For purposes of this study, a literature review and a meta-analysis were performed. In the following section the concepts behind this study are explained. Firstly, flexibility and its dimensions are introduced, following by innovation performance and the concept of resources. Then, the four meta-factors detected in the meta-analysis are explained in following order; resource flexibility, slack resources, functional flexibility, and numerical flexibility. An overview of the relationships tested in this study is presented in the conceptual model (Figure 1). A description of the process of treatment of the meta-factors is explained in the methodology section.

Figure 1. Conceptual model of the relationships between the detected types of resource flexibility and innovation performance

2.1. Innovation Performance

In today’s fast changing world full of uncertainties, it is crucial for firms to stay innovative. Nokia (van Rooij, 2015) or Polaroid (MacRae, 2002) having lost their market positions can serve as examples that even such worldwide well-established companies should not underestimate the importance of innovation. Innovation is defined as the multi-stage process whereby organisations transform ideas into new or improved products, service or processes, in order to advance, compete and differentiate themselves successfully in their marketplace (Baregheh et al., 2009).

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embedded in a product, new R&D resources, and/or new production processes for a firm (Garcia & Calantone, 2002). On the other hand, administrative innovations are indirectly related to the basic work activity and more directly related to its managerial aspects such as organisational structure, administrative processes, and human resources (Crossan & Apaydin, 2010).

2.2. Flexibility Dimensions

Flexibility has been a highly emerging topic in literature about innovation performance, which have resulted in various definitions. Upton (1994) defined flexibility as the ability to change or react with little penalty in time, cost or performance. Further, flexibility has been referred to as a firm’s ability to adapt to substantial and uncertain changes in the environment that requires quick reactions and which has a significant impact on performance (Aaker & Mascarenhas, 1984; Verdú-Jover et al., 2005). Some similar features can be observed in these definitions. For instance, flexibility is referred as a tool that can improve efficiency, which is of a firm’s great interest. Moreover, both definitions mention the direct effect of flexibility on firms’ performance. The relationship between flexibility and innovation performance is explained below as it is the main interest of this study.

Flexibility is an important antecedent of innovativeness (Biazzo, 2009; MacCormack et al., 2001; Thomke, 1997; Verdú-Jover et al., 2005). Many firms struggle in their innovation processes because they actually adhere a rather rigid vision, and they fail to perceive their relationship with the environment correctly (Verdú-Jover et al., 2005). Yet, the potential benefits of flexibility are numerous. First of all, firms that strive to create flexible processes are able to innovate in more complex way using less resources (Thomke, 1997; Wheelwright & Clark, 1992). Next, it has been found that flexibility enhances the possibility to react quickly and systematically on obtained information and adapt the procedure to fast changing environments (Iansiti & MacCormack, 1996). Also, the product definition is postponed to later stages in flexible models and stages in the new product development often overlap. This allows better and faster adaption of the newly developed product (Iansiti, 1995). Yet, despite of the fact that literature about flexibility in innovation is well developed, the results are not unified and some studies offer contradictory results. For example, Thomke (1997) found that high flexibility enabled designers to tolerate high levels of risk, whereas low flexibility resulted in significantly higher resource investments that were aimed at minimising the risk of design changes. Moreover, according to Bacon et al. (1994) the product definition is a process that must be systematically and structurally managed throughout the entire product development process.

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not focused on the whole concept of flexibility but on one dimension within flexibility – dimension of resource flexibility. The importance of concept of resources is introduced in the next section.

2.3. Resources

As resources and their possible flexible use are the focus of this study, it is important to provide an adequate introduction to this concept. Resources can be defined as all assets, capabilities, organisational processes, firm attributes, information, knowledge controlled by a firm that enables the firm to conceive of and implement strategies that improve efficiency and effectiveness (Barney, 1991; Wernerfelt, 1984). Selznick (1953) additionally identified and emphasised the need for organisational leaders to identify and emphasise the importance of unique resources of organisations.

The increasing awareness of importance of resources has resulted in several theoretical concepts. A resource-based view stresses the important differences in the resource endowments of firms within an industry (Barney, 1991; Elsenhardt & Martin, 2000; Mahoney & Pandian, 1992; Wernerfelt, 1984). Because different levels of resource endowments affect potential alliance partner selection. A resource-based view stresses a use of in-house resources, nevertheless, it concedes a positive contribution of an alliance when a firm does not possess essential resources (Eisenhardt & Schoonhoven, 1996; Hitt et al., 2000), because when a firm possesses the resources in-house and they comply with the VRIN framework (in other words, they are valuable, rare, inimitable and non-substitutable), a sustainable competitive advantage can be achieved (Barney, 1991; Lin & Wu, 2014; Su et al., 2011; Talaja, 2012). Also, resources serve as a building block of the PROFIT framework introduced by Morris et al. (2001). The framework is categorised by six key resources that are needed for firm’s success; physical, relational, organisational, financial, intellectual & human, and technological resources. Moreover, resources play an important role in the strategy literature as the SWOT tool was developed. This tool enables a preliminary qualitative sensitivity analysis of the product sustainability by looking at strengths, weaknesses, opportunities and threats (Ansoff, 1970; Kraatz & Zajac, 2001; Pesonen & Horn, 2014).

