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Entrepreneurial orientation, access to financial resources, and product performance in the Greek commercial TV industry

Leonidas A. Zampetakisa; Melina Vekinia; Vassilis Moustakisa a Department of Production, Engineering and Management, Technical University of Crete, Chania Crete, Greece First published on: 11 October 2010

To cite this Article Zampetakis, Leonidas A. , Vekini, Melina and Moustakis, Vassilis(2011) 'Entrepreneurial orientation,

access to financial resources, and product performance in the Greek commercial TV industry', The Service Industries Journal, 31: 6, 897 — 910, First published on: 11 October 2010 (iFirst) To link to this Article: DOI: 10.1080/02642060902960800 URL: http://dx.doi.org/10.1080/02642060902960800

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The Service Industries Journal Vol. 31, No. 6, May 2011, 897 –910

Entrepreneurial orientation, access to financial resources, and product performance in the Greek commercial TV industry Leonidas A. Zampetakis , Melina Vekini and Vassilis Moustakis Department of Production, Engineering and Management, Technical University of Crete, Chania Crete, Greece

Downloaded By: [Zampetakis, Leonidas A.] At: 07:51 4 March 2011

(Received 26 January 2009; final version received 2 April 2009) In this research article, the authors draw on the theoretical insights of the strategic entrepreneurship literature and examine the relationships among entrepreneurial orientation, access to financial resources, and broadcasted product performance using survey data from Greek television enterprises. Data were based on companies’ chief executive officers. Results of Bayesian path analysis indicate that access to financial resources fully mediates the effect of entrepreneurial orientation on product performance. Recommendations for further research are discussed. Keywords: mass media; TV; entrepreneurial orientation; performance

Introduction For more than 40 years, most European countries had two or three television (TV) channels, provided by a public-owned broadcaster and paid for mainly through taxes. In the early 1980s, a wave of deregulation broke over the old order of broadcasting in virtually every European country. The European TV industry has undergone radical changes due to the opening-up of the media caused by technology, deregulation, and internationalization (Albarran & Chan-Olmsted, 1998). Television viewers in many European counties now have on average more than thirty commercial free TV channels to choose from, and advances in digital cable and digital direct broadcast satellite technology offer the possibility of giving viewers more than 200 channels to choose from in the not-so-distant future (Liu, Putler, & Weinberg, 2004). The commercial free TV channels are now found to compete in a highly competitive and dynamic environment aiming at two distinct, but closely related markets, one for viewers and the other for advertisers (Liu et al., 2004). Commercial channels focus on the delivery of TV programmes that have a great number of viewers; revenues are received by selling time to advertisers. Customers of the commercial TV channels (both viewers and advertisers) put a premium on the delivery of unique TV programmes. However, the ability to predict the size and type of viewers for a new TV programme is extremely low (Hang & van Weezel, 2007). Furthermore, watchable (i.e. high-quality and unique) TV programmes are expensive. For instance, Scott (2004, p. 196), reported that the average cost of producing a 1 h episode of a notably successful US prime-time show was about $2 million. Dropping TV programme cost value tends to greatly reduce the appeal of a programme to viewers (Liu et al., 2004); viewers are sensitive to TV

Corresponding author. Email: [email protected]

ISSN 0264-2069 print/ISSN 1743-9507 online # 2011 Taylor & Francis DOI: 10.1080/02642060902960800 http://www.informaworld.com


