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Management Decision How small-medium enterprises leverage intangibles during recessions. Evidence from the Italian clothing industry Marco Cucculelli Cristina Bettinelli Angelo Renoldi

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How small-medium enterprises leverage intangibles during recessions. Evidence from the Italian clothing industry

How SMEs leverage intangibles 1491

Marco Cucculelli Department of Economics and Social Sciences, Universita` Politecnica delle Marche, Ancona, Italy, and

Cristina Bettinelli and Angelo Renoldi University of Bergamo, Bergamo, Italy Abstract Purpose – The purpose of this paper is to focus on how investments in research and development (R&D) and advertising affect the performance of small- and medium-sized enterprises (SMEs) during recessions. Design/methodology/approach – Contingency theory is applied to a data set of 376 Italian clothing SMEs during the period 2000-2010 to test whether investment in R&D and advertising impacts financial performance differently when contingent factors (such as market share, financial leverage and business model change) are taken into account. Findings – Empirical results confirm that market share and leverage moderate the effects of investments in R&D and advertising (i.e. intangibles) on performance, and also that changes in business models are an important contingent factor that explains performance. Specifically, the paper ascertains that a novelty-centered business model, together with investments in intangibles, positively affects performance during recessions. Originality/value – This study offers an input to the debate on how SMEs develop and sustain their competitive advantage during the recession. It contributes to existent theory by showing whether and how contingencies, such as a firm’s market share and leverage, moderate the relationship between performance and investments in R&D and advertising in SMEs. Second, it addresses the call for additional data “about the strategic effects of business models and how they influence the positioning of firms in their competitive environment” (Amit and Zott, 2008, p. 20) by introducing business model change/innovation as a new contingency factor and by empirically testing its effects on “objective measures of firm performance” (Bock et al., 2012, p. 301). Keywords Intangibles, Contingency theory, Small- to medium-sized enterprises, Recession, Business model innovation/change, R&D and advertising Paper type Research paper

Introduction Recessions are cyclical events in the world economy that profoundly influence the competitive landscape. Recessionary reductions in the demand for goods and services can lead to permanent realignments in the marketplace. For example, during the recession of 2008-2009, global trade dropped 30 percent relative to gross domestic product (GDP), and more than 80 percent of this drop was related to a shift in spending away from manufactured goods (Eaton et al., 2011). Following Delios and Beamish (2001), we define intangible assets as the expenditure intensity of research and development (R&D) and advertising (Dierickx and Cool, 1989; Chang, 1995), which also conforms to Caves (1996) concept. Srinivasan et al. (2011) noted that, in recessions, expenditure with limited short-term effects, such as investments in R&D and

Management Decision Vol. 52 No. 8, 2014 pp. 1491-1515 r Emerald Group Publishing Limited 0025-1747 DOI 10.1108/MD-01-2014-0034

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advertising, receive particularly close scrutiny, as firms seek to cut costs. Even if recessions and company crises lead a firm to opt for retrenchment to preserve liquidity, continued investments in R&D and advertising may play a critical role in sustaining the firm’s long-term viability (Steenkamp and Fang, 2011; Villalonga, 2004; Mitchelmore and Rowley, 2013). R&D investments during a recession may represent an opportunity to increase competitiveness and long-term technological advantages (Barrett et al., 2009; Audretsch and Vivarelli, 1996). Similarly, increasing the marketing and advertising budgets can position firms advantageously during and after a recession, while cuts in these areas can hinder a company’s market share and profitability (Scanlon, 2009). Despite the general agreement that bold entrepreneurial strategic decisions positively affect the position of a company facing competitive challenges (Hitt et al., 1998; Lichtenthaler, 2007), studies on R&D and advertising effectiveness in a recession reported mixed findings (for a review, see Srinivasan et al., 2011). The main reason for this lack of consensus seems to be the failure to apply contingency theory to the relevant data. According to contingency theory, to produce high performance, companies have to align themselves advantageously with a set of situational factors termed “contingencies” (for a review, see Burton and Obel, 1998). In their recent work, Srinivasan et al. (2011) applied this approach and found that the effects on profits and stock returns of changes in firms’ R&D and advertising spending in recessions were indeed contingent on measures such as market share, financial leverage, and product-market profile. However, the authors used secondary data that “precluded consideration of organizational factors [y] which are critical in leveraging returns from R&D and advertising spending” (Srinivasan et al., 2011, p. 63). In this paper, we address this gap by introducing business model innovation as an additional contingency factor that captures a firm’s ability to leverage its investments in R&D and advertising. Specifically, we ask: How do a firm’s market share, financial leverage and business model dynamics interact with R&D and advertising investments to affect firm performance during recessions? To address this question, we started with the fact that most of the empirical research focussing on recessions has considered only observable and measurable variables, such as R&D, leverage or advertising (Srinivasan et al., 2005, 2011; Tellis and Tellis, 2009). We therefore proposed that, even if variables measured using secondary data can reasonably summarize changes over time in the strategic approach of the company, the introduction of business model innovation as a contingent factor could increase the explanatory power of empirical analyses in strategic management research (Zott et al., 2011). Indeed, to date, neither the economics literature nor that of strategy and organizations has fully recognized the importance of business model innovation (Teece, 2010; Zott et al., 2011). In a recent literature review, George and Bock (2010) stressed that the dynamics of business models represent a potentially rich source of information about how firm characteristics and strategies interact with the underlying changes in the opportunity landscape, such as those that occur during and after a recession. In particular, understanding the dynamics of business models is one of the most promising avenues for explaining the intangible component of firms’ competitive structure and can therefore complement standard moderating factors like market share and leverage (Teece, 2010; Zott and Amit, 2007, 2008). Our analysis employed contingency theory (Donaldson, 1996; Zeithaml et al., 1988) to understand how diverse factors affect firm performance. According to contingency theory, there is no one optimal strategy that fits all types of companies. Contingent factors such as size, investments, and task uncertainty interact to determine the best


strategic decisions (Donaldson, 1996) given a firm’s particular context and characteristics (Zeithaml et al., 1988). With contingency theory in mind, we tested whether strategic decisions in small- and medium-sized enterprises (SMEs) such as R&D and advertising expenditure affect a firm’s performance during recession, by considering the moderating effects of market share and leverage. We also tested whether business model change/innovation moderates the relationship between firms’ R&D and advertising investments and performance during a recession. While contingency theory has been thoroughly applied to listed companies (e.g. see Zott and Amit, 2008; Srinivasan et al., 2011), the literature has otherwise paid surprisingly little attention to SMEs despite their widespread presence and importance in the global economy (Wymenga et al., 2011; Eggers and Kraus, 2011; Soriano and Dobon, 2009). We therefore think that applying our model to SMEs represents an important contribution to, and expansion of, existing knowledge. By integrating a unique data set on business model changes with companies’ accounts over the period 2000-2010, we found that, during recessions, both market share and leverage moderated the effects of investments in R&D and advertising (i.e. intangibles) on performance in the analyzed SMEs. We also found that changes in business models were the most important single contingent factor accounting for performance. Specifically, we found that during a recession, the performance impact of the analyzed firms’ investment in intangibles increased as their market share increased and decreased as the financial leverage increased. We also ascertained that a noveltycentered business model, together with investments in intangibles, has a positive effect on performance during recessions. This study makes two contributions to the literature. First, it extends the application of contingency theory from large businesses to SMEs. It shows whether and how contingencies, such as a firm’s market share and leverage, moderate the relationship between performance and investments in R&D and advertising in SMEs. By doing so, this study offers an input to the debate on how SMEs develop and sustain their competitive advantage during periods of recession (Ashworth, 2012; Lo, 2013; Fernandes et al., 2013; Purcarea et al., 2013). Second, it addresses the call for additional data “about the strategic effects of business models and how they influence the positioning of firms in their competitive environment” (Zott and Amit, 2008, p. 20) by introducing business model change/innovation as a new contingency factor, and by empirically testing its effects on “objective measures of firm performance” (Bock et al., 2012, p. 301). The following sections present our theory and model and explain the data and methods used to test that model. We then present our results, and conclude with a discussion of our findings and implications for future research and for business practice. Hypotheses Contingency theory emerged in the 1950s and 1960s, when scholars began to argue against the prevailing view that it should be possible to define universal best strategies that would produce optimal outcomes across all types of firms (e.g. Barnard, 1938). Scholars espousing the contingency theory instead proposed that “organizations are more effective when the design of their structures and processes are internally coherent and fit, or match, their environmental demands” (Van de Ven et al., 2013, p. 402). A firm’s performance outcomes are the result of this fit between its external context and internal features (Van de Ven et al., 2013). In other words, external conditions produce particular organizational designs and strategic choices, and there is an


