http://www.smallbusinessinstitute.biz A B S T R A C T Keywords: Journal of Small Business Strategy 2020, Vol. 30, No. 01, 31-42 ISSN: 1081-8510 (Print) 2380-1751 (Online) ©Copyright 2020 Small Business Institute® w w w. j s b s . o rg Introduction 1Cef.UP and Faculdade de Engenharia, Universidade do Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal, ndsoares@fe.up.pt 2NIPE and Escola de Economia e Gestão, Universidade do Minho, Campus de Gualtar, 4710-057 Braga, Portugal, mvalente@eeg.uminho.pt Exploring the relation between cultural values and R&D investment under the behavioral theory of the firm R&D intensity, Innovation, Cultural values, Behavioural theory of the firm APA Citation Information: Soares, N., & Valente, M. (2020). Exploring the relation between cultural values and R&D investment under the behavioral theory of the firm. Journal of Small Business Strategy, 30(1), 31-42. This research focuses on R&D investment by firms under the framework of the behavioral theory of the firm (Cyert & March, 1963). Firm managers have aspi- rations in relation to several of the firm’s characteristics either relative to past performance and decisions or to competitors’ performance and decisions. Additionally, the so-called slack within the firm creates leeway for an R&D decision. Previous empirical evidence has shown there is a link between these two characteristics of firms and R&D investment (e.g. Alessandri & Pattit, 2014; Chen & Miller, 2007; Guedes, da Conceição Gonçalves, Soares, & Valente., 2016; O’Brien & David, 2014). Current global competition motivates countries and firms to heavily invest in R&D and innovations as it is considered fundamental for obtaining a competitive ad- vantage and sustaining future growth (Desjardins, 2018; Eggers & Kaul, 2018). At the same time, there has been a decrease in the role of public funding for R&D reduced from 2009 to 2016 in the OECD area (OECD, 2018), which puts even more emphasis on private R&D and its determi- nants. While there is evidence on the role of organization- al settings and their impact on R&D investment (Driver & Guedes, 2012; Shaikh, O’Brien, & Peters, 2018) not much emphasis has been placed in understanding how underlying cultural values of each society impact the firm’s decision to undertake such an uncertain and risky investment, as is the case of R&D (Shinkle, 2012). In particular, O’Brien and David (2014) propose that the behavioral theory of the firm should be adapted in order to take into account cultural dif- ferences when exploring firm’s R&D investment decision. In this paper we put forward the hypothesis that the relation between slack and R&D investment decisions is influenced by cultural values prevalent in the country of origin of the firm and test this hypothesis using firm-lev- el data. The analysis is undertaken based on a sample of 104,431 firm-year observations of listed non-financial firms This paper explores the role cultural factors play in firms’ decisions to invest in Research and Development (R&D), under the be- havioural theory of the firm (Cyert & March, 1963). Based on a sample of non-financial firms from 23 countries for the pe- riod of 1990 to 2016 and two of the six Hofstede (1984) cultural dimensions, we observe that countries’ cultural values are sta- tistically significant at explaining differences in firms’ R&D decisions. On one hand, there is a negative relation between firms’ R&D investment decisions and countries’ uncertainty avoidance. On the other hand, a positive relation is found be- tween firms’ R&D investment decision and countries’ long-term orientation. Evidence is also found on the extent to which these cultural characteristics influence how firms’ aspirations in relation to performance discrepancies drive R&D investment. Nuno Soares1, Marieta Valente2 *The authors would like to thank the comments provided by the two anonymous reviewers, the special issue editor Andrea Rey-Martí, and the comments from the participants of the 2019 INEKA, Verona. This work was carried out within the funding by Portuguese public funds through FCT - Fundação para a Ciência e a Tecnologia, I.P., in the framework of the projects UID/ECO/04105/2019 and UID/ECO/03182/2019. The funding bodies had no involvement in the conduct of the research or preparation of the article. http://www.smallbusinessinstitute.biz http://www.jsbs.org 32 N. Soares, & M. Valente Journal of Small Business Strategy / Vol. 30, No. 1 (2020) / 31-42 from 23 countries for the period of 1990-2016, retrieved from Thomson Reuters Worldscope. To control for cul- tural values we use the classification and dataset of Hof- stede’s cultural dimensions at the country level (Hofstede, 2001). In particular we focus on two cultural dimensions deemed more pertinent to the study of R&D investment at the firm-level, namely attitudes towards uncertainty and orientation towards the long or the short term. When interacting those cultural dimensions with the firm’s as- pirations we observe that cultural values are statistically significant at explaining differences in