2.3.1. Resource Flexibility

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With regard to innovation performance, flexible operation with resources may be beneficial to firms for several reasons. For instance, more flexible use of resources in uncertain environments brings to firms more market opportunities (Sanchez, 1995). Also, having the ability and resources to respond adaptably to uncertain circumstances may help firms to reduce costs and time in reaction to external environmental changes (Koste et al., 2004; Liu et al., 2009). Furthermore, extended usage of resources allows firms to detect even additional adaptive resources which they can further benefit from even more (Das, 2001; Souder et al., 1998). In addition, it has been found that expanding of adaptation of resources and their possible combining enhance efficiency and increase product introduction capability (Grant, 1991; Liu et al., 2009).

The flexible use of resources can be represented by different types of resources, such as physical capital resources (technology used, firm plant and equipment, and geographic location), human capital resources (training, experience, judgement, intelligence, relationships and insight of individual managers and workers), and organisational capital resources (formal reporting structure, formal/informal planning, controlling and coordination systems, informal relations among groups within a firm and its environment) (Barney, 1991).

Despite of the detected advantages of flexible use of resources not only positive effects towards innovation performance are shown. Low RF allows only narrow use of resources, which may cause difficulties in transforming the functions because of high asset specialisation (Sanchez, 1997). Hence, firms should enhance the flexibility to increase the usage of the resource and meet the requirements they had set for themselves (Liu et al., 2009). Nevertheless, when the resource flexibility crosses a certain level, companies can be caught into ‘competencies traps’ (Alvarez et al., 2013; Kraatz & Zajac, 2001). These traps represent situations when a company pursues in exploiting resources at the expense of exploring new possibilities (Levinthal & March, 1993). Therefore, besides indisputable benefits that RF brings towards innovativeness, it can also have negative impact.

2.3.2. Slack Resources

Contrary to employed resources that are defined and discussed in the previous section, slack resources refer to the stock of additional and unused resources available to an organisation during a given planning cycle (Bromiley, 1991; George, 2005; Nohria & Gulati, 1996). The link between slack resources and flexibility is indicated in Yang et al. (2014), where it is stated that the more slack resources a firm possesses, the better and more flexible strategic decisions can be made. Moreover, the use of free available resources can serve as a flexible tool to eliminate firm performance volatility (George, 2005; Voss et al., 2008).

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et al., 1996). Furthermore, Troilo et al. (2014) found discretionary resources to have positive affect on radical innovation, as its perceived availability encourages employees to pursue their ideas and projects while feeling less constrained. Moreover, slack allows a firm to bring to life those projects that would not come through a firm’s internal selection, but are supported by innovation champions or theorists (Nohria & Gulati, 1996).

Slack may be represented in a company as residual resources, underdeveloped opportunities, cash flows, accounts receivable, inventory, machinery, or equipment (Yang et al., 2014).

As stated before, slack helps firms to operate in extremely dynamic environments. Nevertheless, many opponents of slack state that companies should rather eliminate discretionary resources. One reason is that an increasing global competition forces organisations to eliminate all forms of slack in order to stay more efficient (Nohria & Gulati, 1996). Some researchers also state that experimenting with slack encourages investments that rarely bring economic benefits (Jensen, 1993; Leibenstein, 1969), hence it is an unnecessary cost (Nohria & Gulati, 1996). Therefore, it is not surprising that some contradictory results have appeared on the relationship between slack and innovation.

2.4. HRM Practices

HRM practices are divided into three subgroups; functional flexibility, numerical flexibility, and wage or reward flexibility (Kleinknecht, 1998; Michie & Sheehan, 2003; Zhou et al., 2011). As the wage or reward flexibility is not related to the flexible use of resources, only functional and numerical flexibility are taken into consideration in this study, and they represent the third and fourth meta-factor detected in this study. Human resources (HR) are valuable assets for firms mainly for their firm-specific, socially complex, and path-dependent features (Wright et al., 2001; Youndt et al., 1996). Moreover, it has been found that when a firm shows a commitment to employees, it results in higher firm performance (J. Lee & Miller, 1999). Link between HRM and flexibility can be seen through many particular practices within HRM that allow an organisation for flexibility in the use of labour force to diminish effects of external uncertainties. Also, HR is of a big importance to the field of innovation, as each idea or other type of innovation initiative is highly dependent on employee’s knowledge, expertise, or other commitments that serve as an input to the value creation (Wright et al., 2001; Youndt et al., 1996). Below, functional and numerical flexibility are explained.