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appropriate linkage among the internal and external factors and performance (Burns and Stalker, 1961; Chandler, 1962; Galbraith, 1973; Lorsch and MacIver, 1989). For the authors adopting this perspective, a firm’s performance depends on a set of factors such as the environment (e.g. Burns and Stalker, 1961), the firm’s organizational structure (e.g. Mintzberg, 1979), the allocation of resources by managers (e.g. Child, 1974), and the firm’s general strategic approach (e.g. Chandler, 1962). The debate initially focussed on whether structure follows strategy to affect firm performance (e.g. Chandler, 1962) or vice versa (e.g. Bower, 1970). More recently, attention has shifted to the interaction between the two to explain a firm’s success (e.g. Mintzberg, 1990). Empirical evidence supports this last stream. For example, structural forms of organization such as business model features have interacted with product-market strategies to affect a firm’s performance (Zott and Amit, 2008). Moreover, it has been shown that contingency theory can be useful to explain the strategic and organizational choices that a firm can make when selecting a particular business model (Markides, 2013; Markides and Charitou, 2004). However, in their critical review of suitable approaches in entrepreneurship and small business research, Harms et al. (2009) showed that one of the risks of adopting contingency theory is that the consequent analytical approach may be too simplistic and lead, for instance, to restricted analysis on the interaction between two single domains. As with other approaches, it may also not account for dynamic aspects (Harms et al., 2009). However, these issues can be overcome through the inclusion of multiple interaction terms and the use of longitudinal databases. In this paper, we seek to enrich the debate by considering how firm contingencies (i.e. a firm’s market share, financial leverage and changes in its business model) interact with strategic choices (i.e. investments in intangibles) to affect firm performance by considering external circumstances (i.e. whether or not there is a recession in progress). We tested contingencies based on current literature and applied existing concepts to the context of Italian clothing SMEs before and during the 2008-2010 recession[1]. We propose that three contingencies moderate the rewards that can be achieved through investment in intangibles during recessions: first, a firm’s market share (total sales divided by the sales of all firms in the firm’s primary two-digit ATECO classification code) which provides market power (Buzzell and Gale 1987); second, financial leverage (the ratio of a firm’s bank debt to sales) which constrains a firm’s strategic options ( Jensen and Meckling, 1976); and finally, changes in a firm’s business model (Zott and Amit, 2008). Moderating effects of market share in recessions While businesses have little or no control over environmental variables, they can define a set of other strategic firm contingencies that can be classified as “strategic position” variables and “strategic choice” variables (Hambrick, 1983a, b). Investments in R&D and advertising can be categorized as “strategic choice” variables, and present attributes that can be readily changed in the short term. Market share, a variable affecting firms’ “strategic position” (Hambrick and Lei, 1985), has been shown to be a significant variable in determining the effects of firms’ investments on profitability (Hambrick and MacMillan, 1984; Berry, 2006; Steenkamp and Fang, 2011). Market share can affect the relationship between investments in intangibles and performance. For example, Shama (1993) found that, during recessions, smaller companies, which typically have smaller market shares, are more willing to cut advertising budgets since these are thought to be less crucial in protecting those firms’ competitive advantage


than would be the case for larger firms. Additionally, compared with all firms, those with small market shares may rely less on R&D expenditures (Acs and Audretsch, 1989, 1990; Audretsch and Weigand, 2005; Eggers et al., 2012), indicating that the perceived rewards of R&D are minimal for firms with smaller market shares. There are several reasons why SMEs with greater market share may benefit more from investments in intangibles during recessions than those with lower market share. For example, firms with larger market share can achieve economies of scale in their R&D projects (Buzzell and Gale, 1987) and higher profitability in their advertising investments (Lecraw, 1983). SMEs with greater market share may have more opportunities to leverage their intangibles since consumers tend to perceive firms with higher market shares as more likely to survive an economic decline (Pearce and Michael, 2006; Srinivasan et al., 2011). Indeed, firms with high market shares benefit from high levels of customer awareness and market penetration (Bloom and Kotler, 1975). As a consequence, consumers may be more willing to buy new products (derived from investments in R&D) from SMEs with high market share than from those with low market share. Given these arguments, we propose: H1. During a recession, the performance impact of a firm’s R&D and advertising investments increases as its market share increases. Moderating effects of financial leverage in recessions Another strategic position contingency is financial debt. This, defined as the extent to which a company uses debt to finance its assets ( Jensen and Meckling, 1976), may constrain a firm’s ability to obtain further financing. This increases its rigidity and limits its strategic options (Grewal and Tansuhaj, 2001). A company’s debt level also affects its behavior, determining agency problems with bondholders and shareholders (Myers, 1977). As a consequence, highly leveraged companies are typically less inclined to invest in R&D (Ho et al., 2005, 2006; Long and Malitz, 1985) and advertising (Grullon et al., 2006; Long and Malitz, 1985). SME ownership and management are typically concentrated in the same individuals, minimizing the potential for conflicting interests between company owners and managers (Cowling, 2003; Jensen et al., 1992). Since SMEs tend to have lower debt capacity and are undercapitalized (i.e. have less equity in the business) compared to larger or public firms, high levels of debt increase their need to use resources efficiently (Baker and Nelson, 2005; George, 2005). We therefore contend that financial debt is a significant contingent variable. With regard to R&D, Srinivasan et al. (2011) asserted that if a firm with high levels of debt increases R&D investments during a recession period, it signals that R&D programs are vital to its competitiveness. In such a case, the firm expects the returns on its R&D investment to exceed the costs resulting from the increased debt (Srinivasan et al., 2011). We suggest that this statement applies equally to SMEs. Indeed, it is possible for SMEs with high levels of debt that increase R&D investments during periods of adverse economic conditions, and hence develop better new products, to obtain market recognition and so enhance their performance. By the same token, firms with high levels of financial debt generally tend to decrease their spending on advertisement (Grullon et al., 2006; Long and Malitz, 1985). Consequently, when a highly leveraged firm increases its investment in advertising during a recession, it signals to the market that its products or services are different from or of higher quality than those of its competitors (Srinivasan et al., 2011).


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This should lead to a greater increase in sales and profits for the same advertising spend. For example, Asgharian (2003) found that, even if highly leveraged firms lose market share to their less-leveraged competitors during a downturn, they are still capable of retaining a relatively higher growth in profitability (Asgharian, 2003). Recent empirical evidence also showed that advertising and R&D lower a firm’s systematic risk, the portion of risk accounted for by the changes in average market portfolio returns[2] (McAlister et al., 2007), and positively influence the market value of the firm (Hirschey and Weygandt, 1985; Chan et al., 1999). We therefore propose: H2. During a recession, the performance impact of a firm’s R&D and advertising investments increases as its financial leverage increases. Moderating effects of business model changes We expect firms’ investments in intangibles to affect performance during recessions differently, depending on their business models. SMEs’ business models can be defined as “the design of organizational structures to enact a commercial opportunity” (George and Bock, 2010, p. 99). It is generally recognized that business models can be both enabling and limiting elements for the growth and exploitation of resources and investments (Amit and Zott, 2001; Garnsey et al., 2008; Mahadevan, 2000; Morris et al., 2005; Tracey and Jarvis, 2007). Business models enable a firm’s success when they are dynamic: a recent literature review reveals “an increasing consensus that business model innovation is key to firm performance” (Zott et al., 2011, p. 1033). For example, research confirms that a noveltycentered business model design positively affects the performance of entrepreneurial firms (Zott and Amit, 2007). Particularly in unstable situations, business model change represents a way to innovate and therefore ensure a firm’s survival (Perlow et al., 2002; Fuller et al., 2008) and long-term performance (George and Bock, 2010; Grewal and Tansuhaj, 2001). Business model development and change can often be related to the need to exploit new opportunities (George and Bock, 2010; Franke et al., 2008; Markides, 2008) or to adapt to the firm’s life-cycle advancement (Andries and Debackere, 2007). Business model change is also seen in the literature as a vehicle for firm rejuvenation (Demil and Lecocq, 2010; Ireland et al., 2001; Johnson et al., 2008; Sosna et al., 2010). However, there are some barriers to business model improvement. For example, assets and processes may be subject to inertia, and managers may fail to recognize the latent value of a new business model (Bouchikhi and Kimberly, 2003; Chesbrough, 2010). Empirical evidence shows that the business model is intertwined with strategy and firm performance (Zott and Amit, 2008) and that a change in the firm’s business model can capture value from innovation (Chesbrough and Rosenbloom, 2002; Trimi and Berbegal-Mirabent, 2012). The idea that SMEs can adapt more easily to changes in the environment because of their more manageable size has been widely demonstrated (Soriano and Dobon, 2009; Mazzarol and Reboud, 2006). The link between intangibles, such as advertising and R&D investments, and performance, can also change according to a firm’s business model features (D’aveni and Ravenscraft, 1994). A business model where intangibles such as marketing functions and technology are well managed can influence the rate of entrepreneurial firm development (Valliere, 2010).

During recessions, therefore, expenditure on R&D and advertising can yield low value unless accompanied by real change in the organizational structures of the firm. We therefore propose:

How SMEs leverage intangibles


H3. During a recession, the performance impact of a firm’s R&D and advertising investments increases as a business model change occurs. Methods Study context and data The context in which we tested our hypotheses is Italian SMEs in the clothing industry. This context, as we will show, involves strategic decisions faced by SMEs worldwide. Although the Italian clothing sector has historically established its competitive advantage on the basis of quality, creativity, and innovation, over the past decade many companies have been, and probably still are, under threat, mainly because of the erosion of their relative cost advantages and the absence of a sufficient scale of operations (Dunford, 2006), exacerbated by the recent recession. According to Dunford (2006), most Italian small- and medium-sized clothing companies must prepare themselves for changing market structures by changing their business models and investing in research, especially with regard to knowledge-intensive design, and marketing activities such as advertising and promotion. This is the main reason why Italian clothing SMEs represent a good subject for our analysis, but there are also two other reasons. First, in many countries, SMEs represent over 60 percent of total employment in manufacturing. In Italy, they constitute 80 percent of total employment (Ayyagari et al., 2007). Second, Italy has traditionally been strong in textiles and clothing (Burroni et al., 2008; Camuffo et al., 2008) and is the leader in the European clothing industry with a 40 percent share of the entire EU production (European Commission, 2003). Our analysis used a data set that includes information on a sample of 376 Italian SMEs in the clothing industry. The data set, which contains disaggregated information at the firm level, was built by matching two complementary sources: first, a crosssectional survey data set, collected directly from the companies using questionnairebased phone interviews and second, an accounting data set that consists of the company accounts of interviewed firms from 2000 to 2010 (AIDA – Bureau van Dijk). The questionnaire was addressed to the “person in charge of major company decisions,” i.e. the chief executive officer, the chairman/president, or high-ranking executives. These individuals were targeted as the most knowledgeable people in their firms. This research therefore uses a key informant approach, which is particularly well suited in SMEs research (Kraus et al., 2012; Eggers et al., 2013). An interview outline was pretested in a subsample of eight companies, which were not included in the final study. Companies were selected that met three criteria: first, they were active firms located in Italy; second, they were operating in the clothing industry (their four-digit ATECO codes were 1411, 1413, 1414, 1439)[3]; and finally, they had between ten and 500 employees. In total, 1,508 Italian companies matched these criteria (Istat Istituto Nazionale di Statistica, 2001). In total, 56 percent (844 firms) of companies in this group were considered appropriate for our analysis, as their financial information was included in the AIDA database[4]. Phone interviews were conducted with all the companies between March and May 2011. A total of 408 firms provided the required information. t-tests indicated no significant difference between respondents and non-respondents in terms of firm size, measured as both total sales and employees,

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and investments in intangibles, measured as spend on R&D and advertising. A total of 32 companies were excluded from the analysis because of unreliable information, leaving us with a final sample of 376 firms. The response rate was 44 percent (24.9 percent when calculated on the total number of firms in the Italian clothing industry), in line with the average response rates of similar studies (Baruch and Holtom, 2008).