firms’ behaviours. As such, the way firms are managed is conditioned by the cultural environment in which the firm operates. The main contribution in this paper is first to com- bine a country’s cultural values with the standard ap- proach as to how situational determinants (proposed by the behavioral theory of the firm) condition R&D invest- ment using a panel of firms from different countries. So far, in terms of research, only Lewellyn and Bao (2015) have combined both sets of variables to explore this re- lation in an international panel of firms. However, their study focuses on a specific sector (global paper prod- ucts industry) which limits the generalizability of results. Second, this paper provides further evidence as to the relevance of the behavioral theory of the firm by Cyert and March (1963) relative to managers’ decisions. This literature already acknowledges that managers do not fol- low neoclassical economic theory’s prediction of optimiz- ing behaviour. Instead managers look to firm or industry outcomes to form aspirations and then make local adjust- ments to their decisions in order to match those aspira- tions. Our paper provides supporting evidence from an in- ternational dataset of listed firms in the last three decades that aspirations are important determinants of R&D deci- sions in conjunction with the leeway provided by slack. Third, the empirical relevance of cultural dimensions in this paper further highlights how managerial decisions are indeed not context-free and should not be analysed abstracting from the underlying cultural environment. Ad- ditionally, Lewellyn and Bao (2015) argue that acknowl- edging the national cultural background of firms and man- agers can help explain inconsistent findings in the literature. In the next section we explore the literature on these two complementary determinants of firm’s R&D in- vestment and put forward several testable hypotheses. In section 3 we present the empirical study, namely how the different variables are implemented and present the model. Using regression analysis, in Section 4 we pres- ent the results and discuss implications in Section 5. Literature Review and Hypotheses The behavioral theory of the firm originally developed by Cyert and March (1963) understands managers behaviour as a response to self or social aspirations, either regarding the firm’s past behaviour or relative to the performance and choices of competitors. These behavioral aspirations can thus partially explain decisions made within the firms, con- ditioned by the level of slack resources within the firm. The study of how these aspirations impact decisions is enriched if cultural differences in backgrounds of firms and managers are taken into consideration (as put forward by Shinkle, 2012). The chosen approach in this paper is to combine firm-level data on financial information, to capture the theoretically relevant variables with country data on cultural dimensions. The Behavioral Theory of the Firm Cyert and March (1963)’s behavioral theory of the firm looks at managers’ behaviour as boundedly rational, and rationalizable through routines and local adjustments in decisions, such as problemistic and slack search. As for R&D investment, it can be perceived as potentially provid- ing a solution to performance below aspirations, but at the same time, it is a risky option with uncertain returns, and thus be overlooked as a solution for problemistic search. Within the framework of this theory, there is the as- sumption of problemistic search based on performance feedback and comparison with aspiration levels (Argote & Greve, 2007). Shinkle (2012, p. 416) defines organization aspirations as “desired performance levels in specific orga- nizational outcomes and have also been called goals and reference points”. Managers will define managerial aspira- tions levels based on self or industry performance, which will guide their choices, and in turn if performance is below/ above aspirations, managers will decrease/increase their as- pirations, respectively (Lant, 1992; Lant & Shapira, 2008). According to Posen, Keil, Kim, and Meissner (2018, p. 208) “a firm’s recognition of performance below its aspiration, which is the level of future performance deemed acceptable, leads to a process of search to discover a solution to the problem, resulting in behavioral change intended to restore performance to the aspired level”. In terms of R&D spend- ing, it is perceived in the literature as a form of managerial risk taking (e.g. Greve, 1998; Palmer & Wiseman, 1999). It is however not straightforward how an attainment discrepancy will motivate managers in terms of R&D de- cisions. When a firm is below its aspirations, problemistic search follows and it may include taking more risks, such as increasing R&D investments (as observed for example by Bromiley, 1991; Chen & Miller, 2007). The opposite argu- 33 N. Soares, & M. Valente Journal of Small Business Strategy / Vol. 30, No. 1 (2020) / 31-42 ment has also been observed for performance below aspira- tions (Nickel & Rodriguez, 