2.4.1. Functional Flexibility

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where it is presented as ‘the ability to vary the used labour’. Moreover, the used labour is ‘able to carry out a wide range of tasks’, which can be seen as another feature of flexibility.

Functional flexibility is also of a great importance to innovation performance. Enhancing the ability to accomplish multiple activities contributes to a broader dispersion of skills and knowledge of employees, which in turns helps employees to be more adaptable and eventually positively affects innovativeness (Arvanitis, 2005; Martínez‐Sánchez et al., 2011). Also, the psychological effect plays an important role in the relationship between functional flexibility and innovativeness. Activities such as multi-tasking or cooperation reduce monotonous and repetitive work and increase productivity through commitment and development of core employees (Martínez‐Sánchez et al., 2011). Moreover, high functional flexibility can enhance communication across different departments through better sharing and transferring the knowledge, which can also lead to better innovation performance (Kleinknecht, 1998).

Functional flexibility reflects those activities of workers that include multiple competencies, e.g. multi-skilling, multi-tasking, cooperation, or the involvement of workers in decision-making (Arvanitis, 2005). Moreover, activities such as training, team work, or participation are also included (Lau & Ngo, 2004; Shipton et al., 2006; Zhou et al., 2011).

The relationship between functional flexibility and innovation performance was found to be strictly positive across the studies that were detected in this paper (Arvanitis, 2005; Michie & Sheehan, 2003; Shipton et al., 2006; Zhou et al., 2011). Nevertheless, big differences of the importance of HRM practices related to functional flexibility are present. For example, while in the study of Zhou et al. (2011) the importance of training variable is demonstrated by correlation of 0,1, the very same variable is represented in the study of Shipton et al. (2006) by a correlation 0,66. Therefore, this again shows that conducting a meta-analysis seems appropriate.

2.4.2. Numerical Flexibility

Numerical flexibility is defined as the ability of firms to adjust the number of workers, or the level of worked hours, in line with changes in the level of demand for them (Kok & Ligthart, 2014). It is an external part of HRM practices as it uses changes in the external labour market. Numerical flexibility also reacts to the different demands of certain departments through contracting and firing temporary employees (Zhou et al., 2011). Connection between external part of HRM and flexibility comes again from the definition, as it is presented as ‘the ability of firms to adjust external labour force’.

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bring to the company new knowledge, skills or competencies that can serve as a fertile ground for breakthrough ideas (Kok & Ligthart, 2014). Moreover, consulting companies have been found to have a highly positive impact on innovativeness as they bring into the firms knowledge of industry best practices (Martínez‐Sánchez et al., 2011).

Numerical flexibility can be divided into two parts. First, internal numerical flexibility that is composed of practices such as extra hours paid, % flexible working hours, % standby contracts, task rotation, or job autonomy (Beugelsdijk, 2008; Kok & Ligthart, 2014; Martínez‐Sánchez et al., 2011). Second, external numerical flexibility encompasses practices as annual external labour turnover, temporary work, consulting firms, short term hires, or staffing (Chen & Huang, 2009; Kok & Ligthart, 2014; Martínez‐Sánchez et al., 2011; Zhou et al., 2011).

As mentioned above, external flexibility comes with both advantages and disadvantages towards innovativeness. The same external temporal employees that may bring new ideas suffer from the lack of commitment towards the company. That can have a negative impact on incremental innovation as it is often dependent on manufacturing process changes (Kok & Ligthart, 2014). Moreover, standby contracts have been found to have negative effects on both radical and incremental innovation (Beugelsdijk, 2008). While these contracts enhance efficiency and financial performance, the lack of commitment leads to negative impact on innovation (Arulampalam & Booth, 1998).

3. Data Collection and Methodology

The number of studies dealing with flexibility in innovation performance literature has been increasing recently. It has resulted in a very well-explored and mature body and field of literature, nevertheless, the results are not cohesive. In attempt to unify the contradictory results in the field of flexibility in innovation performance, the quantitative literature review, or in other words meta-analysis, is conducted in the study. A meta-analysis is a quantitative integration of empirical studies that addresses the same or similar issues (Cumming, 2006). It allows to pool all the existing literature and correct it for different artifacts and sample sizes (Hunter & Schmidt, 1990). In this section, the whole procedure including the selection of the studies used in the meta-analysis, the distillation process of the meta-factors and the protocol of the meta-analysis itself is explained. The protocol of (Hunter & Schmidt, 1990) is followed in this study.