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Measures and model In order to gain insight into the impact of R&D/advertising policy on firm performance, we regressed various measures of firm performance on firm investment in intangibles and on a set of controls. Given our interest in the role of firm strategy during the financial crisis, we estimated the impact of R&D and advertising on firm performance by splitting the sample according to two different time intervals: the 2000-2007 period (i.e. pre-crisis) and the 2008-2010 recession. We then tested the recession variable against the main variables and a set of moderating factors in order to identify the differential impact on intangibles in the two sub-periods: PERFORMANCE i;t ¼ aþb1 RECESSION i;t þb2 INTANGIBLE i;t þb3 RECESSION INTANGIBLE i;t þb4 RECESSION INTANGIBLE i;t MODER FACTORi;t1 þb5 CONTROLS i;t1

ð1Þ

þb6 MODER FACTORt1þui;t

where PERFORMANCE was a measure of firm performance at time t (measured as sales growth, return on sales (ROS) and total factors productivity (TFP)[5]). We followed Srinivasan et al. (2011) by including lagged moderating variables and controls in the main regression equation. RECESSION is the recession variable, measured by a dummy variable that took the value of 1 if the year was a recession year (i.e. 2008, 2009 or 2010), and 0 if otherwise. INTANGIBLE is the main explanatory variable (R&D and advertising on sales) at time t[6]. Although numerous objective measures of firm performance exist, profitability, sales growth and total factor productivity are arguably three of the most relevant for this study. Previous studies in similar contexts have adopted one or more of these objective approaches to measure firm performance, such as ROS (Lu and Beamish, 2001; Chiao et al., 2006; Qian, 2002; Shrader, 2001; Greve, 2003), and sales growth (Lu and Beamish, 2001; Kamber, 2002; Voulgaris et al., 2004; Shrader, 2001; Chiao et al., 2006). Investments in R&D and advertising, and business model changes have direct implications for both ROS and sales growth. Previous research has also indicated that strategic assets investments, such as R&D and advertising, positively affect total factors productivity (Urata and Kawai, 2002; Mahmood and Rufin, 2005; Balasubramanian and Lieberman, 2010). We considered three different moderating factors (MODER_FACTOR): Market Share (MS), Finance, and Business Model (BM). The Market Share (MS) variable measured the firm’s market share, or its sales as a percentage of total industry sales. Industry data come from all clothing companies available in the AIDA data set. The Finance variable was the ratio of bank debt to sales (Coltorti, 2006), because this can capture the financial constraints that hinder firms from sustained sales growth. The Business Model variable was a dummy variable that took the value of one in the year when the business model changed and remained equal to one for the following years. Data on business models was obtained by asking the interviewed people the

following questions: “Please indicate i) the item that best describes the actual business model of your company among the list below[7]; and, ii) the year that model was introduced, or no year if the model had not been changed since 2000.” It was assumed that those in charge of decision making could determine this change with reasonable accuracy. The list of business models provided was:


.

Craft labs with direct selling onsite: hand crafters and small manufacturers that sell their products in the laboratory.

.

Phase specialists: producers specializing in one or more phases of the production process.

.

Process specialists: producers specializing in the entire production process.

.

Low-quality producers for mass retailers: producers of low-quality products distributed/sold by distribution chains or mass retailers.

.

Medium-high quality producers for mass retailers: producers of medium-high quality products distributed/sold by distribution chains or mass retailers.

.

Brand-owners, innovation-oriented producers: producers that distribute under their own brand-name, with significant interest in market innovation (e.g. design) and technical innovation (e.g. new materials).

.

Brand-owners, export-oriented producers: producers that distribute under their own brand-name, mainly interested in foreign markets.

.

Brand-owners, mass retailer-oriented producers: producers that sell products to distribution chains or mass retailers under the producer brand-name (not included in previous categories).

.

Converters: companies that create a network of subcontractors to manage the product production process, from the early stages of idea generation, whether developed internally or proposed by a client, to the finished product along the whole production chain.

.

Prototyping: firms that design and make the prototype of the product, and help in the selection of reliable suppliers without direct involvement in the manufacturing process.

CONTROLS were firm age (number of years since establishment), value added per number of employees, and outsourced services on sales ratio (total cost of outsourced services over sales). Firm age is included in order to account for the role of the organizational life cycle in the investment-performance relationship (Hanks et al., 1993; Miller and Friesen, 1983). To a certain extent, firm age is related to the level of experience, learning ability, and managerial competencies that an organization can draw upon in facing competition (Zott and Amit, 2008; Miller and Friesen, 1983) and downturns (Graham et al., 2011; Zott and Amit, 2008). Value added per capita indicates the firm’s positioning in the value chain (Pelham, 2000). The literature differentiates, among manufacturing companies, between those that avoid cost competition by basing their competitiveness on high value-added activities (i.e. companies that compete on design, brand-name manufacturing, marketing and retailing) and those that do not (Humphrey and Schmitz, 2002). As value added per worker is considered an important factor in the textile/clothing industry (Becchetti et al., 2007), we included it as a control variable.


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Outsourced services on sales (i.e. the level of a firm’s external sourcing) measures the extent of vertical integration (Novak and Stern, 2008). Considering the potential impact of outsourcing activities on firm outcomes (Poppo and Zenger, 1998; Salimath et al., 2008; Zeynep Aksin and Masini, 2008; Jiang et al., 2007), we also controlled for the presence of outsourcing services[8]. CONTROLS also included year and region fixed effects; ui,t is a normally distributed error term. Descriptive statistics Table I provides descriptive statistics for the variables used in the study, as well as their correlations. The low mean of R&D and advertising is in line with the average values of the clothing industry in Italy and Europe[9]. The firms were on average 23.6 years old, although a small number were significantly older. As the table shows, correlations among the independent variables suggest that multicollinearity is unlikely to be a problem. Table II summarizes the distribution of sample firms by business model. The data show that phase and process specialists accounted for the largest number of firms in 2000. During the decade from 2000 to 2010, intense competitive pressure forced most companies to change their strategic orientation toward a “flight

Variables

Mean SD Firms

1. Sales growth 0.12 0.36 2. ROS 0.02 0.09 3. TFP 0.02 0.39 4. R&D þ Adv on Sales 0.01 0.01 5. MS 0.01 0.01 6. Finance 0.26 0.25 7. Firm age 23.60 14.30 8. BM 0.54 0.27 9. Value added per capita 0.46 0.46 10. Outsourced services on sales 0.38 0.15

Table I. Descriptive statistics and correlations

Table II. Distribution of firm’s business models in 2000 and 2010 (absolute and percentage values)

1

2

3

4

5

6

7

8

9

10

356 1.00 355 0.08 1.00 354 0.03 0.39 1.00 351 0.02 0.16 0.09 1.00 364 0.01 0.11 0.01 0.07 1.00 362 0.19 0.07 0.38 0.21 0.01 1.00 364 0.09 0.20 0.26 0.36 0.01 0.09 1.00 348 0.39 0.30 0.23 0.13 0.08 0.09 0.10 1.00 344 0.04 0.29 0.44 0.03 0.37 0.21 0.10 0.16 1.00 344 0.01 0.09 0.32 0.18 0.10 0.07 0.04 0.11 0.20 1.00

Notes: Descriptive statistics (mean and SD) are calculated for 2010 – correlations are calculated over the period 2000-2010. The table indicates the average values of correlations over the ten years period Source: AIDA

Craft labs with direct onsite selling Phase specialists Process specialists Low-quality producers for mass retailers Medium-high quality producers for mass retailers Brand-owners. Innovation-oriented producers Brand-owners. Export-oriented producers Brand-owners. Mass retailer-oriented prod Converters Only prototyping Total

n

n

%

%

2000 22 78 85 10 18 68 38 34 13 10 376

2010 18 51 46 6 10 107 79 26 9 24 376

2000 5.90 20.70 22.60 2.70 4.80 18.10 10.10 9.00 3.50 2.70 100

2010 4.80 13.60 12.20 1.60 2.70 28.50 21.00 6.90 2.40 6.40 100


to quality,” which pushed firms away from previous highly specialized market positions toward a broader role in the industry value chain (Camuffo et al., 2008). As a consequence, product quality, innovation, and branding were the tools which firms exploited to integrate downward and upward in the industrial value chain. Before moving to the multivariate analysis, it is important to note that studying the impact of investment in intangibles on firm performance in a panel raises the concern that intangibles may not be exogenous as an independent variable on changes in the outcomes that are being assessed. If they are not, the coefficient on the estimated variables could be biased (Barber and Lyon, 1996). To obviate this potential problem, we ran a performance-based matching procedure that compared sample and control firms grouped according to: profitability and value added per capita, which were used in the subsequent empirical analysis. The procedure compared firms with similar pre-event economic structure in order to attest if a different outcome in performance could be ascribed to a different investment strategy in intangibles. The procedure enabled us to identify two different samples of companies that did not invest in R&D and advertising, which were used as a matched control group for the two samples of firms that did invest in R&D and advertising (Huson et al., 2004).