2002) and specifically for the case where firm’s performance matches industry performance al- beit below self-aspirations (Lv, Chen, Zhu, & Lan, 2019). When firms perform above aspirations, the results are also ambiguous. From the behavioral theory of the firm, we understand that if a firm is successfully attaining its aspirations, it should continue replicating past routines. However empir- ical research show that is not always the case (Iyer & Mill- er, 2008; Labianca, Fairbank, Andrevski, & Parzen, 2009). In the present paper we model both self-aspirations based on historical firm data and social aspirations that come from comparisons with industry data as explanatory vari- ables for R&D investment. To define the first hypothesis, we follow the theoretical predictions of the behavioral theo- ry of the firm. Given that firm’s aspirations can be defined in relation to past self-performance or to industry peer perfor- mance, we can test Hypothesis 1 in these two dimensions. Hypothesis 1. R&D investment is positively related to performance below aspirations and independent from performance above aspirations, all else being equal. At the same time, the hypothesis of slack search implies that the slack within firms allows them lee- way to innovate. Cyert and March (1963) define or- ganization slack as the “difference between total re- sources and total necessary payments” (p. 42). As resources are available within the firm, slack search al- lows managers to promote innovation and invest in R&D. The majority or studies exploring slack search have opt- ed for quantifying organization slack using accounting mea- sures (one exception is for example Nohria and Gulati, (1996) who elicit perception of firm slack using questionnaires). Em- pirical results have however not been consensual in terms of the sign of the relation between slack and R&D investment, namely some studies have found a positive relation (Greve, 2003; Marlin & Geiger, 2015) or an inverse U-shaped rela- tion (e.g. Geiger & Cashen, 2002; Nohria & Gulati, 1996). Daniel, Lohrke, Fornaciari, and Turner Jr (2004) con- sider that one of the most commonly used empirical imple- mentation of the concept of slack is Bourgeois and Singh (1983)’s “ease-of-recovery” definition. Using accounting information on current assets relative to current liabili- ties, the authors defined the more easily accessible type of slack (available slack). Recoverable slack is less easily ac- cessible and relates selling, general and administrative ex- penses to sales. Finally, potential slack is the least easily recoverable (“the capital-raising potential represented by changes in stock price is just that—potential”, (Bourgeois & Singh, 1983, p. 43). The present paper focuses on the two more easily recoverable forms of slack (as for exam- ple Wiersma, 2017). Following from the behavioral the- ory of the firm, we put forward the following hypothesis: Hypothesis 2. R&D investment is positively relat- ed to firms’ slack resources, all else being equal. These two hypotheses derived from the behavioral theory of the firm have been tested jointly in the empiri- cal literature and partial supporting evidence has been found (e.g. Alessandri and Pattit 2014 for publicly traded U.S. manufacturing firms during 2001-2007; Chen and Miller 2007 for publicly traded U.S. manufacturing firms in the period 1998-2001; Guedes et al. 2016 for Lon- don Stock Exchange listed firms from 2009-2014). Our study enriches the empirical literature by using a panel of firms from several countries, which is then enriched with cultural variables, whose pertinence is discussed next. Cultural Dimensions and Management Decisions The national context in which a firm operates has an impact on management decisions, and this influence can op- erate through national cultural dimensions. Li, Griffin, Yue, and Zhao (2013, p. 1) argue that “even in a highly globalized world with sophisticated managers, culture matters” and Beckmann, Menkhoff, and Suto (2008) observe how asset managers’ views and behaviour are impacted by cultural dif- ferences. Aggarwal, Faccio, Guedhami, and Kwok (2016, p. 466) define culture as including “an enduring set of beliefs or values that influences individuals’ perceptions, preferences, decisions, and behaviors. It is therefore likely that culture in- fluences business and financial decisions”. In what concerns the focus of the present paper, i.e. R&D investment, Li et al. (2013) have concluded that culture indeed matters for cor- porate risk-taking as measured by the volatility of earnings and R&D investments. Lievenbrück and Schmid (2014) corroborate the result in the case of risk hedging by firms. Hofstede’s approach assumes that cultural differenc- es translate into different management styles and values (Hofstede, 1984). As such, managers when faced with the same objective financial conditions may make different decisions depending on cultural values in the home coun- try. One fundamental management decision concerns R&D investment, which involves judgments on uncertain and risky