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programme’ production value as a distinct evaluative dimension (Shamir, 2007). Therefore, access to financial resources is of particular importance to commercial TV channels. According to Miller (1983), when customers focus on unique services, firms’ entrepreneurial-type strategies are likely to be more successful. Entrepreneurial orientation (EO) refers to methods, practices, and decision making styles managers use to act entrepreneurially and can be thought of as a type of strategic orientation insofar as it captures how a firm intends to compete (Lumpkin & Dess, 1996). Extant literature suggests that the strength of a firm’s EO can have a strong, positive effect on firm’s performance (Avlonitis & Salavou, 2007; Wiklund & Shepherd, 2005; Zahra & Covin, 1995). Though, few research studies have empirically explored the influence of EO on product performance in general, (Salavou & Lioukas, 2003) and TV broadcasted programmes, in particular. Television products differ significantly from products and services of other industries in a number of respects (Picard, 2005). However, the characteristics of the TV products are very much aligned to the dimensions of the EO i.e. innovativeness, risk-taking and proactiveness (Hang & van Weezel, 2007). EO could be an important measure of the way a TV channel is organized – one that enhances the product performance (that is TV programmes delivered to viewers) through acquiring external financial resources. This is in line with the notion that entrepreneurship theory has an important role to play in the exploration of alternative approaches in service industries (Dobon & Soriano, 2008). For instance, entrepreneurship is closely related to innovation and service firms that do not innovate will find it hard to maintain high levels of competitiveness; entrepreneurial-type strategies are likely to be more successful towards acquiring competitive advantage. To address the aforementioned gaps the present study contributes to service management research by constructing a conceptual model to describe the relationship among EO, access to financial resources and broadcasted product performance, and to develop knowledge about the path-dependence of the influence of TV channels’ EO through access to financial resources. By examining the relationship among these factors in Greek TV channels, we will explain the effects of EO and access to financial capital to broadcasted product performance. Specifically, the study contributes to service management in three respects. First, unlike the numerous studies, which place a major emphasis on explaining the complexity in the EO-firm’s performance link (Avlonitis & Salavou, 2007; Wiklund & Shepherd, 2005; Zahra & Covin, 1995), this study focuses mainly on the relationship between EO and TV product performance. Such a focus follows the suggestion of Hang and van Weezel (2007) to empirically research the EO construct in the media industries and acknowledges the significant differences between media products and products from other industries. Second, while access to financial resources has been investigated as a moderator in the EO-firm’s performance link (Wiklund & Shepherd, 2005), it has not been previously investigated as a potential mediator. Finally, while assessment of TV programme performance is a challenging endeavour we propose a new way to operationalize TV product performance by means of item response theory (IRT) (Singh, 2004). The remainder of the paper is structured as follows: First, by reviewing the previous literature on EO and product performance, we present the conceptual model in Figure 1 and set out the hypotheses of the study. Then there follows a brief description of the Greek TV market and the methodology used, in the empirical research. The paper ends with a discussion of the implications, the limitations, and future research.


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Figure 1. Representation of the hypothesized theoretical model. Numbers represent common metric standardized parameter estimates.


Research background EO and product performance Miller (1983) appears to offer the earliest operationalization of the entrepreneurship orientation concept. He defines an entrepreneurship-oriented firm as one that engages in product marketing innovation, undertakes somewhat risky ventures, and is the first to come up with proactive innovations. EO refers to a firm’s strategic orientation, capturing specific entrepreneurial aspects of proactiveness, risk-taking, and innovativeness (Lumpkin & Dess, 1996). Proactiveness refers to a posture of anticipating and acting on future wants and needs in the marketplace; with such a forward-looking perspective, proactive firms capitalize on emerging opportunities. Risk-taking is associated with a willingness to commit large amounts of resources to projects where the cost of failure may be high (Lumpkin & Dess, 1996). Innovativeness reflects a tendency to support new ideas, experimentation, and creative processes, thereby departing from established practices and technologies (Lumpkin & Dess, 1996). The literature on the relation between the EO of a firm and its performance is quite extensive; the relationship has been thoroughly investigated, from both a conceptual viewpoint (Covin, & Slevin, 1991; Lumpkin & Dess, 1996) and an empirical point of view (Keh, Nguyen, & Ng, 2007; Li, Liu, & Zhao, 2006; Lumpkin & Dess, 2001; Wiklund & Shepherd, 2003, 2005). It is likely that EO has positive performance implications for the firm. By introducing new products and services, firms can establish industry standards. Firms with EO have the capabilities to discover and exploit new market opportunities and they can respond to challenges raised by the competitive and uncertain environment. All this can positively influence performance of the firm. Surprisingly, however, few research studies focus on combining the key concepts of EO and product performance (Salavou & Lioukas, 2003). For the TV industry market, there exists a very limited degree of product price competition; rather broadcasters compete primarily on product attributes and performance (Liu et al., 2004). Television products differ considerably from products and services of other industries in a number of respects (Picard, 2005). For example, the ability to predict the size and type of viewers for a TV programme is extremely low (Hang & van Weezel, 2007). One cannot produce test units for most TV products to determine market demand before full production. Consequently, product failure rates are high. Furthermore, there is a large oversupply of content from which consumers choose (Picard, 2005). Since it is impossible to consume all products, consumers have significant power in the TV industry in determining success and failure of TV products. Finally, much of the economic value of TV products results from a small number of products/services. Although failure rates are high, successes are financially well-rewarded (Picard, 2005).