3.1. Data Collection

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introduction. To research the literature, several internet portals were used, namely: Google Scholar, EBSCOHOST, and Web of Knowledge. The following set of keywords and their combinations were used to identify the potentially important studies: flexibility, resource, NPD, firm level, innovation. First, the keywords were used to search through the whole literature field about innovation performance. Second, the number of publications was limited to those that were considered as relevant and an individual search through each of them was made. The list of relevant publications as well as the publications sources of the articles used in the meta-analysis can be found in Appendix 1. The detected studies were also used for a secondary searching through their references and detecting related articles using the mentioned internet portals.

For purposes of the meta-analysis, the selected studies had to meet the following conditions: 1) a correlation coefficient between dependent and independent variable had to be present, 2) an independent variable had to reflect antecedents regarding key resources used in a new product development, 3) a dependent variable had to measure innovation performance, and 4) the study had to be conducted on a firm level. The data collection resulted in 27 studies that met all the conditions. From these studies 9 were eliminated due to the same sample (Chen et al., 2012; Jimenez-Jimenez & Sanz-Valle, 2005; Martinez-Sanchez et al., 2007; Martínez-Sánchez et al., 2008; Martínez-Sánchez et al., 2009; Vela-Jiménez et al., 2014), or due to methodological discrepancies (Jin, 2001; Serrano & Altuzarra, 2010; Song & Chen, 2014). That resulted in the final number of 18 studies and 40 possible variables. The final list of studies and sources can be found in Appendix 2.

3.2. Distillation Process

After detecting the studies they were sorted out in different groups. This was done initially by the author of the study. The results of this categorisation were introduced to the two respected researchers dr. J.D. van der Bij and dr. W.G. Biemans and after discussion the variables were adjusted to the final form of four meta-factors. When categorising the studies, care was taken to group together only variables with similar characteristics, as that is the first condition for a meta-analysis to be conducted correctly. The meta-factors and the independent variables can be found in Appendix 4. List of dependent variables and their definitions can be found in Appendix 5.

3.3. Data Analysis

meta-11

analysis as these correlations are independent of other variables present in models. The protocol of the meta-analysis continued as follows.

In the first step two sub steps were conducted. First, corrections for sampling error were made. This error is caused by sample size differences. To correct for these differences, the sample correction was weighted by sample size. The following formula was used:

𝑟

̅ =

𝑛 𝑖=1

∑𝑛𝑖=1𝑁𝑖

Where Ni is the sample size of the primary study i.

Second, measurement errors were corrected. To remedy measurement error, Cronbach alphas were used. The correlation coefficient was divided by the product of the square root of the reliability of the meta-factor and the square root of the reliability of performance (Song et al., 2008). When Cronbach alpha was not present in a study, an estimation was made by calculating an average of other Cronbach alphas that were present. The following formula was used to correct for measurement error:

𝜌 = 𝑟̅̅̅0 𝐴̅

=

Where Ā is the compound reliability correction factor; √𝑅𝑥𝑥 is the average of the square roots of reliabilities of independent variables composing a given meta-factor; and √𝑅𝑦𝑦 is the average of the square roots of reliabilities of dependent variables composing a given meta-factor (Song et al., 2008). All the used formulas can be found in the Appendix 3.

In the second step, two checks were made to determine whether a meta-factor was a success factor. First, a homogeneity check was made by using the so called 75% rule. The meta-factor is assumed to be homogeneous, if the real variance is no more than 25% of the total variance (Song et al., 2008). Then, those artifacts that are unknown and uncorrected account for these 25 percent so that the real variance is actually close to zero (Hunter & Schmidt, 1990). Second, for those meta-factors that appeared to be homogeneous it was determined whether the whole confidence interval (based on the real standard deviation) was above zero (Song et al., 2008).

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4. Results

Four meta-factors related to the relationship between resource flexibility and innovation performance were detected for the purposes of this study. Their definitions are presented in the following Table 1.

Table 1. The meta-factors and their definitions

Meta-factors Definition Source

Resource flexibility The range of alternative uses to which a resource can be applied, the costs and difficulty of switching from one use of the resource to another, and the time required to switch one use of the resource to another

(Sanchez, 1995; Suarez et al., 1996)

Slack resources The stock of additional and not used resources available to an organisation during a given planning cycle

(Bromiley, 1991; George, 2005; Nohria & Gulati, 1996) Functional flexibility The ability of firms to vary the amount of

labour they use without resorting to the external labour market and is accomplished primarily by having a labour force that is able to carry out a wide range of tasks

(Martínez‐Sánchez et al., 2011; Michie & Sheehan, 2003; Zhou et al., 2011)

Numerical flexibility The ability of firms to adjust the number of workers, or the level of worked hours, in line with changes in the level of demand for them

(Kok & Ligthart, 2014; Martínez‐Sánchez et al., 2011)