outcomes, and has a potential impact on profitabili- ty. We argue in this paper that the cultural contextual val- ues will influence the way managers decide about R&D under the framework of the behavioral theory of the firm. In terms of the six dimensions in Hofstede’s analysis, we focus on Uncertainty Avoidance (the corresponding variable is the Uncertainty Avoidance Index - UAI) and 34 N. Soares, & M. Valente Journal of Small Business Strategy / Vol. 30, No. 1 (2020) / 31-42 long-term orientation versus short-term normative orien- tation (for parsimony, Long Term Orientation - LTO). We argue that these cultural variables can impact the attitude to risk and investment within a firm, that is, when a firm is confronted with a discrepancy in its aspirations for a certain level of slack, the response may vary as a conse- quence of the cultural framework of the country. As such, these variables are likely to impact R&D investment. Hofstede (2001) defines UA as “the extent to which a culture programs its members to feel either uncomfort- able or comfortable in unstructured situations. Unstructured situations are novel, unknown, surprising, different from usual”. Since risky choices, such as R&D investment are often associated with uncertain firm outcomes (Palmer & Wiseman, 1999), we can expect that in countries with high levels of uncertainty avoidance, there is less R&D invest- ment, all else being equal. As argued by Li et al. (2013, p. 2), countries with low uncertainty avoidance, “val- ue innovation” and do not “shun ambiguous situations”. Hypothesis 3. R&D investment is negatively relat- ed to Uncertainty Avoidance (UA), all else being equal. As for the dimension of Long-term versus short-term orientation, according to Hofstede (2001, p. 15) it “refers to the extent to which a culture programs its members to ac- cept delayed gratification of their material, social, and emo- tional needs”. A long-term orientation is associated with “the fostering of virtues oriented toward future rewards—in particular, perseverance and thrift” (Hofstede, Hofstede, & Minkov, 2010, p. 239). A short-term orientation “stands for the fostering of virtues related to the past and present” (ibidem). The outcome of present R&D is uncertain and may only provide benefits in the future, so we can expect countries with a long-term orientation to be more accepting of R&D investments, all else being equal, as proposed in: Hypothesis 4. R&D investment is positively related to Long-Term Orientation (LTO), all else being equal. Method For the empirical analysis, data for the period of 1990- 2016 from all non-financial listed firms in 23 countries is retrieved from Thomson Reuters Worldscope. The origi- nal sample started with a selection of 51 countries which are often the focus of analysis of influential international studies (e.g. Covrig, Defond, & Hung, 2007; Leuz & Ver- recchia, 2000; Persakis & Iatridis, 2017). However, data requirements, as explained next, impose restrictions on which firms are included in the dataset and consequently lead to the loss of 28 countries. Firms are excluded from the sample if they have no R&D, if the ratio of R&D-to-sales is larger than one, or lack the needed data to calculate the variables used. Given the nature of some of the explanatory variables, it is required that for each combination of coun- try, year and two-digit SIC code there are at least 5 firms available and that each country has at least 100 observa- tions to be included in the final sample. Data for the coun- tries’ cultural dimensions is retrieved from Hofstede (2015). The final sample is comprised of 104,431 firm-year ob- servations. With the exception of the Hofstede dimensions, all continuous variables are winsorised at the top/bottom 1% to avoid the effect of outliers. Tables 1 and 2 provide a breakdown of the final sample by country and by industry, respectively. Table 1 Sample breakdown by country Countries N Percent Australia 601 0.58 Canada 1,963 1.88 China 4,281 4.1 Denmark 183 0.18 Finland 396 0.38 France 744 0.71 Germany 2,119 2.03 Greece 245 0.23 Hong Kong 1,370 1.31 Israel 1,034 0.99 Italy 159 0.15 Japan 30,460 29.17 Malaysia 216 0.21 Netherlands 215 0.21 Singapore 273 0.26 South Africa 184 0.18 South Korea 9,317 8.92 Sweden 768 0.74 Switzerland 993 0.95 Taiwan 11,823 11.32 Turkey 824 0.79 United Kingdom 4,708 4.51 United States 31,555 30.22 Total 104,431 100 As can be seen from Table 1, Japan and the United States dominate the sample, although there is a wide variety of countries, which allows for differences in cultural charac- 35 N. Soares, & M. Valente Journal of Small Business Strategy / Vol. 30, No. 1 (2020) / 31-42 teristics. From Table 2, we observe that firms in the Tech- nology Hardware & Equipment and Electronic & Electri- cal Equipment industries are the ones most represented in the sample. There is a clear increase in the R&D intensity during the sample period, which is present in almost all the sectors, but particularly in the technological and software sectors. This data is not reported here, but is available upon request. Table 2 Sample breakdown by industry Industries N Percent Aerospace & Defense 1,594 1.53 Alternative Energy 448 0.43 Automobiles & Parts 4,198 4.02 Beverages 511 0.49 Chemicals 7,427 7.11 Construction & Materials 6,340 6.07 Electricity 151 0.14 Electronic & Electrical Equipment 13,657 13.08 Fixed Line Telecommunications 167 0.16 Food & Drug Retailers 107 0.1 Food Producers 3,944 3.78 Forestry & Paper 676 0.65 Gas, Water & Multiutilities 299 0.29 General Industrials 2,085 2 General Retailers 577 0.55 Health Care Equipment & Services 5,514 5.28 Household Goods & Home Const. 