Results[10] Table III shows that there was no particularly significant relationship between investments in intangibles and performance in the pre-recession period[11]. Both samples (matched on profitability and value added) display mainly positive but not significant coefficients for the intangible variables. Conversely, the impact of investments in R&D and advertising proved to be significant during the recession (e.g. b ¼ 6.44; po0.05 when ROS is the dependent variable), and almost offsets the negative effect of the recession on performance (i.e. b ¼ 6.16; po0.01 when ROS is the dependent variable). Therefore, the first conclusion that can be drawn is that, among Italian clothing SMEs, as shown in previous literature for larger firms, intangible assets stabilize firm performance during recession. Control variables showed that Panel Aa Sales growth R&D þ Adv/Sales REC (Dummy 2008-2010) [(RD þ Adv)/Sales] REC [(RD þ Adv)/ Sales] REC MS Firm age Value added (per capita) Outsourced services/sales Obs Firms R2

0.26* 0.01* 0.01*** 0.22** 0.01 0.02 0.14** 1618 291 0.21

Panel Bb

ROS

TFP

Sales growth

4.44 6.16*** 6.44**

0.01 0.15* 0.13

0.20 0.06** 0.18**

4.11 6.26*** 7.17*

0.09 0.17* 0.11*

0.03 0.02 0.03* 5.50*** 1618 291 0.37

0.01* 0.01 0.04* 0.51** 1618 291 0.26

0.29* 0.02*** 0.01*** 0.48*** 2038 342 0.21

0.02* 0.01 0.12*** 8.01*** 2038 342 0.34

0.03 0.01 0.05*** 0.72*** 2015 342 0.28

ROS

TFP

Notes: The regressions include controls for: firm age, value added per capita, outsourced services on sales ratio. Controls also include year and region fixed effects. aMatched sample estimates: firms are matched on value added per capita (2000-2001 average). bMatched sample estimates: firms are matched on initial profitability (2000-2001 average ROS). *po0.10; **po0.05; ***po0.01

Table III. The impact of intangibles [(RD þ Adv)/ Sales] on firm performance (panel estimates 2000-2010)

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firms that avoid cost competition, that is, firms with higher value added per capita (e.g. in panel B when ROS is the dependent variable; b ¼ 0.12; po0.01) and lower outsourced services to sales ratios are more likely to experience good performance (e.g. in panel A when ROS is the dependent variable; b ¼ 5.50; po0.01). Thus, among the analyzed companies, those that based their competitiveness on high value-added activities and those with higher vertical integration showed better performance. H1 suggests that, during recessions, the impact on performance of a firm’s R&D and advertising increases as its market share increases. Table IV shows that, when the effect of investment in intangibles and external economic conditions are taken into account, companies with a larger market share benefit more from investment in R&D and advertising than those with smaller market shares (i.e. b ¼ 0.22; po0.05). H1 on the role of market power as a moderating factor is therefore supported in our sample. Having a larger market share helped firms in our sample to leverage their investment in intangibles. Table IV summarizes all the estimated relationships for Equation (1) using two different panels (samples matched on value added in panel A and on initial profitability in panel B) and the three moderating variables (Market Share, Finance and Business Model Change). Estimated results show that performance among the sampled companies is more reactive to intangibles when measured using growth of sales and, to Dependent variable (a)Panel estimates 2000-2010 a Panel A: firms matched on value added per capita [(RD þ Adv)/Sales] RECESSION MS [(RD þ Adv)/Sales] RECESSION Finance [(RD þ Adv)/Sales] RECESSION BM Panel B: firms matched on profitability (ROS) [(RD þ Adv)/Sales] RECESSION MS [(RD þ Adv)/Sales] RECESSION Finance [(RD þ Adv)/Sales] RECESSION BM (b)Marginal effects 2000-2010 b Panel A: firms matched on value added per capita [(RD þ Adv)/Sales] RECESSION MS [(RD þ Adv)/Sales] RECESSION Finance [(RD þ Adv)/Sales] RECESSION BM Panel B: firms matched on profitability (ROS) [(RD þ Adv)/Sales] RECESSION MS [(RD þ Adv)/Sales] RECESSION Finance [(RD þ Adv)/Sales] RECESSION BM

Table IV. The impact of [(RD þ Adv)/Sales] on firm performancec

Sales growth

0.22** 0.01 0.48***

ROS

0.03 0.05 0.03**

TFP

0.02* 0.01 0.20

0.30* 0.15* 0.36***

0.02* 0.02 0.02

0.01 0.01 0.07

17.40*** 1.50 44.30***

1.90* 7.90* 15.40**

2.80 10.10 21.30*

19.80* 0.20 35.80***

4.50** 11.20 18.30*

4.90 37.20 36.70**

Notes: The regressions include controls for: firm age, value added per capita, outsourced services on sales ratio. Controls also include year and region fixed effects. aThe table reports the coefficients of the interaction variables included in the model for the three dependent variables. Panel A includes 291 firms matched on average 2001-2002 individual value added per capita. Panel B includes 342 firms matched on average 2001-2002 individual profitability. bStudent t-test on the difference of the mean ( p(Tot)): *po0.10; **po0.05; ***po0.01. The table reports the percentage changes (computed by marginal effect) that the dependent variables undergo passing from the 25th to the 75th distribution percentile of each moderating variable (MS and Finance). For the BM variable the change is between 0 and 1. Panel A includes 291 firms matched on average 2001-2002 individual value added per capita. Panel B includes 342 firms matched on average 2001-2002 individual profitability. *po0.10; **po0.05; ***po0.01


a lesser extent, profitability. The impact of intangibles is lower for total factor productivity. This shows that, in our sample, the performance effect of intangibles acts more through the market side of a firm’s operations than through its ability to combine factors (TFP). H2 suggests that, during a recession, the performance impact of a firm’s R&D and advertising investments increases as its financial leverage increases. Table IV indicate that the financial leverage of a company has a negative effect on the relationship between intangibles and performance (e.g. in Table IV, panel B, with Sales Growth as a dependent variable, b ¼ 0.15; po0.10). This is contrary to our second hypothesis and indicates that for the analyzed companies, financial distress during recession neither necessarily increases the ability to exploit successfully investments in intangibles nor determines whether the firm obtains the desired market recognition, thus affecting sales growth. H3 suggests that during recessions, the effect on performance of a firm’s investments in intangibles increases as its business model changes. Panels A and B in Table IV show that the moderating effects of business models on performance are positive and statistically significant for sales growth (in panel A, b ¼ 0.48; po0.01 and in panel B, b ¼ 0.36; po0.01) and, in panel A, also for ROS (i.e. b ¼ 0.03; po0.05). H3 is therefore supported in our sample. Interestingly, even if the market share of the company has a positive effect on performance when intangibles are involved, the contribution of business model changes is larger for almost all the performance measures. However, part of the difference in the size of coefficients between market share and business model may be due to the latter being a dummy variable. The marginal effect of the explanatory variables confirms this picture. Table IV, which represents marginal effects, displays the percentage changes that the three dependent variables (Sales growth, ROS and TFP) undergo in moving from the 25th to the 75th distribution percentile of each regressor after the crisis. In general, the main determinants are very similar to those identified in the previous analysis, as both market share and business model show high and significant values of percentage changes in the dependent variable. For example, in Table IV, panel A, the interaction between the variables R&D and advertising, and recession, and business model produces a change of 44.3 percent ( po0.01) for sales growth, of 15.4 percent ( po0.05) for ROS, and 21.3 percent ( po0.10) for TFP. Conversely, the moderating role of finance is low and rarely statistically significant. These results show that, in our sample, market share and change in business model are the main drivers of improved performance, as measured by sales growth and profitability. The marginal effects therefore confirm that, in terms of economic impact, the analyzed firms weathered crises better not only when they had invested in R&D and advertising, but also when these investments were accompanied by a larger market share and a new business model. This second contingency, business model change, is hardly ever the result of pure luck (Barber and Lyon, 1996); rather, it is a deliberate strategy worthy of detailed analysis. Direction of causality To provide further evidence supporting our results, we investigated the direction of causality by estimating the model with a set of leads and lags for our main variable. Table V summarizes the estimated results of Equation (1) for a subset of firms augmented with lags and leads for the R&D and advertising variable. We examined the significance of lead and lag variables for a window of three years before and after


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Table V. Estimates of the impact of intangibles [(RD þ Adv)/Sales] on firm performance with lags and leads for the R&D þ Advertising on sales variable

Panel Aa Sales growth ROS [(RD þ Adv)/Sales] REC (Dummy 2008-2010) [(R&D þ Adv)/Sales]t þ 2 REC [(R&D þ Adv)/Sales]t þ 1 REC [(R&D þ Adv)/Sales]t REC [(R&D þ Adv)/Sales]t1 REC [(R&D þ Adv)/Sales]t2 REC Firm age Value added (per capita) Outsourced services/sales Obs Firms R2

0.14 0.05 0.03** 0.08* 0.02* 0.05 0.02 0.02 0.01 0.10** 1,442 287 0.23

TFP

3.24 0.08 2.11** 0.59* 0.15 0.05 1.74** 0.03 4.21** 0.44 3.78 0.02 4.71* 0.01 0.02 0.01 0.12** 0.08 4.21 1.88** 1,440 1,384 284 272 0.39 0.27

Panel Bb Sales growth ROS 0.10 0.20** 0.48* 1.84** 0.21** 0.12 0.36* 0.02** 0.02* 0.68 2,024 339 0.23

TFP

3.77 0.06 5.88* 0.12 3.22 0.23 2.82* 0.83 6.15** 0.31* 1.49 0.16 2.48* 0.49** 0.02 0.08 0.01* 0.04** 6.12 0.74 2,020 1,991 337 314 0.37 0.30