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Empirical evidence suggests that, in part, both high and low levels of product innovativeness are characterized by higher product performance (Avlonitis & Salavou, 2007). According to Avlonitis and Salavou (2007), the ingredient of uniqueness acts as an important contributor to product performance. In their study, Salavou and Lioukas (2003) investigated the strategic drivers of radical product innovation adoptions and found a positive effect of EO on product innovativeness. In a similar vein, Zhou, Yim and Tse (2005) found that EO positively affects breakthrough innovations; the authors argued that substantial product innovations require a greater amount of risk-taking and proactiveness from firms. Finally, Li et al. (2006) empirically demonstrated that EO is positively related to the degree of improvement of new product development. According to our previous discussion, evidence suggests that EO promotes the introduction and implementation of innovative products within the firm; however, there is no explicit empirical evidence concerning the direct influence of EO on product performance. Therefore, we put forward the following hypothesis regarding the role of EO on product performance. H1: EO will be positively related to broadcasted product performance.

EO and access to financial resources Access to financial resources appears to be of particular importance to commercial TV channels. Having financial strength TV channels can support information gathering and dissemination activities and produce quality entertainment (Liu et al., 2004; Shamir, 2007). Financial strength permits TV channels to maintain their independence and engage in activities designed to hold government and other institutions in society accountable (Picard, 2006). Finally, financial capital is the most generic type of resource and can relatively easily be converted into other types of resources (Wiklund & Shepherd, 2005). A study published by Wiklund and Shepherd (2005) is perhaps the first that specifically introduces access to financial resources and relates it to EO. The authors argue that access to financial resources acts as a moderating variable relevant to the relation between EO and firm performance. However, as discussed above, EO refers to a firm’s strategic orientation, capturing specific entrepreneurial aspects of decision-making styles, methods, and practices; as such EO could be an important measure of the way a firm is organized (Lumpkin & Dess, 1996). Accordingly, commercial TV channels high on EO have more aptitude for risk-taking, innovativeness, and proactiveness; as such they are oriented towards action, they pursue active implementation of new ideas, or processes not merely of their generation but also actively seek to anticipate opportunities to instigate changes to current strategies and tactics, and detect future trends in the market (Covin & Slevin, 1991; Lumpkin & Dess, 1996). Although financial capital is an important resource, it is not so much the ownership of the financial resources that is important but the access to it (Stevenson & Jarillo, 1990). Thus, an effective EO may be a good predictor of firm access to financial resources. These arguments lead to the following hypothesis. H2: EO will be positively related to access to financial resources.

Access to financial resources and product performance Several studies show that access to financial capital influences the performance and growth of the small firms (Moreno & Casillas, 2008; Wiklund & Shepherd, 2005). Financial


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capital provides a buffer against unforeseen difficulties that may arise and also provides organizational financial slack, facilitating the necessary response to changing conditions. As discussed above, TV products differ from products and services of other industries (Picard, 2005). One important aspect of the TV industry is that providing watchable TV programmes is extremely expensive and on the rise. We mentioned the study of Scott (2004) in the Introduction; more recent data from the American Association of Advertising Agencies’ (AAAA) television production cost survey, suggests for instance, that the average production cost of TV commercials ranges from $230.000 to $350.000 per half minute (AAAA, 2008). Furthermore, the production value of TV programmes is perceived by viewers as a salient evaluative dimension that contributes significantly to viewers’ interest/enjoyment and quality assessments of most programme (Shamir, 2007). Therefore, we put forward the following hypothesis regarding the relationship between access to financial resources and TV product performance: Downloaded By: [Zampetakis, Leonidas A.] At: 07:51 4 March 2011

H3: Access to financial resources will be positively related to product performance.

The mediating effect of access to financial resources Some researchers have suggested that the relationship between EO and firm performance may be more complex than a simple main effect (Lumpkin & Dess, 1996; Wiklund & Shepherd, 2005). As noted previously, H2 states that EO will be positively related to access to financial resources and H3 states that access to financial resources will be positively related to TV broadcasted product performance. These two hypotheses link EO with access to financial resources, and access to financial resources with product performance. This means that the relationship between EO and product performance is hypothesized to be indirect. Therefore, access to financial resources plays the role of intermediate variable to mediate the relationships between independent variables of EO and dependent variable of product performance. Accordingly, the following hypothesis is developed. H4: Access to financial resources will mediate the relationship between EO and product performance.