2,873 2.75 Industrial Engineering 10,472 10.03 Industrial Metals & Mining 2,307 2.21 Industrial Transportation 245 0.23 Leisure Goods 2,814 2.69 Media 832 0.8 Mining 401 0.38 Mobile Telecommunications 196 0.19 Oil & Gas Producers 466 0.45 Oil Equipment & Services 563 0.54 Personal Goods 3,259 3.12 Pharmaceuticals & Biotechnology 5,256 5.03 Software & Computer Services 9,284 8.89 Support Services 2,330 2.23 Technology Hardware & Equipment 14,760 14.13 Tobacco 114 0.11 Travel & Leisure 564 0.54 Total 104,431 100 Next we present in detail how the dependent vari- able and independent variables were constructed from the data following options in the previous literature. Con- trol variables are included that are not discussed in the literature review, but are relevant to capture other effects that may be impacting the dependent variable. These controls include the distance from bankruptcy and indus- try effects, such as industry growth and R&D intensity. Dependent Variable Research intensity is used as the dependent variable and, following Chen and Miller (2007) and Cohen and Levin- thal (1989), is proxied by R&D divided by sales (rd_sales). Firm and Industry Discrepancy Regarding firm discrepancy (firm_discrepancy), we follow Chen and Miller (2007) and define firm discrepan- cy as the difference between the firm’s Return on Assets (ROA) relative to the previous year ROA. As for industry discrepancy (ind_discrepancy), it is defined as the differ- ence between the firm’s ROA relative to the median ROA in the 2-digit Standard Industrial Classification (SIC) industry in the specific country of the focal firm in the prior year. We further decompose the discrepancy measures into positive (pos_firm_discrepancy and pos_ind_discrepan- cy) and negative discrepancy variables (neg_firm_dis- crepancy and neg_ind_discrepancy), which are calcu- lated as the multiplication of a dummy variable which takes the value of 1 if the discrepancy measure exhibits a positive value, and 0 otherwise, with the relevant firm or industry discrepancy. Such approach allows the mod- el to capture an eventual asymmetry in the way firms re- spond to different discrepancy measures, in line with the ambiguous empirical evidence concerning these effects. Slack Measures Given that this study focuses on the role of internal slack on the manager’s decision of investing on R&D, only the available and recoverable slack measures are used, and we follow Bourgeois (1981) definition. Available slack (a_ slack) is defined as the current ratio and calculated as total current assets divided by total current liabilities, while re- coverable slack (rec_slack) is measured as the ratio of sell- ing, general and administrative expenses divided by total revenue. Both these measures are included independently in the estimated model as they capture different levels of slack that are at the managers’ disposal, where available slack is immediately available to the manager while recov- 36 N. Soares, & M. Valente Journal of Small Business Strategy / Vol. 30, No. 1 (2020) / 31-42 erable slack is harder for the manager to use (Greve, 2003). Hofstede Cultural Dimensions Regarding the Hofstede cultural dimensions vari- ables, both the Uncertainty Avoidance Index (UAI) and Long-Term Orientation Index (LTO) are used. As for the original scores in each of the cultural di- mensions, “they are always relative scores in which the lowest country is situated around zero and the highest around 100” (Hofstede, 1984, p. 84). For the UAI, a low index means a country with low uncertainty avoidance and a high value of 100 or slightly higher means the stron- gest uncertainty avoidance; the index for LTO means that the higher the value of the index the more long-term ori- ented the country is (Hofstede et al., 2010). Given the differences in the magnitude of these variables and the previous variables, UAI and LTO were scaled by 100. Data for the countries’ cultural dimensions is retrieved from Hofstede (2015). It has been argued that underly- ing national cultural features do not change over time in terms of countries’ relative positions (e.g. Beugelsdijk, Maseland, & van Hoorn, 2015; Inglehart & Baker, 2000). Distance from Bankruptcy A control variable that is often used to account for a firm’s financial situation is how far from bankruptcy the firm appears, by inspecting financial and accounting data. This is used as a control by for example Chen and Miller (2007) and Guedes et al. (2016). The distance