4.1. Results of the Main Meta-Analysis

Table 2 presents the results of the main effects of the meta-analysis. The table presents ρ, an estimate of the real population correlation; N, the aggregate sample size; and K, the number of correlations that are used to build a given meta-factor. The spread of the real correlation variance is 95 percent confidence interval. Also, Varrealasa percentage of the total variance, and the information

13 Table 2. Main results of the meta-analysis

Meta-factors N K ρ 95% Confidence Interval Varreal(%) Moderators

Type of Flexibility

Resource Flexibility 637 3 0,52* (0.50, 0.54) 7,49%

Slack Resources 5627 9 0,23 (-0.06, 0.22) 92,51% Yes

Functional Flexibility 4301 10 0,06 (0.09, 0.36) 88,76% Yes Numerical Flexibility 3252 5 0,08 (-0.05, 0.16) 87,10% Yes Note: Varreal(%) above 25% means that the meta-factor has moderators.

* Significant

4.2. Moderators

14 Table 3. Results of the moderator analysis

Note: Varreal(%) lower than 25% means that a moderator brings a homogeneous result. * Significant

To sum it up, the main effects of the meta-analysis together with the moderators that showed homogeneous results can be seen in the conceptual model in the Figure 2.

Figure 2. The conceptual model of the main effects of the meta-analysis and homogeneous results of the moderator analysis

The dotted lines symbolise heterogeneous relationships and the solid lines homogeneous relationships. Lines in red colour indicate moderator, while plus indicates positive significant relationship.

Meta-factor Moderator N K ρ 95% Confidence

Interval Varreal(%) Organisational Practice Functional flexibility Not-Specified Practices 2586 6 0,23 (0.07, 0.39) 91,91% Team Work 443 2 0,38* / 0,00% Training 3572 6 0,19 (0,08, 0,30) 87,94% Numerical flexibility Internal

Numerical Fl. 1395 3 0,06* / 0,00% External Numerical Fl. 2264 4 0,05 (-0.08, 0.19) 90,58% Innovation Performance

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5. Conclusions and Implications

To the best of author’s knowledge, this is the first systematic quantitative study aiming to integrate results on the relationship between resource flexibility and innovation performance. In order to answer the research question of this study, four meta-factors reflecting the relationship between resource flexibility and innovation performance on a firm level were detected and subsequently examined. These meta-factors are: resource flexibility, slack resources, functional flexibility and numerical flexibility.

5.1. Theoretical Implications

First, the resource flexibility meta-factor strives to explain the impact of flexible use of resources on innovation performance in general, to obtain a better view about the problem before more specific practices are tested. The relationship appeared to be homogeneous and moreover, it shows a positive significant result. Therefore, despite of competencies traps in which a company can fall after using flexible approach towards resources (Alvarez et al., 2013; Kraatz & Zajac, 2001; Levinthal & March, 1993), results of this study are aligned with studies claiming a positive impact (Su et al., 2011; Wei et al., 2014). Nevertheless, future research can focus on these competencies traps. It would be useful to define how it could be found out by a company that it is focusing only on exploiting resources in expenses of exploring new opportunities and how a firm could avoid falling in such a trap.

However, it was found that flexible use of resources positively affect innovation performance in general, yet it does not deny an existence of some practices that affect innovativeness negatively. More specific practices within resource flexibility were tested in order to distinguish between those with positive and negative effects and to separate them. Hence, the slack resources was represented the second tested meta-factor. Nevertheless, the results remained heterogeneous even after using several moderators. This can be explained due to the fact that the impact of this meta-factor is highly inconsistent. Furthermore, this implies the existence of other moderators that either have not been discovered or have not been presented in the existing studies, Future research should focus on searching for these further moderators.

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The fourth tested meta-factor was numerical flexibility. Successful moderator was found in terms of internal numerical flexibility, whereas positive significant result towards innovativeness was generated. This result is an interesting reflection of literature. Whereas Martínez-Sánchez et al. (2008) did not find any significant result while a positive result was expected, Kok & Ligthart (2014) discovered a negative impact of internal numerical flexibility, that was represented by paid overtime, on incremental innovation. According to Kok & Ligthart (2014) even though paid overtime does create flexibility, it may be used only in the operations and hence it jeopardizes innovation. To sum up, it appears that internal numerical flexibility has a positive effect on innovation, but paid overtime, which is a part of the internal numerical flexibility, has negative impact on incremental innovation. Therefore, future research may focus on the impact of paid overtime on radical, incremental, and moderate level of innovativeness as the results are contradictory to make clear what impact paid overtime has on each level of innovativeness. Also, future research can focus on further moderators between external numerical flexibility and innovativeness as the results remained heterogeneous. Other two successful moderators in terms of radical and incremental innovation appeared within numerical flexibility, which implies that HRM practices related to numerical flexibility have positive impact on generating radical or incremental innovations. Future research can be done on the relationship between numerical flexibility and products on moderate level of innovativeness.