Research methods The Greek TV industry Television broadcasting in Greece began officially in 1966. The Greek broadcasting system underwent spectacular changes in the late 1980s. The deregulation of Greek TV has to be seen as a logical consequence of the general European TV liberalization of the 1980s. From a broadcasting environment with two public TV channels and four public radio stations, to an overcrowded environment comprising 160 private TV channels and 1000 private radio stations broadcasting in the early 1990s. In addition, Greece has undergone a broadcasting commercialization, adopting a market-led approach, resulting in more channels, more advertising, and more programme imports (Papathanassopoulos, 1997). The contemporary audiovisual landscape in Greece is characterized by the dominance of the private sector. Unlike many European states, Greek public TV was not able to keep up with the evolution of the sector. Seven commercial private channels are free to receive (Mega Channel, Antenna TV, Alpha, Alter, Star, Sky, and Macedonia TV) broadcast via

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the national terrestrial network. The range of public channels consists of those operated by the broadcaster ERT (NET, ET1, ET2, and ERTSat). There is only one digital pay TV operator (Nova) and a small number of content specific channels (such as religious or music); finally, a particular large number of regional/local private channels (about 140) complete the list of Greek TV stations. The Greek National Council for Radio and Television (NCRTV – www.ers.gr), an independent administrative authority founded in 1989, supervises and regulates the radio/TV market. Participants and procedures We employed a questionnaire survey approach to collect data. The population in the study was TV channels listed in NCRTV records. We excluded channels with specialized content (such as music or religious channels), pay TV channels and public TV due to their different production function. Therefore our analyses were restricted to commercial free TV channels. Such TV channels focus on programming and sales. One hundred and nine commercial free TV channels were identified. The responsible chief executive officers (CEOs) were invited to participate in the study through personal telephone contact by the authors. CEOs were given the following explanation for the purposes of the study: ‘This is an effort to combine research into the strategic orientation of Greek private TV stations. Your participation is not obligatory; and your answers are confidential. The results will be used to better understand the entrepreneurial posture of commercial TV channels’. A pilot study was conducted of the survey instrument used in this research with three CEOs in order to ensure face and content validity. After reviewing comments from these individuals, we made minor modifications to the instrument. CEOs who agreed to participate in the survey were given the survey instrument to complete; upon completion of the survey, CEOs sent their responses by email or fax to the first author. A total of 109 questionnaires were distributed and 48 CEOs completed usable surveys, representing a response rate of 43%. In an effort to assess the possible impact of response bias, we contacted 10 randomly selected non-respondents directly on the telephone and requested input. Seven of them complied with the request. Comparison of respondents and non-respondents indicated that they did not differ with respect to any of the variables of interest in the present study. Data collection took place during May 2008. The questionnaire contained 14 items representing the theoretical constructs (EO, access to financial resources, product performance, and total performance (TP)) along with demographic data of the respondent (age and gender) and of the channel (years of operation, total number of staff, number of technical, administrative staff, number of journalists, and whether the channel broadcasts in national, regional or local context). The respondents of the sample included 32 males and 16 females CEOs aged between 29– 69 years (M ¼ 45.30 years, SD ¼ 12.39). Average years of channel operation was 14.14 years (SD ¼ 4.89). The average number of staff was 65.18 (SD ¼ 150.15), the average numbers of technical, administrative staff and journalists were 24.15 (SD ¼ 56.97), 11.85 (SD ¼ 27.77) and 15.30 (SD ¼ 33.75), respectively. Finally the sample consisted of 19 local, 25 regional, and 4 national commercial free channels. Measurement of theoretical constructs All the main constructs included in the analysis were assessed with self-report measures based on multi-item scales. Responses to all items were made on five-point Likert-type


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scales (1 ¼ strongly disagree, 5 ¼ strongly agree). Native speakers translated all the items into the Greek language. A back-translation into English by other bilingual individuals revealed that the translation had worked quite well and that the wording had similar connotations. The specific measures used in the analysis, along with the items of the relevant constructs, are outlined.