from bankruptcy (zscore) is proxied by the Altman’s (1968) z-score and calculated as: 𝑧𝑧𝑧𝑧𝑧𝑧𝑧𝑧𝑧𝑧𝑧𝑧 = 1.2 𝑤𝑤𝑧𝑧𝑧𝑧𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤 𝑧𝑧𝑐𝑐𝑐𝑐𝑤𝑤𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑧𝑧𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑧𝑧𝑧𝑧𝑧𝑧𝑐𝑐𝑧𝑧 + 1.4 𝑧𝑧𝑧𝑧𝑐𝑐𝑐𝑐𝑤𝑤𝑤𝑤𝑧𝑧𝑟𝑟 𝑧𝑧𝑐𝑐𝑧𝑧𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑤𝑧𝑧 𝑐𝑐𝑧𝑧𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑧𝑧𝑧𝑧𝑧𝑧𝑐𝑐𝑧𝑧 + 3.3 𝑤𝑤𝑤𝑤𝑧𝑧𝑧𝑧𝑖𝑖𝑧𝑧 𝑏𝑏𝑧𝑧𝑏𝑏𝑧𝑧𝑧𝑧𝑧𝑧 𝑤𝑤𝑤𝑤𝑐𝑐𝑧𝑧𝑧𝑧𝑧𝑧𝑧𝑧𝑐𝑐 𝑧𝑧𝑒𝑒𝑐𝑐𝑧𝑧𝑤𝑤𝑧𝑧𝑧𝑧 𝑐𝑐𝑤𝑤𝑟𝑟 𝑐𝑐𝑐𝑐𝑒𝑒𝑧𝑧𝑧𝑧 𝑐𝑐𝑧𝑧𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑧𝑧𝑧𝑧𝑧𝑧𝑐𝑐𝑧𝑧 + 0.6 𝑖𝑖𝑐𝑐𝑧𝑧𝑤𝑤𝑧𝑧𝑐𝑐 𝑧𝑧𝑐𝑐𝑐𝑐𝑤𝑤𝑐𝑐𝑐𝑐𝑐𝑐𝑤𝑤𝑧𝑧𝑐𝑐𝑐𝑐𝑤𝑤𝑧𝑧𝑤𝑤 𝑐𝑐𝑧𝑧𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑤𝑤𝑐𝑐𝑏𝑏𝑤𝑤𝑐𝑐𝑤𝑤𝑐𝑐𝑙𝑙 + 1 𝑐𝑐𝑧𝑧𝑐𝑐𝑐𝑐𝑐𝑐 𝑧𝑧𝑧𝑧𝑟𝑟𝑧𝑧𝑤𝑤𝑟𝑟𝑧𝑧 𝑐𝑐𝑧𝑧𝑐𝑐𝑐𝑐𝑐𝑐 𝑐𝑐𝑧𝑧𝑧𝑧𝑧𝑧𝑐𝑐𝑧𝑧 (1) Industry Effects Following Chen and Miller (2007), contemporaneous industry search intensity (ind_rd_sales) is included in the model as a control variable and is calculated as the mean R&D-to-sales in the 2-digit SIC industry in the specific country for the year. In addition, industry sales growth (ind_ growth) is also included in the model and is calculated as the change in total sales in the 2-digit SIC industry in the spe- cific country for the year, from the past to the current year. Model The testing of the hypotheses is done by estimating the following main models: 𝑟𝑟𝑟𝑟_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡 = 𝛽𝛽1 + 𝛽𝛽2𝑖𝑖𝑖𝑖𝑟𝑟_𝑔𝑔𝑟𝑟𝑔𝑔𝑔𝑔𝑔𝑔ℎ𝑖𝑖,𝑡𝑡 + 𝛽𝛽3𝑖𝑖𝑖𝑖𝑟𝑟_𝑟𝑟𝑟𝑟_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡 + 𝛽𝛽4𝑠𝑠_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽5𝑟𝑟𝑠𝑠𝑠𝑠_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽6𝑧𝑧𝑠𝑠𝑠𝑠𝑔𝑔𝑟𝑟𝑠𝑠𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽7𝑝𝑝𝑔𝑔𝑠𝑠_𝑓𝑓𝑖𝑖𝑟𝑟𝑓𝑓_𝑟𝑟𝑖𝑖𝑠𝑠𝑠𝑠𝑟𝑟𝑠𝑠𝑝𝑝𝑠𝑠𝑖𝑖𝑠𝑠𝑑𝑑𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽8𝑖𝑖𝑠𝑠𝑔𝑔_𝑓𝑓𝑖𝑖𝑟𝑟𝑓𝑓_𝑟𝑟𝑖𝑖𝑠𝑠𝑠𝑠𝑟𝑟𝑠𝑠𝑝𝑝𝑠𝑠𝑖𝑖𝑠𝑠𝑑𝑑𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽9𝑈𝑈𝑈𝑈𝑈𝑈 + 𝛽𝛽10𝐿𝐿𝐿𝐿𝐿𝐿 + 𝜀𝜀𝑖𝑖,𝑡𝑡 (2) 𝑟𝑟𝑟𝑟_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡 = 𝛽𝛽1 + 𝛽𝛽2𝑖𝑖𝑖𝑖𝑟𝑟_𝑔𝑔𝑟𝑟𝑔𝑔𝑔𝑔𝑔𝑔ℎ𝑖𝑖,𝑡𝑡 + 𝛽𝛽3𝑖𝑖𝑖𝑖𝑟𝑟_𝑟𝑟𝑟𝑟_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡 + 𝛽𝛽4𝑠𝑠_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽5𝑟𝑟𝑠𝑠𝑠𝑠_𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽6𝑧𝑧𝑠𝑠𝑠𝑠𝑔𝑔𝑟𝑟𝑠𝑠𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽11𝑝𝑝𝑔𝑔𝑠𝑠_𝑖𝑖𝑖𝑖𝑟𝑟_𝑟𝑟𝑖𝑖𝑠𝑠𝑠𝑠𝑟𝑟𝑠𝑠𝑝𝑝𝑠𝑠𝑖𝑖𝑠𝑠𝑑𝑑𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽12𝑖𝑖𝑠𝑠𝑔𝑔_𝑖𝑖𝑖𝑖𝑟𝑟_𝑟𝑟𝑖𝑖𝑠𝑠𝑠𝑠𝑟𝑟𝑠𝑠𝑝𝑝𝑠𝑠𝑖𝑖𝑠𝑠𝑑𝑑𝑖𝑖,𝑡𝑡−1 + 𝛽𝛽9𝑈𝑈𝑈𝑈𝑈𝑈 + 𝛽𝛽10𝐿𝐿𝐿𝐿𝐿𝐿 + 𝜀𝜀𝑖𝑖,𝑡𝑡 (3) The previous models only differ on the use of firm or industry discrepancies as capturing managers’ aspi- rations, based on the work by Chen and Miller (2007). Following the findings by Petersen (2009), Gow Ormaz- abal and Taylor (2010) and Thompson (2011), and giv- en the panel data nature of the sample, these are estimat- ed using firm and year clustered standard errors. Table 3 presents the descriptive statistics and correlation coef- ficients for the variables used in the empirical analysis. Results We use panel regression analysis of R&D to sales for the panel of firms described above. Table 4 presents the re- sults when the aspirations are defined relative to past perfor- mance of the firm (firm discrepancy) and Table 5 considers peer effects with the performance discrepancy calculated in relation to the median of the industry (industry discrepancy). In both Tables 4 and 5, column (1) tests the hy- potheses concerning the behavioral theory of the firm in isolation. As postulated by the behavioral theo- ry of the firm, the net impact of lagged measures of slack within the firm is positive and statistically signif- icant, corroborating Hypothesis 2 that the more slack re- sources, the more leeway firms have to invest in R&D. As for aspirations, both firm discrepancy and industry discrepancy have statistically significant coefficients, cor- roborating the prediction that firms indeed react to aspira- tion levels. Firms that exhibit a positive discrepancy, i.e. have performance above aspirations, tend to invest more in R&D, which is contrary to Hypothesis 1 (that follows from the behavioral theory of the firm, whereby firms would not change their decision). For firms that exhibit a nega- tive discrepancy, the coefficient is negative: the closer they 37 N. Soares, & M. Valente Journal of Small Business Strategy / Vol. 30, No. 1 (2020) / 31-42 Table 3 Descriptive statistics and Pearson correlations Panel A: Descriptive Statistics Obs Mean SD Min Max rd_salest 104,431 0.057 0.082 0.000 0.458 ind_growtht 104,431 0.071 0.154 -0.281 0.777 ind_rd_salest 104,431 0.043 0.036 0.002 0.140 a_slackt-1 104,431 2.436 1.978 0.414 12.336 rec_slack t-1 104,431 0.282 0.244 0.030 1.450 zscore t-1 104,431 0.037 0.057 -0.191 0.328 pos_firm_disc t-1 104,431 0.032 0.082 0.000 0.549 neg_firm_disc t-1 104,431 -0.034 0.078 -0.503 0.000 pos_ind_disc t-1 104,431 0.032 0.058 0.000 0.315 neg_ind_disc t-1 104,431 -0.049 0.123 -0.805 0.000 UAI 104,431 0.656 0.226 0.080 1.120 LTO 104,431 0.652 0.300 0.212 1.000 Panel B: Pearson correlations 1 2 3 4 5 6 7 8 9 10 11 12 1 rd_sales t 1 2 ind_growth t -0.031 1 3 ind_rd_sales t 0.515 -0.092 1 4 a_slack t-1 0.295 0.008 0.198 1 5 rec_slack t-1 0.689 -0.041 0.498 0.210 1 6 zscore t-1 0.060 0.056 0.074 0.519 -0.075 1 7 pos_firm_disc t-1 0.187 0.027 0.162 0.032 0.221 -0.046 1 8 neg_firm_disc t-1 -0.246 0.017 -0.150 -0.045 -0.328 0.137 0.169 1 9 pos_ind_disc t-1 0.080 0.048 0.176 0.201 0.002 0.368 0.306 0.104 1 10 neg_ind_disc t-1 -0.364 0.018 -0.184 0.037 -0.575 0.333 -0.113 0.603 0.221 1 11 UAI -0.284 -0.079 -0.295 -0.139 -0.290 -0.118 -0.147 0.156 -0.184 0.182 1 12 LTO -0.355 0.096 -0.455 -0.151 -0.402 -0.043 -0.181 0.176 -0.188 0.216 0.706 1 Notes: all correlations statistically significant at a 1% level. 