Fifth implication is related to both functional and numerical flexibility. As it revealed in the theoretical background, practices within functional and numerical flexibility differs in importance or in impact they have on innovativeness. While some show low importance or negative impact, others demonstrate stronger importance or positive impact. This is also aligned to the findings of this study as most of the results remained heterogeneous, and when a significant result occurred it was mainly on a separate practice. Therefore, future research can study each relationship between an HRM practice and innovation performance separately, in order to provide managers with more precise implications.

To conclude theoretical implications of this study, results are somewhat aligned with the literature mainstream. Especially the results on flexible use of resources and functional flexibility confirm what have been expected by literature. Numerical flexibility showed somewhat surprising results, and results related to slack resources remained heterogeneous.

5.2. Managerial Implications

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The second implication is associated to functional flexibility. As studies discussing functional flexibility detected in this meta-analysis showed predominantly positive significant results, use of internal reorganizing of workplaces is recommended in general. Moreover, one clear implication in terms of team work was found. Therefore managers pursuing an innovative approach should emphasise team-based work (Mirvis, 1997).

Third, interesting implications were found within numerical flexibility. Practices such as temporary employees, short term hires and consulting firms turned to be of importance for both incremental and radical innovations. This is ideal as it helps companies to stay competitive by improving the products or services, and also it increases the chances of introducing a product or service that would disrupt a market and bring disproportionally large profit. Moreover, internal numerical flexibility was found to have a positive effect on innovativeness in general. Practices related to internal numerical flexibility are for example standby contracts or extra hours paid.

To conclude, managers may draw a following lesson from this research in order to enhance the innovativeness of the firm: 1) resource flexibility is the right approach to follow in general, 2) HRM practices are of a crucial importance, 3) team-based work positively influences innovativeness, 4) numerical flexibility enhances chances to introduce both incremental and radical innovations.

5.3. Limitations

As with all research, also this meta-analysis has several limitations. First, this research is limited by the sample size of the meta-study, which influences the generalizability of the results of this study. Despite the extensive research efforts, it is unlikely that this study covers the whole innovation performance literature related to resource flexibility. More time and experience would be necessary to enlarge the research scope of the research. Also, not all the detected studies about resource flexibility within the innovation field on a firm level contained a correlation coefficient, and consequently not all studies could have been used for the meta-analysis.

Secondly, the moderator analysis is constrained by the information scope provided in the detected studies. Although many studies provide detailed contextual background, some moderators could have been missed due to missing information. Researchers are by this encouraged to describe measurements in the future as detailed as possible.

Thirdly, meta-studies using Pearson correlations may be biased by the fact that it is primarily intended to measure the strength of linear relationship between two variables. Nevertheless, when zero correlations appear, a vivid curvilinear relationship can be observed (Song et al., 2008).

18

between resource flexibility and innovation performance on a project level in order to obtain more precise results.

5.4. Future Research Directions

Many directions of future research have been pointed out earlier and will be extended in this section. First, future research can focus on another dimensions of flexibility and conduct a meta-analysis as flexibility of resources is certainly not the only one. It would be of a great interest of innovation performance literature to observe how different types of flexibility either contribute or harm innovativeness.

Second, future research can extend the view on resources used in this study to the types of resources that are needed for companies to be successful. For instance Morris et al. (2001) introduced in his study the PROFIT framework that discusses six types of resources that a firm needs in order to be successful. It would be of a great interest if another meta-study was conducted on what impact has flexible use of each one of them on a firm’s innovativeness.

Acknowledgement

The author would like to take this opportunity to thank Dr. J. D. Hans van der Bij and Dr. W.G. Wim Biemans for useful comments that they provided throughout the whole writing process.

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19

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7. Appendices

Appendix 1. The List of Relevant Publications and Publication Sources of the Articles Used in the Meta-Analysis