Entrepreneurial orientation We used six items, relevant to the TV industry, of the nine-item EO scale developed by Covin and Slevin (1989). This scale is the most widely utilized measure of EO in entrepreneurship and strategic management literature (Covin, Green, & Slevin, 2006). The following are the items for innovativeness, proactiveness, and risk-taking dimensions respectively: Inno1 – ‘In general, the top managers of my channel favour a strong emphasis on the marketing of tried and true products or services instead of on R&D, technological leadership, and innovations’, Inno2 – ‘My channel has marketed no new lines of products or services in the past three years’; Inno3 – ‘Changes in product or service lines in my channel have been mostly a minor nature in the past 3 years’; Proac1 – ‘In dealing with its competitors, my channel typically responds to actions which competitors initiate’; Proac2 – ‘In dealing with its competitors, my channel is very seldom the first to introduce new products or services, administrative techniques, operating technologies, etc’; Risk1 – ‘In general, the top managers of my channel have a strong proclivity for low risk projects (with normal and certain rates of return)’. All items were reverse coded and the mean ratings of these six items were used as the EO measure so that the higher the score, the more entrepreneurial the strategic posture. Cronbach’s reliability coefficient (0.83) for all six items was deemed acceptable. Access to financial resources Financial resources refer to capital availability from banks, suppliers, and customers. We used two items from Shane and Kolvereid (1995): FinRes1 – ‘Bank loans are easily available for us’; FinRes2 – ‘Capital from suppliers or customers is easily available for us’. In the present study, Cronbach’s a-value of 0.77 for the two items was considered acceptable. Product performance Broadcasted product performance measurement was based on the relative success of the TV channel’s products in terms of sales (to advertisers) and at achieving market share (in terms of audience share). Since, much of the economic value of TV products results from a small number of products/services (Picard, 2005), it was sensible to capture the relative success of the TV channels’ products at securing sales and market share compared to competitors, as these dimensions capture the two basic areas in which commercial TV channels must make an impact if they are to survive and prosper. We adopted two items from Hughes and Morgan (2007): Perf1 – ‘Relative to competing products, those of our channel have been more successful in terms of sales to advertisers’; Perf2 – ‘Relative to competing products, those of our channel have been more successful in terms of achieving and establishing market share (in terms of audience share)’. Cronbach’s a-value for the two items was 0.91. We ascribe to the view that product performance is multidimensional (i.e. Huang, Soutarb, & Brown, 2004). Our reliance on self-report data from single informants


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introduces the potential of common method variance. Therefore, we correlated the product performance index from the mail survey with another product performance measure (ProdPerf_IRT), collected during a five-minute telephone interview, after data collection. CEOs who participated in the survey were asked to indicate whether five distinct product categories [(1) news; (2) documentaries; (3) game shows; (4) political talk shows; and (5) athletic shows) had an impact both on the market share and sales of the channel. Responses to items were made on a binary scale (1 ¼ Yes, 0 ¼ No). Cronbach’s a-value was 0.72 for the five items. The measurement of the ProdPerf_IRT variable was examined in the context of IRT using the mean and variance adjusted weighted least squares estimator and the Mplus (version 5.2) software (Mutheń and Mutheń, 1998 – 2008, www. statmodel.com). IRT refers to a diverse family of probalistic models for expressing the association between an individual’s response to an item and the underlying latent variable being measured by the instrument. These models are able to deal with responses to items that are scored in either of the dichotomous items (i.e. only two possible scored responses exist, such as true – false, correct – incorrect, endorsed – not endorsed, etc.) (Singh, 2004). In the present study, the one parameter logistic model (1-PLM) or Rasch model was used. Generally, 1-PLM models estimate fewest parameters, and thus smaller sample sizes are adequate for stable parameter estimates – perhaps as few as 50 (Linacre, 1994). The correlation between the two indices, performance measured by the mail survey and a different measure of performance from the telephone interview (ProdPerf_IRT), was 0.529 (p , 0.001), suggesting that the correlation between the two underlying theoretical constructs, corrected for measurement error, is 0.653 (Bedeian, Day, & Kelloway, 1997); this indicates that common method bias is not a major problem. TV channels’ TP was measured through the mail survey with four items that asked CEOs to evaluate their channels’ performances relative to their principal competitors for the past 3 years on (1) sales growth rate; (2) market share; (3) overall performance; and (4) cash flow (Tang, Tang, Marino, Zhang, & Li, 2008). All items were anchored on a five-point Likert scale ranging from ‘very low’ (1) to ‘very high’ (5). The average of these four items was used as the TV channels’ TP (Cronbach’s a-value was 0.83). The correlation between broadcasted product performance and TP was 0.485 (p , 0.001). Control variables Two controls were entered when we tested the hypothesized relationships. The first control was TV channel size, which was measured by the total number of employees (Tang et al., 2008). Respondents were asked to indicate the total number of employees. The second control was TV channel years of operation. Previous research suggests that both firm size and age are attributes that significantly impacts firms’ outcomes (e.g. Hughes & Morgan, 2007). Analytical strategy Prior to testing the proposed model and the associated hypotheses, confirmatory factor analyses (CFAs) were conducted to demonstrate the construct validity of variables. We used Analysis of Moment Structures (AMOS, version 7.0) software (Arbuckle, 2006) taking a Bayesian approach to estimate CFA measurement models along with Markov Chain Monte Carlo (MCMC) for model fit. Bayesian modelling does not rely on asymptotic theory, making it particularly useful when the sample size is small and thus classical estimation methods (such as maximum likelihood) are not robust. Gelman, Carlin, Stern,