38 N. Soares, & M. Valente Journal of Small Business Strategy / Vol. 30, No. 1 (2020) / 31-42 Table 4 Firm discrepancy results Variables (1) (2) (3) ind_growth t 0.004 0.001 0.000 (0.004) (0.004) (0.004) ind_rd_sales t 0.466*** 0.462*** 0.461*** (0.020) (0.021) (0.020) a_slack t-1 0.005*** 0.005*** 0.005*** (0.000) (0.000) (0.000) rec_slack t-1 0.180*** 0.179*** 0.179*** (0.004) (0.004) (0.004) Zscore t-1 0.039** 0.030* 0.032* (0.015) (0.016) (0.016) pos_firm_disc t-1 0.038*** 0.035*** 0.020 (0.007) (0.007) (0.016) neg_firm_disc t-1 -0.044*** -0.040*** -0.037 (0.010) (0.010) (0.023) UAI -0.020*** -0.024*** (0.003) (0.003) LTO 0.007*** 0.012*** (0.003) (0.002) UAI*pos_firm_disc t-1 -0.065** (0.027) UAI*neg_firm_disc t-1 0.073** (0.033) LTO*pos_firm_disc t-1 0.082** (0.036) LTO*neg_firm_disc t-1 -0.067 (0.044) Constant -0.031*** -0.021*** -0.021*** (0.001) (0.003) (0.003) Observations 104,431 104,431 104,431 R-squared 0.534 0.536 0.536 Notes: Two-way (firm and year) clustered standard errors estimation used. Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1. Table 5 Industry discrepancy results Variables (1) (2) (3) ind_growth t 0.005 0.001 0.001 (0.005) (0.005) (0.005) ind_rd_sales t 0.473*** 0.466*** 0.468*** (0.020) (0.021) (0.021) a_slack t-1 0.005*** 0.005*** 0.005*** (0.000) (0.000) (0.000) rec_slack t-1 0.181*** 0.180*** 0.180*** (0.004) (0.004) (0.004) Zscore t-1 0.033** 0.024 0.025 (0.016) (0.017) (0.017) pos_ind_disc t-1 0.020** 0.014* -0.037* (0.008) (0.008) (0.022) neg_ind_disc t-1 -0.020*** -0.016** -0.012 (0.007) (0.007) (0.021) UAI -0.021*** -0.024*** (0.003) (0.003) LTO 0.007*** 0.007*** (0.003) (0.003) UAI*pos_ind_disc t-1 0.010 (0.028) UAI*neg_ind_disc t-1 0.023 (0.032) LTO*pos_ind_disc t-1 0.082** (0.037) LTO*neg_ind_disc t-1 -0.025 (0.048) Constant -0.030*** -0.019*** -0.018*** (0.001) (0.002) (0.003) Observations 104,431 104,431 104,431 R-squared 0.532 0.534 0.534 Notes: Two-way (firm and year) clustered standard errors estimation used. Standard errors in parentheses. ***p < 0.01, **p < 0.05, *p < 0.1. 39 N. Soares, & M. Valente Journal of Small Business Strategy / Vol. 30, No. 1 (2020) / 31-42 are to matching their aspiration (that is, lower discrepan- cy in absolute terms), the less R&D investment is made, whereas the further away they are (that is, higher discrep- ancy in absolute terms), the more R&D investment is made, all else being equal. This result for firms with negative discrepancy corroborates Hypothesis 1, whereby R&D investment allows firm to address problemistic search. For these regressions, the control of industry R&D in- tensity is positive statistically significant, which is expected that in more R&D intensive sectors, a firm exhibits more R&D intensity. A higher zscore of the firm, proxying more distance from bankruptcy, yields more R&D investment. In column (2) we add the cultural dimension vari- ables of uncertainty avoidance and long-term orienta- tion. The coefficients for UAI are negative in both tables and statistically significant as hypothesized in Hypoth- esis 3. Controlling for the other variables in the model, the more a country exhibits uncertainty avoidance, the lower the investment in R&D a firm in that country will undertake. Hypothesis 4 proposes a positive relation be- tween LTO and R&D intensity and this is corroborated in the data, whereby a firm in a country with an outlook more towards the future engages more in R&D investment. In column (3) we enrich the model and in- clude interactions of the cultural dimensions and the firm and industry discrepancy in each table respec- tively. We hypothesize that being above or below as- pirations interacts with the country’s UAI and LTO. In Table 4 for firm discrepancy results, the coeffi- cients on the discrepancy dummy variables are no longer statistically significant. It should be noted that in column (1), the coefficients were statistically significant, although the direction of the relation was not fully consistent with the theoretical predictions. For a firm performing above self-aspirations, the higher the UAI, the lower the R&D investment (in fact the coefficient of the interaction am- plifies the negative relation found for the UAI variable). For a firm below aspirations, the coefficient on the inter- action is positive, which in conjunction with the coefficient for UAI alone dampens the negative effect of uncertainty avoidance. So, when