Publication source

Number of studies

Academy of Management Journal 1

Entrepreneurship Theory and Practice 0

Human Resource Management Journal 1

IEEE Transaction on Engineering Management 0

Industrial and Corporate Change 1

Industrial and Labor Relations Review 0

International Business Review 1

International Journal of Production Economics 1

Journal of Business Research 1

Journal of Business Venturing 0

Journal of Management Studies 1

Journal of Managerial Issues 1

Journal of Product Innovation Management 4

Journal of Small Business Management 0

Journal of Small Business Management 0

Organization Studies 29(06) 1

R&D Management 0

Research Policy 0

Small Business Economics 1

Technological Forecasting & Social Change 1

Technovation 2

The International Journal of Human Resource Management 2

25 Appendix 2. Studies Included in the Meta-Analysis

Studies Sample (n) Independent Dependent

Akgün et al. (2012) 153 S T

Beugelsdijk (2008) 988 F N Ra I T

Herold et al. (2006) 242 S P

Huang & Chen (2010) 2745 S P

Chen, Huang (2009) 146 F N Te

Jiménez-Jiménez & Sanz-Valle (2008) 173 F T

Kok & Ligthart (2014) 284 F N Ra I T

Lau & Ngo (2004) 332 F T

Lee et al. (2014) 1267 S T P

Martinez-Sanchez et al. (2011) 123 F N

Nohria & Gulati (1996) 264 S Ra T

Shipton et al. (2006) 111 F T Te Su et al. (2011) 204 R S T Troilo et al. (2013) 363 S Ra Wei et al. (2013) 213 F R T Yang et al. (2014) 169 S Ra Yi et al. (2009) 220 F R S T Zhou et al. (2011) 1711 F N T

26 Appendix 3. Formulas Used in the Meta-Analysis A) Correction for sampling error

𝑟̅ =0

∑𝑛𝑖=1𝑁𝑖𝑟0𝑖 ∑𝑛𝑖=1𝑁𝑖

Where: Ni is the sample size of the primary study i,

roi is the correlation coefficient of the primary study i.

B) Correction for measurement error

𝜌 = 𝑟̅0 𝐴̅ = 𝑟̅0 √𝑅𝑥𝑥 ̅̅̅̅̅̅̅ ∗ √𝑅̅̅̅̅̅̅̅𝑦𝑦 ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅

Where: Ā is the compound reliability correction factor,

√𝑅𝑥𝑥 is the average of the square roots of reliabilities of independent variables composing a given meta-factor,

√𝑅𝑦𝑦 is the average of the square roots of reliabilities of dependent variables composing a given meta-factor. C) Calculated variances Total Variance 𝑉𝑎𝑟𝑡𝑜𝑡𝑎𝑙 = ∑𝑛𝑖=1[𝑁_𝑖 (𝑟0𝑖− 𝑟̅̅̅̅)00 2] ∑𝑛𝑖=1𝑁𝑖

Where: 𝑟0𝑖 observed correlation of the primary study i, 𝑟00

̅̅̅̅ weighted average of the observed correlations of the primary studies, so that

𝑟00

̅̅̅̅ =∑ 𝑁𝑖𝑟0𝑖 𝑛 𝑖=1 ∑𝑛𝑖=1𝑁𝑖

Variance due to artefacts

𝑉𝑎𝑟

= 𝑝

𝐴̅

𝑉 = 𝑟

̅

𝑉 = 𝑟

̅

(

+

)

Variance due to sampling error 𝑉𝑎𝑟𝑠.𝑒.=

(1−𝑟̅̅̅̅̅002) 2

𝑁̅−1

Where: N̅ is the average samples size of primary studies Real variance if the population correlation

27

Appendix 4. Independent Variables from Primary Studies and Corresponding Measures

Organisational Dimension

Independent

Variable Measure Article

Resource Flexibility

Flexibility Of Employed Resources

Measured by five items based on Sanchez (1997) and Saini and Johnson (2005): (1) it is easy for our firms to make changes in the product offered; (2) it is easy for our firm to switch focus to different targets/markets; (3) it is easy for our firm to apply resources to a wide range of uses; (4) it is easy for our firm to switch the uses and applications of resources; (5) it is easy for our firm to make fast changes in how resources are used.

Su et al. (2011)

Resource Flexibility (1) The main resources contribute to product development, product, sale and so on, 2) the sharing degree of the main resources used in developing, producing, selling and after-sell services of different products is high (3) The firm often finds new uses for existing main resources through communication between units 4) Uses of the main resource can be easily switched to an alternative one indifferent units of the firm.

Liu et al. (2009)

Resource Flexibility 1) There is a large range of alternative uses to which our major resources can be applied. 2) The difficulty of switching from one use of our major resources to an alternative use is low. 3) The time required to switch to an alternative resource use is short. 4) The costs of switching from one use of our major resources to an alternative use are low. 5) The major resources can be allocated to develop, manufacture, and deliver a diverse line of products.

Wei et al. (2014)

Slack Absorbed Hard-to-redeploy; measured as the sum of the standardized estimations of three items: major repair fund, inventory fund, and accounts payables

Huang & Chen (2010) Discretionary Slack A pool of resources in an organisation that is in excess of the minimum necessary

to produce a given level of organisational output.

Troilo et al. (2014) Financial Slack The current ratio of assets divided by the current liabilities. Lee et al. (2014) Quick Ration Defined as current assets, minus inventories, divided by

current liabilities. The quick ratio indicates the extent to which current assets, not counting inventories, cover the current liabilities.