Notes: The regressions include controls for: firm age, value added per capita, outsourced services on sales ratio. Controls also include year and region fixed effects. aMatched sample estimates: firms are matched on value added per capita (2000-2001 average). bMatched sample estimates: firms are matched on initial profitability (2000-2001 average ROS). *po0.10; **po0.05; ***po0.01

the investment in R&D and advertising. After a first estimation run, we dropped the t þ 3 and t3 variables because they were not significant, and Table V summarizes only the remaining variables. Following Autor (2003), lag and lead variables are set equal to one only in the relevant year. Estimated results show that the contemporaneous R&D and advertising variable is significant in almost all the models, thus confirming previous estimated results. There is no generalized evidence of any effect on performance from the lagged R&D and advertising variable, except for the two-year lagged variable, whose coefficient is significant for profitability in panel A and for profitability and sales growth in panel B. However, both the size and the statistical significance of estimated coefficients are small, suggesting that lagged variables produce only marginal effects above those already shown by the contemporaneous main variable. There is some evidence of reverse causality, as the lead variables are significant in t þ 1 and t þ 2, in particular when sales growth and profitability are used as dependent variables. This suggests that a company’s good performance during a crisis allows it to spend more on R&D and advertising in the years following recession. More generally, when both lags and leads are considered, investments in R&D and advertising seem to be closely intertwined with company performance over time, thus confirming the crucial role of intangible variables in explaining the competitive success of companies during recession. Discussion In this paper we have used contingency theory to analyze how investments in intangible assets (R&D and advertising) affect firm performance during recession by taking into account contingent elements such as business model innovation/changes, firm leverage and firms’ market shares. In doing so, we have tried to extend the scholarly understanding of contingency factors from its previous application to large businesses toward a focus on SMEs. We have also tried to address the call for additional evidence demonstrating how


strategic decisions related to business models affect the positioning of firms (Zott and Amit, 2008, p. 20). This study integrated a data set on SME business model changes with companies’ accounts over the period 2000-2010. Our results confirmed that investment in intangible assets exerts a stabilizing effect on the performance of Italian clothing SMEs during a recession. We also found that, in our sample, during recession, the impact on firm performance of R&D and advertising investment increased as its market share increased. Contrary to our expectations, the impact on firm performance of R&D and advertising investment decreased as its financial leverage increased. Finally, according to our data, the impact on firm performance of investments in intangibles increased as the firm’s business model changed. The existing empirical evidence which indicates that R&D and advertising have positive effects on profitability (Lecraw, 1983; Qian, 2002; Luo and de Jong, 2012) and sales (Salomon and Shaver, 2005; Thornhill and Gellatly, 2005) is therefore supported by our findings, which stress the importance of intangibles for SMEs during recessions. Even if SMEs are more vulnerable and face greater obstacles compared to large firms (Beck et al., 2005), our data indicate that the positive performance impact of such firms’ investment in intangibles increased under recessionary conditions. We therefore found support for our H1 and H3, but reject H2. The finding that the analyzed firms’ market share positively moderated the relationship between investments in intangibles and performance supports H1. In other words, the positive impact on firm performance of investment in R&D and advertising was larger for firms with larger market shares. This observation is in line with previous research which suggested that firms with larger market share tend to consider such investments as more crucial for their survival during recession than firms with smaller market share (e.g. Shama, 1993; Acs and Audretsch, 1990). During recession, SMEs that invest in R&D and advertising, instead of opting for retrenchment, do so to strengthen their competitive position with a long-term perspective. However, the fact that our data supported H1 indicates that the benefits of this bold strategic decision may be larger for firms with larger market shares. The latter may indeed benefit from higher market penetration and customer awareness more than firms with smaller market share. These elements can increase the possibility of achieving economies of scales once a firm launches a new version of an existing product and advertises it. The negative moderating effect of leverage on performance is best explained by considering that the uncertainty caused by recession may reduce the responsiveness of intangibles to changes in demand (Bloom, 2007). This is especially true when a firm’s financial resources and strategic options are constrained by an economic downturn (Grewal and Tansuhaj, 2001; Ho et al., 2005, 2006; Long and Malitz, 1985; Qian, 2002). Srinivasan et al. (2011) found only partial support for the positive interaction effect between R&D and advertising spending in recessions and financial leverage in large public firms. Our findings, however, indicate that, at least for the studied SMEs, increasing debt during a recession does not necessarily increase their ability to exploit resources (Baker and Nelson, 2005; George, 2005). Our findings reconcile previously inconsistent findings of both positive and negative effects of debt on SMEs’ performance (Baker and Nelson, 2005; Thornhill et al., 2004; Ho et al., 2006; Qian, 2002) by showing that when debt is considered as a moderator of the relationship between investments in intangibles and performance in clothing SMEs, it has a negative effect on performance. In the context of Italian clothing SMEs, firms tend to be undercapitalized (Coltorti, 2006), and higher levels of leverage constrain their ability


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to obtain further financing, reducing their strategic options and eventually also their resilience, an essential element for such firms during recession (Pal et al., 2014). The finding that a change in the business model interacted positively with the intangible assets rewards allows us to conclude that investing in R&D and advertising may not be enough to assure good firm performance. Value is created only if the company is able to change with the context. Our analysis revealed that both intangibles and business model innovations can enhance clothing SMEs’ competitive advantage, independently as well as together, and therefore complement each other. This supports the proposition that to thrive during challenging periods, companies must be flexible enough to change their business models (Zott et al., 2011). This paper’s findings may be useful to managers and entrepreneurs in pointing out ways in which SMEs can successfully compete during a recession. In particular, we found that investing in R&D and advertising can increase performance, especially during challenging periods such as recessions. SMEs, however, should note that these positive effects are not always seen. In our sample, companies with smaller market shares saw fewer benefits than those with larger shares. Our data also showed that companies with higher financial leverage registered fewer benefits. We therefore conclude that investments in R&D and advertising during recession are advisable for clothing SMEs, but should be assessed with more caution by firms with smaller market share or high financial leverage. In other words, our findings also serve as a warning to managers and entrepreneurs that while higher market share may reinforce the positive effects of intangibles, high leverage may reduce them. Our results have shed some light on how, when and why investments in intangibles can positively affect performance. An important implication for business practice is that the results show that the analyzed companies gained maximum benefit from such investments only when they were coupled with business model innovations, and that this was especially true during recession. The explanation for this finding is that these factors seem to be contingent and therefore need to be considered together (e.g. Milgrom and Roberts, 1990). This is a useful avenue for future research. From a theoretical perspective, our research provides support for a contingencybased approach, and suggests that the rewards to be derived from firms’ R&D and advertising programs during recessions differ according to the SME’s characteristics (i.e. market share and leverage), and strategy (i.e. the chosen business model and whether or not it is changed in response to external conditions), even after controlling for the economic environment (i.e. whether there is a recession). This research has limitations that offer opportunities for further research. In our empirical analysis, we measured intangibles by considering the investments made each year in R&D and advertising. We did not measure these separately. Even though other research has suggested that investment in advertising and R&D are correlated and produce similar effects on performance (Malerba et al., 1999), further research focussing on the two elements separately might well produce new insights. In addition, our work is based on SMEs from the clothing industry. This narrow focus reduces generalizability. On the other hand, it allowed us to use a homogeneous sample that should “generate more fine-grained and more empirically-valid knowledge” when studying complex behaviors (Miller, 2011, p. 881). It also seems likely that the findings could be generalized to other countries that also have a large share of low-tech-mature industries, such as many European countries and several emerging economies. However, further research on other industries would be a useful extension. We view this study as a useful step in exploring the performance implications of strategic


decisions for SMEs in a recession, that will help entrepreneurs and managers determine how best to navigate during troubled times. We hope this study sheds more light on the subject and stimulates further work in the domain.

How SMEs leverage intangibles

Notes 1. The recession started with the financial crisis of late 2008 (Keely and Love, 2010) and there are good reasons to assert that GDP and income levels are unlikely to return any time soon to their initially projected path (OECD, 2010). Therefore, we defined the recession period for our purpose as starting in 2008 and ending with the last available year in terms of data (i.e. 2010 in our sample).

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2. Systematic risk is that portion of risk accounted for by the changes in average market portfolio returns. 3. The ATECO classification is the Italian coding based on the NACE classification of wearing apparel manufacturers. We included the segments that best describe the clothing industry. The description of each code as follows: 14.11 Manufacture of leather clothes, 14.13 Manufacture of other outerwear, 14.14 Manufacture of underwear, 14.39 Manufacture of other knitted and crocheted apparel. 4. The remaining 664 firms were excluded because they did not have a compulsory obligation to file their financial data in the Public Registry. 5. Sales growth is calculated as (SalestSalest1)/Salest1 , ROS is calculated as (net income before interest and tax)/sales. Finally, as in Bloom et al. (2011), we use a simplified definition of TFP as log (value added)0.40 log(capital)0.60 log (employees), where the factor weights are the cost shares estimated in the sample. 6. AIDA – Bureau van Dijk provides yearly information on “Intangible assets” that includes several items that are not the focus of this paper and therefore excluded from our analysis, to wit, start-up and expansion costs; licenses, goodwill. The annual value of expenses in R&D and advertising was computed by differentiating the stock values in the balance sheets of each year and by adjusting gross flows by an average four-year amortization rule. This allowed us to work with R&D and advertising values that refer to the annual value of expenses (i.e. flows). 7. This taxonomy has been adapted from that suggested by Camuffo et al. (2008) and Pozzana (2011) who built and validated Business Model taxonomies by means of cluster analyses and other multivariate analysis on the most comprehensive databases on SMEs in the clothing sector managed by the Italian Ministry of the Economy (i.e. Annual Industry Revenue Survey on Companies in the Textile and Clothing Sector – Studi di Settore). The definition of each business model is based on a grid of variables that captures the most relevant dimensions of business models in the industry. It refers to choices, activities, resources and capabilities that provide a unique identification of the company organizations structure: the position that a firm attains and maintains within the industry; the markets in which it competes (e.g. its role within the vertical contracting structure of the industry, degree of internationalization, customers’ portfolio); the activities it performs to attain and maintain these positions (such as scale of operations, nature and scope of activities); the resources and capabilities that enable it to perform these activities (e.g. technologies, people); and the relationships between these elements (for details see Pozzana, 2011; Camuffo et al., 2008). 8. Correlations between control variables are: firm age and value added per capita ¼ 0.0822; external sourcing and value added per capita ¼ 0.0825. 9. Data on the average yearly values of investments in R&D and advertising in the Italian and European clothing industry are available upon request. 10. Because of space constraints we only include in all the tables the main variables of interest. The estimates presented in the following tables include all the variables based on model/ Equation (1).