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& Rubin, (2004) provide a comprehensive treatment of Bayesian methodology. More introductory textbooks about this topic include Congdon (2003) and Berry (1996). In Bayesian estimation, a point estimate of a parameter is the mean of the posterior distribution. Furthermore, unlike the conventional confidence interval, the Bayesian credible interval is interpreted as a probability statement about the parameter itself; Prob(a u b) ¼ 0.95) literally means that we are 95% sure that the true value of u lies between a and b. Inference in the Bayesian framework involves summarizing the joint posterior of all unknown parameters. MCMC simulation-based methods in general and Metropolis – Hastings algorithm in particular were used to obtain random draws from the posterior density. The convergence of the MCMC simulation was verified using a variety of diagnostics such as time series (or trace plots) plots to graphically assess the quality of the mixing of the chain and autocorrelation plots (Gelman et al., 2004, p. 296). In the present study, we consider a common situation that accurate prior information is not available for model parameters. Therefore, we chose flat prior distributions that assign zero probability to improper solutions for the variances (i.e. negative variances) of the model, that is, the variance parameters were constrained to have strictly positive values. For the rest of the model parameters we chose non-informative normal priors. Under this design, for the variance parameters, the conjugated posterior distribution is an inverse gamma distribution and for the rest of the model parameters a normal distribution (Gelman et al., 2004). The adequacy of a Bayesian model can be assessed using posterior predictive model checking (Gelman, Meng, & Stern, 1996). Posterior predictive p-value (PP p-value) is the probability that the replicated data (i.e. the PP distributions) could be more extreme than the observed data. As suggested in Gelman et al. (2004, pp. 175 – 176), PP p-value should be near 0.5 for a correct model, with values towards the extremes of 0 or 1 indicating that a model is not plausible. This statistic has been applied to Bayesian analyses (e.g. Lee, 2007, pp. 128– 129; Scheines, Hoijtink, & Boomsma, 1999) and has produced dependable results for assessing goodness-of-fit of the posited model. Furthermore, the deviance information criterion (DIC) (Spiegelhalter, Best, Carlin, & van der Linde, 2002) was used as a method of choosing among competing Bayesian models. The model with the smallest DIC is selected to be the best model. Finally, the small sample size used in the present study raises concerns about the statistical power of the model’s parameter estimates (McQuity, 2004). Statistical power is defined as one minus the probability of Type II error. The cut-off most frequently used to define acceptable power is 0.80, that is 80% likelihood of rejecting the null hypothesis. We performed a Monte Carlo simulation study in order to determine power (Mutheń & Mutheń, 2002). The Mplus (version 5.2) software (Mutheń & Mutheń, 1998 – 2008) was used due to its extensive Monte Carlo simulation facilities.