the performance is poor relative to the benchmark (in this case the firm’s own performance) the dissuading effect of uncertainty avoidance is less strong. Concerning LTO, the coefficient on the level variable is positive and the effect is amplified when firms are above aspirations. There is however no incremental effect for below aspirations firms. So a firm in a beneficial position compared to past performance will further its investment in R&D, the more the country is oriented towards the long term. The results are similar in terms of direction in Table 5 – column (3), where the discrepancy is in relation to in- dustry median performance. Only the coefficient for the positive industry discrepancy is statistically significant and negative in column (3). In terms of the interaction be- tween the relative aspirations position of the firm and the cultural dimensions, only LTO for a positive industry dis- crepancy is statistically significant and positive, generating a positive effect from the higher long-term orientation on R&D investment. In the model without interaction, again the coefficients on UAI and LTO are respectively neg- ative and positive as postulated by Hypotheses 3 and 4. Discussion and Conclusion We explore firm-level data to provide further evidence that cultural values at the country level influence firms’ R&D choices. Using the framework of the behavioral the- ory of the firm, we explore the situational determinants of R&D investment, namely aspirations and slack within the firm, but interact these variables with the cultural values of Hofstede of uncertainty avoidance and long-term orienta- tion. Our results show consistent evidence for cultural di- mensions impacting R&D search activities of the firms in the sample. This paper adds to the literature on R&D and innovation by using a panel of firms across different coun- tries and expanding the approach of the behavioral theory of the firm to account for cultural differences between the countries. Given the importance that innovation can play in a country’s future development, these results are con- sequential to designing country specific R&D promotion policies. This should be done acknowledging that slack re- sources and aspirational levels condition managerial deci- sions, but also that the cultural context of the country in its outlook towards uncertainty and the future also play a role. Our study is to our knowledge the first that uses a panel of firms from different countries to combine the framework of the behavioral theory of the firm, acknowledging satis- ficing choices from managers in terms of R&D investment in response to aspirations and availability of slack, and the role of cultural dimensions, to better understand this re- lation. A previous paper by Lewellyn and Bao (2015) ex- plored this relation but focused on a single sector, making their conclusions sector-specific and limited. We enrich the literature by extending the analysis across sectors. The results show how countries cultural characteristics impact R&D investment across most economic activity sectors. Additionally, as briefly presented in the literature over- view, the evidence has not been consistent in supporting the direction of impact of aspirations on managerial decisions. Aspirations measured in relation to past performance of the firm or of peers have been found to consistently mat- ter for decisions such as R&D investment, however the 40 N. Soares, & M. Valente Journal of Small Business Strategy / Vol. 30, No. 1 (2020) / 31-42 behavioral theory suggests that performance above aspi- rations should not catalyse changes in behaviour, whereas performance below aspirations, should create problemis- tic search and increase R&D investment. This paper adds to the literature partially contradicting these predictions when cultural variables are not considered. When these discrepancies in aspirations are interacted with the cul- tural dimensions, the direction of change is impacted, al- beit not necessarily towards the theoretical predictions. The research approach presented in this paper can thus further clarify in what circumstances behavioral deter- minants impact managerial decisions. There are nonethe- less limitations which can be explored in future research. By focusing just on listed firms, which are normally just a subset of all the firms in every country and which is likely to exclude smaller firms, we do not consider how non-list- ed firms make their R&D investment decisions. It should however be noted that previous research has documented many idiosyncrasies of small and medium firms relative to larger firms (e.g., Lumpkin, McKelvie, Gras, & Nason, 2010; Marom, Lussier, & Sonfield, 2019). It would thus be relevant to extend the analysis to more directly account for those specificities, as well as the nature of ownership, namely family vs. non-family (e.g., Ahluwalia, Mahto, & Walsh, 2017; Bendickson, Davis, Cowden, & Liguori, 2015; Campbell, Line, Runyan, & Swinney, 2010; Chris- man & Patel, 2012). 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