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11. A negative relationship between intangibles and performance was detected for the pre-recession period in the plain OLS estimates without the matched sample. Data are available upon request. We believe that a number of factors drive these results, in particular: the cost of R&D and advertising, which affects firm performance negatively; the more volatile response from the market to those firms that invest in intangibles as a response to poor financial performance; and a selection bias because firms that perform poorly may be forced to invest in intangibles in order to improve performance. References Acs, Z. and Audretsch, D.B. (1990), Innovation and Small Firms, MIT Press, Cambridge, MA. Acs, Z.J. and Audretsch, D.B. (1989), “Entrepreneurial strategy and the presence of small firms”, Small Business Economics, Vol. 1 No. 3, pp. 193-213. Amit, R. and Zott, C. (2001), “Value creation in e-business”, Strategic Management Journal, Vol. 22 Nos 6-7, pp. 493-520. Andries, P. and Debackere, K. (2007), “Adaptation and performance in new businesses: understanding the moderating effects of independence and industry”, Small Business Economics, Vol. 29 Nos 1-2, pp. 81-99. Asgharian, H. (2003), “Are highly leveraged firms more sensitive to an economic downturn?”, The European Journal of Finance, Vol. 9 No. 3, pp. 219-241. Ashworth, C.J. (2012), “Marketing and organisational development in e-SMEs: understanding survival and sustainability in growth-oriented and comfort-zone pure-play enterprises in the fashion retail industry”, International Entrepreneurship and Management Journal, Vol. 8 No. 2, pp. 165-201. Audretsch, D.B. and Vivarelli, M. (1996), “Firms size and R&D spillovers: evidence from Italy”, Small Business Economics, Vol. 8 No. 3, pp. 249-258. Audretsch, D.B. and Weigand, J.R. (2005), “Do knowledge conditions make a difference?: investment, finance and ownership in German industries”, Research Policy, Vol. 34 No. 5, pp. 595-613. Autor, D.H. (2003), “Outsourcing at will: the contribution of unjust dismissal doctrine to the growth of employment outsourcing”, Journal of Labor Economics, Vol. 21 No. 1, pp. 1-42. Ayyagari, M., Beck, T. and Demirguc-Kunt, A. (2007), “Small and medium enterprises across the globe”, Small Business Economics, Vol. 29 No. 4, pp. 415-434. Baker, T. and Nelson, R.E. (2005), “Creating something from nothing: resource construction through entrepreneurial bricolage”, Administrative Science Quarterly, Vol. 50 No. 3, pp. 329-366. Balasubramanian, N. and Lieberman, M.B. (2010), “Industry learning environments and the heterogeneity of firm performance”, Strategic Management Journal, Vol. 31 No. 4, pp. 390-412. Barber, B.M. and Lyon, J.D. (1996), “Detecting abnormal operating performance: the empirical power and specification of test statistics”, Journal of Financial Economics, Vol. 41 No. 3, pp. 359-399. Barnard, C.I. (1938), The Functions of the Executive, Harvard University Press, Cambridge, MA. Barrett, C.W., Musso, C.S. and Padhi, A. (2009), “Upgrading R&D in a downturn”, The McKinsey Quarterly, Vol. 2, pp. 1-3, available at: www.mckinsey.com/insights/operations/upgrading_ r_and_d_in_a_downturn Baruch, Y. and Holtom, B.C. (2008), “Survey response rate levels and trends in organizational research”, Human Relations, Vol. 61 No. 8, pp. 1139-1160. Becchetti, L., De Panizza, A. and Oropallo, F. (2007), “Role of industrial district externalities in export and value-added performance: evidence from the population of Italian firms”, Regional Studies, Vol. 41 No. 5, pp. 601-621.


Beck, T., Demirguc¸-Kunt, A. and Maksimovic, V. (2005), “Financial and legal constraints to growth: does firm size matter?”, The Journal of Finance, Vol. 60 No. 1, pp. 137-177. Berry, H. (2006), “Leaders, laggards, and the pursuit of foreign knowledge”, Strategic Management Journal, Vol. 27 No. 2, pp. 151-168. Bloom, N. (2007), “Uncertainty and the dynamics of R&D”, American Economic Review, Vol. 97 No. 2, pp. 250-255. Bloom, P.N. and Kotler, P. (1975), “Strategies for high market-share companies”, Harvard Business Review, Vol. 53 No. 6, pp. 63-72. Bloom, N., Eifert, B., Mahajan, A., Mckenzie, D. and Roberts, J. (2011), “Does management matter? Evidence from India”, NBER Working Paper No. 16658, National Bureau of Economic Research, Cambridge, MA, available at: www.nber.org/papers/w16658 (accessed November 12, 2013). Bock, A.J., Opsahl, T., George, G. and Gann, D.M. (2012), “The effects of culture and structure on strategic flexibility during business model innovation”, Journal of Management Studies, Vol. 49 No. 2, pp. 279-305. Bouchikhi, H. and Kimberly, J.R. (2003), “Escaping the identity trap”, MIT Sloan Management Review, Vol. 44 No. 3, pp. 20-26. Bower, J.L. (1970), Managing the Resource Allocation Process: A Study of Corporate Planning and Investment, Harvard Business Press, Boston, MA. Burns, T.E. and Stalker, G.M. (1961), The Management of Innovation, Tavistock Publications, London. Burroni, L., Crouch, C., Kaminska, M.E. and Valzania, A. (2008), “Local economic governance in hard times: the shadow economy and the textile and clothing industries around zodz´ and Naples”, Socio-Economic Review, Vol. 6 No. 3, pp. 473-492. Burton, R.M. and Obel, B. (1998), Strategic Organizational Diagnosis and Design: Developing Theory for Application, Kluwer Academic Publishers, Boston, MA. Buzzell, R.D. and Gale, B.T. (1987), The PIMS Principles, The Free Press, New York, NY. Camuffo, A., Pozzana, R., Vinelli, A. and Benedetti, L. (2008), “Not doomed to death: a map of small firms’ business models in the Italian textile apparel industry”, Sloan Industry Studies Conference, Boston, MA, available at: http://ssrn.com/abstract¼1135782 or http://dx.doi. org/10.2139/ssrn.1135782 (accessed November 12, 2013). Caves, R.E. (1996), Multinational Enterprise and Economic Analysis, Cambridge University Press, Cambridge. Chan, L., Lakonishok, J. and Sougiannis, T. (1999), The Stock Market Valuation of Research and Development Expenditures, National Bureau of Economic Research Cambridge, MA. Chandler, A.D. (1962), Strategy and Structure: Chapters in the History of the Industrial Enterprise, MIT Press, Cambridge, MA. Chang, S.J. (1995), “International expansion strategy of Japanese firms: capability building through sequential entry”, Academy of management Journal, Vol. 38 No. 2, pp. 383-407. Chesbrough, H. (2010), “Business model innovation: opportunities and barriers”, Long Range Planning, Vol. 43 Nos 2-3, pp. 354-363. Chesbrough, H. and Rosenbloom, R.S. (2002), “The role of the business model in capturing value from innovation: evidence from Xerox Corporation’s technology spin-off companies”, Industrial and corporate change, Vol. 11 No. 3, pp. 529-555. Chiao, Y.C., Yang, K.P. and Yu, C.M.J. (2006), “Performance, internationalization, and firm-specific advantages of SMEs in a newly-industrialized economy”, Small Business Economics, Vol. 26 No. 5, pp. 475-492. Child, J. (1974), “Managerial and organizational factors associated with company performance”, Journal of Management Studies, Vol. 11 No. 3, pp. 175-189.