Results Preliminary analyses – assessment of measurement models The first measurement model tested the EO variable postulating that each item would load significantly onto the common factor. Model fitting was accomplished using the Metropolis – Hastings algorithm. The initial 10,000 MCMC scans were discarded as burn-in. The posterior summaries were based on a posterior sample of size 20,000 scans. The MCMC chain mixed well and standard diagnostics suggest that the sample is approximated to the stationary distribution. Based on the standards for Bayesian modelling, the model

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demonstrated an acceptable level of fit (PP p-value ¼ 0.47). All indicators’ estimated pattern coefficient on the underlying construct factor was statistically significant. The second measurement model tested the hypothesized three-factor measurement model (that is EO, access to financial resources, and product performance), with a onefactor model (Harman’s one-factor test) in which all of the items were set to load on a single underlying factor (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). The basic assumption of Harman’s one-factor test is that if a substantial amount of common method variance exists in the data, either a single factor will emerge or one general factor will account for the majority of the covariance among the variables. The hypothesized three factor-measurement model fit the data better (PP p-value ¼ 0.19; DIC ¼ 115.35) than the alternative one-factor model, both in terms of PP p-value and when directly contrasted with a change in DIC (PP p-value ¼ 0.0; DIC ¼ 189.66).

Hypothesis testing Table 1 presents means, standard deviations, and correlations of the variables. It is notable that EO was significantly related to TP (r ¼ 0.573, p , 0.001) providing support for the positive relationship between EO and business performance found in previous research (e.g. Avlonitis & Salavou, 2007; Wiklund & Shepherd, 2005; Zahra & Covin, 1995). Furthermore, EO was positively related to broadcasted product performance (r ¼ 0.324, p , 0.05) and access to financial resources (r ¼ 0.371, p , 0.01). Thus, initial support for hypotheses H1 and H2. Product performance was positively related to access to financial resources (r ¼ 0.533, p , 0.001) and years of TV channels operation (r ¼ 0.298, p , 0.05); thus, initial support for H3. Our results also suggest that the total number of employees was positively related to TP (r ¼ 0.401, p , 0.01) but not to product performance (r ¼ 0.075, non-significant). We used Bayesian path analysis to test the proposed hypotheses and estimate the integrated model (Figure 1). Model fitting was accomplished using the Metropolis–Hastings algorithm. The initial 10,000 MCMC scans were discarded as burn-in. The posterior summaries were based on a posterior sample of size 30,000 scans. H1. The standardized direct effect of EO on product performance was significant (b ¼ 0.35, 95% credible interval: 0.19– 0.66). The proportion of variance in product performance that was explained by EO was 10.5%. Thus, results provide support for H1.

Table 1. Descriptive statistics and intercorrelations for total sample. M

SD

EO 3.50 0.84 TP 3.72 0.70 Product performance 3.54 1.10 0.0 0.98 ProdPerf_IRT† Access to resources 3.12 1.16 Years of operation 14.14 4.89 Employees 65.18 150.15

1

2

– 0.573 0.324 0.215 0.371 0.288 0.179

– 0.485 0.284 0.360 0.373 0.401

3

4

5

6

7

– 0.529 – 0.533 0.396 – 0.298 0.312 0.09 – 0.075 20.206 20.183 0.228 –

Note: N ¼ 48. † ProdPerf_IRT has a mean of zero and standard deviation of one. p , 0.05. p , 0.01. p , 0.001.



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H2. The standardized direct effect of EO on access to financial resources was significant (b ¼ 0.41, 95% credible interval: 0.09 – 0.57). The proportion of variance in access to financial resources that was explained by EO was 14%. Thus, results provide support for H2. H3. The standardized direct effect of access to financial resources on product performance was significant (b ¼ 0.52, 95% credible interval: 0.28 – 0.70). The proportion of variance in access to financial resources that was explained by EO was 28%. Thus, results provide support for H3. H4. According to Baron and Kenny (1986), the necessary conditions that must be met in order to claim that mediation is occurring are as follows: the predictor variable is significantly related to the mediator; the mediator variable is significantly related to the outcome variable; and finally the relationship between the predictor and outcome diminishes when the mediator is in the model. In our study, EO significantly correlates to mediator variable (access to financial resources) and outcome variable (product performance). Furthermore, the mediator variable significantly correlates with outcome variable. However, we made no prediction whether partial of full mediation exists. The partial mediation model provided an adequate fit to the data (PP p-value ¼ 0.44, DIC ¼ 19.09); however, the standardized direct effect of EO on product performance was not statistically significant (b ¼ 0.14, 95% credible interval: 20.12 to 0.39); the alternative full mediation model provided a better fit in terms of DIC (PP p-value ¼ 0.45, DIC ¼ 17.66). Thus, our data provide evidence that access to financial resources fully mediates the relationship between EO and product performance. The standardized direct effect of EO on access to financial resources was significant (b ¼ 0.37, 95% credible interval: 0.08 –0.59). The standardized direct effect of access to financial resources on product performance was significant (b ¼ 0.53, 95% credible interval: 0.30 – 0.72). Finally the standardized total effect of EO on product performance was significant (b ¼ 0.26, 95% credible interval: 0.04– 0.36). The standardized effects of the control variables on product performance were not statistically significant (TV channel size, b ¼ 0.10, 95% credible interval: 20.13 – 0.34; years of operation, b ¼ 0.21, 95% credible interval: 20.05 – 0.44). Results of power analysis suggests that an N ¼ 48 has sufficient power to reject a false null hypothesis with regard to the path coefficients (Statistical power was 95% for the access to financial resources – product performance path and 73% for the EO – access to financial resources path).