Herold et al. (2006)

Resource Availability

We measured this scale by three items:(1)financial resources are abundant for our further development;(2)technology progress can ensure our further development; and (3)barriers to enter other industries do not constrain the further development of our firm.

28

Resource Slack Resource redundancy, for instance, allows organisations to develop adaptive capability by (1) helping them to experiment with new structures and more readily implement strategic changes;(2) relaxing controls or restrictions due to the extra resources; and (3) encouraging the pursuit of innovative ideas and fostering a culture of experimentation with less fear of failure

Akgun et al. (2012)

Slack The pool of resources in an organisation that is in excess of the minimum necessary to produce a given level of organisational output. Slack resources include excess inputs such as redundant employees, unused capacity, and unnecessary capital expenditures

Nohria & Gulati (1996)

Slack Resources “The difference between total resources and total necessary payments” Yang et al. (2014) Unabsorbed Easy-to-redeploy; measured as the sum of the standardized estimations of five

items: depreciation fund, reserve fund, loans, sales expenses, and retained earnings

Huang & Chen (2010) Unabsorbed The slack resources with high flexibility; measured by: (1) our firm has more

sufficient net sufficient cash flows than major competitors; (2) the asset liability ratio of our firm is lower than major competitors; (3) generally, our firm is of rich financial assets. Su et al. (2011) Functional Flexibility Coordination Flexibility

Refers to the capabilities to create new resource combinations through an internal coordination process

Wei et al. (2014) Coordination

Flexibility

Increases with a decrease in the cost, difficulty, and/or time required executing these actions; seen as a particularly efficient approach for managing the mismatch between available production resources and time-varying product demands

Liu et al. (2009)

Extent of Team Working

Respondents were first asked to detail 'what percentage of management and administrative staff work in teams' and then 'what percentage of production staff work in teams'. They were specifically asked for 'refer only to stable teams'. These items were combined to form one scale with reasonable reliability

Shipton et al. (2006)

Functional Flexibility

29

HRM Practices A) flexible job design and empowerment; b) team working; c) long-term and skill-oriented staffing; d) extensive-and long-term skill-oriented training; e) broad career opportunities; f) behaviour-based appraisal, and g) organic compensation system, will be positively associated with organisational innovation.

Jiménez-Jiménez & Sanz-Valle (2008)

Participation Consists of three indicators reflecting the degree to which firms allow the employees to make decisions; provide the employees the opportunity to suggest improvements into their work; and value the voices of the employees

Chen & Huang (2009)

Team Development Measured by three items on a five-point Likert scale: 1) Problem-solving sessions are practiced in my organisation. 2) Team building is practiced in my organisation. 3) Quality circles are practiced in my organisation. 4) Quality improvement teams are practiced in my organisation. 5) Leadership training is practiced in my organisation.

Lau & Ngo (2004)

Training The percentage of employees that participated in training (both internal and external trainings).

Zhou et al. (2011) Training Includes four items to indicate the availability of formal training activities,

comprehensive training policies and programs, training for new hires, and training for problem-solving ability

Chen & Huang (2009)

Training And Education

Variable that indicates the percentage of employees who participated in external and/or internal education or training

Kok & Ligthart (2014) Training And

Education

The percentage of employees who participated in external and/or internal education or training

Kok & Ligthart (2014) Training And

Schooling

XXX Beugelsdijk (2008)

Training Focused HR

Measured by three items on a five-point Likert scale: 1) We provide a considerable amount of training. 2) We provide very little management training (reversed). 3) Employee transfers to new functional areas and/or new units are used as a development activity in our firm.

Lau & Ngo (2004)

% Flexible Working Hours

30 Numerical Annual External

Labor Turnover

Maximum of the share of newly hired employees and the share of employees that left the firm during the last year

Zhou et al. (2011)

Consulting Firms XXX Martínez‐Sánchez et al.

(2011) Extra Hours Paid The share of extra hours worked as paid overtime to the total number of hours

worked

Kok & Ligthart (2014) Internal Numerical

Flexibility

XXX Martínez‐Sánchez et al.

(2011)

Job Autonomy XXX Beugelsdijk (2008)

Percentage Of Standby Contracts

XXX Beugelsdijk (2008)

Short Term Hires XXX Martínez‐Sánchez et al.

(2011) Staffing Consists of three items regarding selectivity in hiring, selection for expertise and

skills, and selection for future potential

Chen & Huang (2009)

Task Rotation XXX Beugelsdijk (2008)

Temporary Agency Workers XXX Martínez‐Sánchez et al. (2011) Temporary Job Agency Workers

A nominal variable indicating whether or not the organisation has temporary job agency workers (on-call or day labor basis) in the total workforce

Kok & Ligthart (2014)

Temporary Work The percentage of employees having fixed-term contracts hired directly by the firm.