MD 52,8


1510

Coltorti, F. (2006), “Il capitalismo di mezzo negli anni della crescita zero”, Economia Italiana, Vol. 3, pp. 665-687. Cowling, M. (2003), “Productivity and corporate governance in smaller firms”, Small Business Economics, Vol. 20 No. 4, pp. 335-344. D’aveni, R.A. and Ravenscraft, D.J. (1994), “Economies of integration versus bureaucracy costs: does vertical integration improve performance?”, The Academy of Management Journal, Vol. 37 No. 5, pp. 1167-1206. Delios, A. and Beamish, P.W. (2001), “Survival and profitability: the roles of experience and intangible assets in foreign subsidiary performance”, Academy of Management Journal, Vol. 44 No. 5, pp. 1028-1038. Demil, B. and Lecocq, X. (2010), “Business model evolution: in search of dynamic consistency”, Long Range Planning, Vol. 43 No. 2, pp. 227-246. Dierickx, I. and Cool, K. (1989), “Asset stock accumulation and sustainability of competitive advantage”, Management Science, Vol. 35 No. 12, pp. 1504-1511. Donaldson, L. (1996), “The normal science of structural contingency theory”, in Clegg, S.R., Hardy, C. and Nord, W.R. (Eds), Handbook of Organization Studies, Sage, London, pp. 57-76. Dunford, M. (2006), “Industrial districts, magic circles, and the restructuring of the Italian textiles and clothing chain”, Economic Geography, Vol. 82 No. 1, pp. 27-59. Eaton, J., Kortum, S., Neiman, B. and Romalis, J. (2011), Trade and the Global Recession, National Bureau of Economic Research, Cambridge, MA, available at: www.nber.org/papers/ w16666 (accessed November 12, 2013). Eggers, F. and Kraus, S. (2011), “Growing young SMEs in hard economic times: the impact of entrepreneurial and customer orientations — a qualitative study from Silicon Valley”, Journal of Small Business & Entrepreneurship, Vol. 24 No. 1, pp. 99-111. Eggers, F., Hansen, D.J. and Davis, A.E. (2012), “Examining the relationship between customer and entrepreneurial orientation on nascent firms’ marketing strategy”, International Entrepreneurship and Management Journal, Vol. 8 No. 2, pp. 203-222. Eggers, F., O’dwyer, M., Kraus, S., Vallaster, C. and Gu¨denberg, S. (2013), “The impact of brand authenticity on brand trust and SME growth: a CEO perspective”, Journal of World Business, Vol. 48 No. 3, pp. 340-348. European Commission (2003), “Economic and competitiveness analysis of the European textile and clothing sector in support of the communication”, The future of the textiles and clothing sector in the enlarged Europe, European Commission, Brussels, available at: http://ec.europa.eu/enterprise/sectors/textiles/files/sec2003_1345_en.pdf (accessed November 12, 2013). Fernandes, C.I., Ferreira, J.O.J. and Raposo, M.R.L. (2013), “Drivers to firm innovation and their effects on performance: an international comparison”, International Entrepreneurship and Management Journal, Vol. 9 No. 4, pp. 557-580. Franke, N., Gruber, M., Harhoff, D. and Henkel, J. (2008), “Venture capitalists’ evaluations of startup teams: trade-offs, knock-out criteria, and the impact of VC experience”, Entrepreneurship Theory and Practice, Vol. 32 No. 3, pp. 459-483. Fuller, T., Warren, L. and Argyle, P. (2008), “Sustaining entrepreneurial business: a complexity perspective on processes that produce emergent practice”, International Entrepreneurship and Management Journal, Vol. 4 No. 1, pp. 1-17. Galbraith, J.R. (1973), Designing Complex Organizations, Addison-Wesley, Reading, MA. Garnsey, E., Lorenzoni, G. and Ferriani, S. (2008), “Speciation through entrepreneurial spin-off: the Acorn-ARM story”, Research Policy, Vol. 37 No. 2, pp. 210-224.


George, G. (2005), “Slack resources and the performance of privately held firms”, Academy of Management Journal, Vol. 48 No. 4, pp. 661-676. George, G. and Bock, A.J. (2010), “The business model in practice and its implications for entrepreneurship research”, Entrepreneurship Theory and Practice, Vol. 35 No. 1, pp. 83-111. Graham, J.R., Hazarika, S. and Narasimhan, K. (2011), Financial Distress in the Great Depression, National Bureau of Economic Research, Cambridge, MA, available at: www.nber.org/ papers/w17388 (accessed November 12, 2013). Greve, H.R. (2003), “A behavioral theory of R&D expenditures and innovations: evidence from shipbuilding”, Academy of Management Journal, Vol. 46 No. 6, pp. 685-702. Grewal, R. and Tansuhaj, P. (2001), “Building organizational capabilities for managing economic crisis: the role of market orientation and strategic flexibility”, The Journal of Marketing, Vol. 65 No. 2, pp. 67-80. Grullon, G., Kanatas, G. and Kumar, P. (2006), “The impact of capital structure on advertising competition: an empirical study”, Journal of Business, Vol. 79 No. 6, pp. 3101-3124. Hambrick, D.C. (1983a), “An empirical typology of mature industrial-product environments”, Academy of management Journal, Vol. 26 No. 2, pp. 213-230. Hambrick, D.C. (1983b), “High profit strategies in mature capital goods industries: a contingency approach”, Academy of management Journal, Vol. 26 No. 4, pp. 687-707. Hambrick, D.C. and Lei, D. (1985), “Toward an empirical prioritization of contingency variables for business strategy”, Academy of Management Journal, Vol. 28 No. 4, pp. 763-788. Hambrick, D.C. and Macmillan, I.C. (1984), “Asset parsimony-managing assets to manage profits”, Sloan Management Review, Vol. 25 No. 2, pp. 67-74. Hanks, S.H., Watson, C.J., Jansen, E. and Chandler, G.N. (1993), “Tightening the life-cycle construct: a taxonomic study of growth stage configurations in high-technology organizations”, Entrepreneurship Theory and Practice, Vol. 18 No. 2, pp. 5-5. Harms, R., Kraus, S. and Schwarz, E. (2009), “The suitability of the configuration approach in entrepreneurship research”, Entrepreneurship and Regional Development, Vol. 21 No. 1, pp. 25-49. Hirschey, M. and Weygandt, J.J. (1985), “Amortization policy for advertising and research and development expenditures”, Journal of Accounting Research, Vol. 23 No. 1, pp. 326-335. Hitt, M.A., Keats, B.W. and Demarie, S.M. (1998), “Navigating in the new competitive landscape: building strategic flexibility and competitive advantage in the 21st century”, The Academy of Management Executive (1993-2005), Vol. 12 No. 4, pp. 22-42. Ho, Y.K., Keh, H.T. and Ong, J.M. (2005), “The effects of R&D and advertising on firm value: an examination of manufacturing and nonmanufacturing firms”, Engineering Management, IEEE, Vol. 52 No. 1, pp. 3-14. Ho, Y.K., Tjahjapranata, M. and Yap, C.M. (2006), “Size, leverage, concentration, and R&D investment in generating growth opportunities”, Journal of Business, Vol. 79 No. 2, pp. 851-876. Humphrey, J. and Schmitz, H. (2002), “How does insertion in global value chains affect upgrading in industrial clusters?”, Regional Studies, Vol. 36 No. 9, pp. 1017-1027. Huson, M.R., Malatesta, P.H. and Parrino, R. (2004), “Managerial succession and firm performance”, Journal of Financial Economics, Vol. 74 No. 2, pp. 237-275. Ireland, R.D., Hitt, M.A., Camp, S.M. and Sexton, D.L. (2001), “Integrating entrepreneurship and strategic management actions to create firm wealth”, The Academy of Management Executive, Vol. 15 No. 1, pp. 49-63. Istat Istituto Nazionale Di Statistica (2001), Censimento Generale dell’Industria e Servizi, Istat Istituto Nazionale Di Statistica, Roma.


MD 52,8


1512

Jensen, G.R., Solberg, D.P. and Zorn, T.S. (1992), “Simultaneous determination of insider ownership, debt, and dividend policies”, Journal of Financial and Quantitative Analysis, Vol. 27 No. 2, pp. 247-263. Jensen, M.C. and Meckling, W.H. (1976), “Theory of the firm: managerial behavior, agency costs and ownership structure”, Journal of Financial Economics, Vol. 3 No. 4, pp. 305-360. Jiang, B., Belohlav, J.A. and Young, S.T. (2007), “Outsourcing impact on manufacturing firms’ value: evidence from Japan”, Journal of Operations Management, Vol. 25 No. 4, pp. 885-900. Johnson, M.W., Christensen, C.M. and Kagermann, H. (2008), “Reinventing your business model”, Harvard Business Review, Vol. 86 No. 12, pp. 57-68. Kamber, T. (2002), “The brand manager’s dilemma: understanding how advertising expenditures affect sales growth during a recession”, The Journal of Brand Management, Vol. 10 No. 2, pp. 106-120. Keeley, B. and Love, P. (2010), From Crisis to Recovery, OECD, Paris. Kraus, S., Rigtering, J.C., Hughes, M. and Hosman, V. (2012), “Entrepreneurial orientation and the business performance of SMEs: a quantitative study from the Netherlands”, Review of Managerial Science, Vol. 6 No. 2, pp. 161-182. Lecraw, D.J. (1983), “Performance of transnational corporations in less developed countries”, Journal of International Business Studies, Vol. 14 No. 1, pp. 15-33. Lichtenthaler, U. (2007), “Hierarchical strategies and strategic fit in the keep-or-sell decision”, Management Decision, Vol. 45 No. 3, pp. 340-359. Lo, F.Y. (2013), “The dynamic adjustment of environment, strategy, structure, and resources on firm performance”, International Entrepreneurship and Management Journal, Vol. 9 No. 2, pp. 217-227. Long, M.S. and Malitz, I.B. (1985), Investment Patterns and Financial Leverage, University of Chicago Press, Chicago, IL. Lorsch, J.W. and Maciver, E. (1989), Pawns or Potentates: The Reality of America’s Corporate Boards, Harvard Business Press, Cambridge, MA. Lu, J.W. and Beamish, P.W. (2001), “The internationalization and performance of SMEs”, Strategic Management Journal, Vol. 22 Nos 6-7, pp. 565-586. Luo, X. and De Jong, P.J. (2012), “Does advertising spending really work? The intermediate role of analysts in the impact of advertising on firm value”, Journal of the Academy of Marketing Science, Vol. 40 No. 4, pp. 605-624. Mcalister, L., Srinivasan, R. and Kim, M.C. (2007), “Advertising, research and development, and systematic risk of the firm”, Journal of Marketing, Vol. 71 No. 1, pp. 35-48. Mahadevan, B. (2000), “Business models for internet-based e-commerce”, California Management Review, Vol. 42 No. 4, pp. 55-69. Mahmood, I.P. and Rufin, C. (2005), “Government’s dilemma: the role of government in imitation and innovation”, Academy of Management Review, Vol. 30 No. 2, pp. 338-360. Malerba, F., Nelson, R., Orsenigo, L. and Winter, S. (1999), “History-friendly’ models of industry evolution: the computer industry”, Industrial and corporate change, Vol. 8 No. 1, pp. 3-40. Markides, C. and Charitou, C.D. (2004), “Competing with dual business models: a contingency approach”, The Academy of Management Executive, Vol. 18 No. 3, pp. 22-36. Markides, C.C. (2008), Game-changing Strategies: How to Create New Market Space in Established Industries by Breaking the Rules, Jossey-Bass Inc. Pub., San Francisco, CA. Markides, C.C. (2013), “Business model innovation: What can the ambidexterity literature teach us?”, The Academy of Management Perspectives, Vol. 27 No. 4, pp. 313-323.