Discussion and conclusions This study develops a conceptual model to examine the mediating role of access to financial resources in the relationship between EO and broadcasted product performance (Figure 1). The results show that EO can positively enhance TV channels’ access to financial resources (the standardized direct effect of EO on access to financial resources was significant (b ¼ 0.37)); access to financial resources, in turn has a positive effect on TV channels’ broadcasted product performance (the standardized direct effect of access to financial resources on product performance was significant (b ¼ 0.53)). Including access to financial resources as mediator, the directly positive relationship between EO and product performance becomes insignificant. Thus, our proposed model specifically implies that EO indirectly influences firm performance by access to external financial resources.


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This study contains two important novelties with regard to previous research projects. First, while the importance of EO in firm performance has been recognized (Avlonitis & Salavou, 2007; Wiklund & Shepherd, 2005; Zahra & Covin, 1995), very few empirical studies have focused on the link between EO and product performance (Salavou & Lioukas, 2003). The present study reveals that EO is critical to TV channels and has positive impact on the product performance they broadcast. The inclusion of access to financial resources as a mediating variable may help to enhance our understanding of how EO affects product performance. Our findings make a contribution to service management literature by clarifying the role that access to financial resources plays. Second, the emergent model provides empirical support of the entrepreneurship concept in communications in general and the TV industry in particular. Although there exist a few published studies on entrepreneurship in communication (e.g. Hang & van Weezel, 2007) this is the first study that empirically examines the EO construct in the TV industry. The findings demonstrate the mediating effect of access to financial resources when TV channels want to execute EO to achieve improved product performance. Taking a broader perspective, it could be argued that EO contributes to the creation of added value for the firm supporting the view that entrepreneurship is more than the mere creation of business (e.g. Zampetakis & Moustakis, 2007). Nonetheless, the research does have some limitations. First, we have adopted an aggregate (or higher-order) approach to the assessment of EO. That is, we created a composite EO scale for our analysis. The problems with this approach are that it neglects the individual influence of each dimension (e.g. risk-taking, innovativeness, and proactiveness) and assumes a universal and uniform influence by each dimension (Hughes & Morgan, 2007). Further research is needed to understand how different EO dimensions might influence product performance. Second, our cross-sectional design prevents us from studying causal relationships among our variables. A longitudinal investigation would provide further insights. Third, our study concentrated on TV channels in Greece. Caution should therefore be exercised in generalizing these findings to non-comparable populations. Consequently, future studies might want to consider the implications of our work for different populations. These limitations represent, in any case, opportunities to advance in our efforts to better understand the relation between EO and product performance. Acknowledgements This research has been partially supported by a post-doctoral research scholarship granted to the first author by the Greek State Scholarship Foundation (IKY-801/2009). References AAAA. (2008). American Association of Advertising Agencies’ (AAAA) television production cost survey. Retrieved March 2, 2009, from http://www.vidopp.com/tv-ad-production-coststudy-released/ Albarran, A., & Chan-Olmsted, S. (1998). Global media economics. Ames, IA: Iowa State University Press. Arbuckle, J.L. (2006). AMOS 7.0 user guide. Chicago: SmallWaters Corporation. Avlonitis, G.J., & Salavou, H.E. (2007). Entrepreneurial orientation of SMEs, product innovativeness, and performance. Journal of Business Research, 60(5), 566–575. Baron, R.M., & Kenny, D.A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173–1182.



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