Mazzarol, T. and Reboud, S. (2006), “The strategic decision making of entrepreneurs within small high innovator firms”, International Entrepreneurship and Management Journal, Vol. 2 No. 2, pp. 261-280. Milgrom, P. and Roberts, J. (1990), “The economics of modern manufacturing: technology, strategy, and organization”, The American Economic Review, Vol. 80 No. 3, pp. 511-528. Miller, D. (2011), “Miller (1983), revisited: a reflection on EO research and some suggestions for the future”, Entrepreneurship Theory and Practice, Vol. 35 No. 5, pp. 873-894. Miller, D. and Friesen, P.H. (1983), “Strategy-making and environment: the third link”, Strategic Management Journal, Vol. 4 No. 3, pp. 221-235. Mintzberg, H. (1979), The Structuring of Organizations: A Synthesis of the Research, Prentice Hall, Englewood Cliffs, NJ. Mintzberg, H. (1990), “The design school: reconsidering the basic premises of strategic management”, Strategic Management Journal, Vol. 11 No. 3, pp. 171-195. Mitchelmore, S. and Rowley, J. (2013), “Growth and planning strategies within women-led SMEs”, Management Decision, Vol. 51 No. 1, pp. 83-96. Morris, M., Schindehutte, M. and Allen, J. (2005), “The entrepreneur’s business model: toward a unified perspective”, Journal of Business Research, Vol. 58 No. 6, pp. 726-735. Myers, S.C. (1977), “Determinants of corporate borrowing”, Journal of Financial Economics, Vol. 5 No. 2, pp. 147-175. Novak, S. and Stern, S. (2008), “How does outsourcing affect performance dynamics? Evidence from the automobile industry”, Management Science, Vol. 54 No. 12, pp. 1963-1979. OECD (2010), Economic Policy Reforms: Going for Growth 2010, OECD Publishing, Paris. Pal, R., Torstensson, H., Mattila, H. and Part, B. (2014), “Antecedents of organizational resilience in economic crises- an empirical study of Swedish textile and clothing SMEs”, International Journal of Production Economics, Vol. 147 No. Part B, pp. 410-428. Pearce, J.A. and Michael, S.C. (2006), “Strategies to prevent economic recessions from causing business failure”, Business Horizons, Vol. 49 No. 3, pp. 201-209. Pelham, A.M. (2000), “Market orientation and other potential influences on performance in small and medium-sized manufacturing firms”, Journal of Small Business Management, Vol. 38 No. 1, pp. 48-67. Perlow, L.A., Okhuysen, G.A. and Repenning, N.P. (2002), “The speed trap: exploring the relationship between decision making and temporal context”, Academy of Management Journal, Vol. 45 No. 5, pp. 931-955. Poppo, L. and Zenger, T. (1998), “Testing alternative theories of the firm: transaction cost, knowledge-based, and measurement explanations for make-or-buy decisions in information services”, Strategic Management Journal, Vol. 19 No. 9, pp. 853-877. Pozzana, R. (2011), “Modelli di business e competizione nelle piccole imprese manifatturiere”, in Paolazzi, R. and Trau`, F. (Eds), Effetti della Crisi, Materie Prime e Rilancio Manifatturiero, le Strategie di Sviluppo delle Imprese Italiane, Confindustria Centro Studi, Rome, available at: www.confindustria.it/indcong.nsf/0/86c88726d7d395fdc12578aa002 fdeb7/$FILE/Capitolo%204.pdf (accessed November 12, 2013). Purcarea, I., Benavides Espinosa, M.D.M. and Apetrei, A. (2013), “Innovation and knowledge creation: perspectives on the SMEs sector”, Management Decision, Vol. 51 No. 5, pp. 1096-1107. Qian, G. (2002), “Multinationality, product diversification, and profitability of emerging US smalland medium-sized enterprises”, Journal of Business Venturing, Vol. 17 No. 6, pp. 611-633. Salimath, M.S., Cullen, J.B. and Umesh, U. (2008), “Outsourcing and performance in entrepreneurial firms: contingent relationships with entrepreneurial configurations”, Decision Sciences, Vol. 39 No. 3, pp. 359-381.


MD 52,8


1514

Salomon, R. and Shaver, J.M. (2005), “Export and domestic sales: their interrelationship and determinants”, Strategic Management Journal, Vol. 26 No. 9, pp. 855-871. Scanlon, J. (2009), “Is recession the time to Boost Ad Spending?”, Business Week, Bloomberg, May 1, available at: www.businessweek.com/innovate/content/may2009/id2009051_ 889327.htm (accessed November 12, 2013). Shama, A. (1993), “Marketing strategies during recession: a comparison of small and large firms”, Journal of Small Business Management, Vol. 31 No. 3, pp. 62-72. Shrader, R.C. (2001), “Collaboration and performance in foreign markets: the case of young high-technology manufacturing firms”, Academy of management Journal, Vol. 44 No. 1, pp. 45-60. Soriano, D.R. and Dobon, S.R. (2009), “Linking globalization of entrepreneurship in small organizations”, Small Business Economics, Vol. 32 No. 3, pp. 233-239. Sosna, M., Trevinyo-Rodrı`guez, R.N. and Velamuri, S.R. (2010), “Business model innovation through trial-and-error learning: the Naturhouse case”, Long Range Planning, Vol. 43 No. 2, pp. 383-407. Srinivasan, R., Lilien, G.L. and Sridhar, S. (2011), “Should firms spend more on research and development and advertising during recessions?”, Journal of Marketing, Vol. 75 No. 3, pp. 49-65. Srinivasan, R., Rangaswamy, A. and Lilien, G.L. (2005), “Turning adversity into advantage: does proactive marketing during a recession pay off?”, International Journal of Research in Marketing, Vol. 22 No. 2, pp. 109-125. Steenkamp, J.B.E.M. and Fang, E.E. (2011), “The impact of economic contractions on the effectiveness of R&D and advertising: evidence from US companies spanning three decades”, Marketing Science, Vol. 30 No. 4, pp. 628-645. Teece, D.J. (2010), “Business models, business strategy and innovation”, Long Range Planning, Vol. 43 Nos 2-3, pp. 172-194. Tellis, G. and Tellis, K. (2009), “A critical review and synthesis of research on advertising in a recession”, Journal of Advertising Research, Vol. 49 No. 3, pp. 304-327. Thornhill, S. and Gellatly, G. (2005), “Intangible assets and entrepreneurial finance: the role of growth history and growth expectations”, The International Entrepreneurship and Management Journal, Vol. 1 No. 2, pp. 135-148. Thornhill, S., Gellatly, G. and Riding, A. (2004), “Growth history, knowledge intensity and capital structure in small firms”, Venture Capital, Vol. 6 No. 1, pp. 73-89. Tracey, P. and Jarvis, O. (2007), “Toward a theory of social venture franchising”, Entrepreneurship Theory and Practice, Vol. 31 No. 5, pp. 667-685. Trimi, S. and Berbegal-Mirabent, J. (2012), “Business model innovation in entrepreneurship”, International Entrepreneurship and Management Journal, Vol. 8 No. 4, pp. 449-465. Urata, S. and Kawai, H. (2002), “Technological progress by small and medium enterprises in Japan”, Small Business Economics, Vol. 18 No. 1, pp. 53-67. Valliere, D. (2010), “Reconceptualizing entrepreneurial framework conditions”, International Entrepreneurship and Management Journal, Vol. 6 No. 1, pp. 97-112. Van De Ven, A.H., Ganco, M. and Hinings, C. (2013), “Returning to the frontier of contingency theory of organizational and institutional designs”, The Academy of Management Annals, Vol. 7 No. 1, pp. 393-440. Villalonga, B. (2004), “Intangible resources, Tobin’s q, and sustainability of performance differences”, Journal of Economic Behavior & Organization, Vol. 54 No. 2, pp. 205-230. Voulgaris, F., Asteriou, D. and Agiomirgianakis, G. (2004), “Size and determinants of capital structure in the Greek manufacturing sector”, International Review of Applied Economics, Vol. 18 No. 2, pp. 247-262.


Wymenga, P., Spanikova, V., Derbyshire, J. and Barker, A. (2011), “Are EU SMEs recovering?”, Annual Report on EU SMEs 2010/2011, European Commission, Brussels. Zeithaml, V.A., Varadarajan, R.P. and Zeithaml, C.P. (1988), “The contingency approach: its foundations and relevance to theory building and research in marketing”, European Journal of Marketing, Vol. 22 No. 7, pp. 37-64. Zeynep Aksin, O. and Masini, A. (2008), “Effective strategies for internal outsourcing and offshoring of business services: an empirical investigation”, Journal of Operations Management, Vol. 26 No. 2, pp. 239-256. Zott, C. and Amit, R. (2007), “Business model design and the performance of entrepreneurial firms”, Organization Science, Vol. 18 No. 2, pp. 181-199. Zott, C. and Amit, R. (2008), “The fit between product market strategy and business model: implications for firm performance”, Strategic Management Journal, Vol. 29 No. 1, pp. 1-26. Zott, C., Amit, R. and Massa, L. (2011), “The business model: recent developments and future research”, Journal of Management, Vol. 37 No. 4, pp. 1019-1042. Corresponding author Assistant Professor Cristina Bettinelli can be contacted at: [email protected]

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