http://www.smallbusinessinstitute.biz A B S T R A C T Keywords: Journal of Small Business Strategy 2019, Vol. 29, No. 02, 1-21 ISSN: 1081-8510 (Print) 2380-1751 (Online) ©Copyright 2019 Small Business Institute® w w w. j s b s . o rg Research generally shows a strong association be- tween entrepreneurial orientation (EO) and firm per- formance, and this relationship has been found to hold across multiple operationalizations of the construct, as well as a number of cultural settings (see, Rauch, Wiklund, Lumpkin, & Frese, 2009). The true value in EO is the creation of strategy through which innovative- ness, risk-taking, and proactiveness (Lumpkin & Dess, 1996; Wiklund, 1998), are consistently communicated and manifest to innovative behaviors (Kang, Matusik, Kim & Phillips, 2016; Pett & Wolff, 2016) or outputs (e.g., Shan, Song, & Ju, 2016; Wang & Juan, 2016). In fact, higher levels of EO are consistently associated with the firm’s ability to find leverageable new oppor- tunities via the creation of innovative solutions; thus, providing a competitive advantage and superior per- formance effects (Ahluwalia, Mahto, & Walsh, 2017; Aloulou & Fayolle, 2005; Forés & Camisón, 2016; Ire- land, Hitt, & Sirmon, 2003; McDowell, Peake, Coder, & Harris, 2018). As such, both EO (Covin & Slevin, 1991; Wiklund & Shepherd, 2003, 2005) and individ- ual entrepreneurial orientation (IEO) (Bolton & Lane, 2012), when proxying strategic priority have been shown to influence reported innovation levels, or inno- vation output within small firms (Avlonitis & Salavou, 2007; Bolton, Peake, & Coder, 2017). Based on the initial work of Covin and Slevin (1986, 1989) Lumpkin and Dess (1996) and Miller (1983), EO is generally viewed at the organizational level as “the entrepreneurial strategy-making process- es that key decision makers use to enact their firm’s organizational purpose, sustain its vision, and cre- ate competitive advantage(s)” (Rauch et al., 2009, p. 763). Since the strategic direction of small firms of- Introduction Whitney O. Peake1, Dennis Barber III2,, Amy McMillan3, Dawn L. Bolton4, Leanne Coder5 1Western Kentucky University, USA, whitney.peake@wku.edu 2East Carolina University, USA, barberde17@ecu.edu 3East Carolina University, USA, mcmillana@ecu.edu 4Western Kentucky University, USA, dawn.bolton@wku.edu 5Western Kentucky University. USA, leanne.coder@wku.edu Do management control systems stifle innovation in small firms? A mediation approach Small business, Management control, Innovation, Accounting How entrepreneurial orientation (EO) as a strategy manifests into entrepreneurial behaviors like innovation, is an important research topic but not well understood. There is a gap in the examination of EO and entrepreneurial behavioral outcomes. Since mediators exist (see Rauch, Wiklund, Lumpkin, & Frese, 2009; Wales, 2016; Wales, Patel, Parida, & Kreiser, 2013) additional research is needed to uncover these potential relationships. Research suggests that management controls systems (MCS) may serve as a mediator between strategy and innovation outcomes. There is, however, conflicting evidence regarding the impact and use of management control systems (MCS) in the small firm context. As such, we examine the relationship between an individual-level measure of EO (IEO) and innovation level and explore the mediating role of financial and nonfinancial MCS on that relationship. Results suggest that nonfinancial MCS partially mediate the relationship between IEO and innovation, while financial MCS do not. APA Citation Information: Peake, W. O., Barber III, D., McMillan, A., Bolton, D. L., Coder, L. (2019). Do management control systems stifle innovation in small firms? A mediation approach. Journal of Small Business Strategy, 29(2), 1-21. http://www.smallbusinessinstitute.biz http://www.jsbs.org 2 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 ten depends on the values and priorities of the owner (Dickson & Weaver, 2008), IEO has been a valuable construct in bringing EO from a firm level phenome- non to the individual level (Bolton & Lane, 2012; Gok- tan & Gupta, 2015). While research examining EO and innovation as an output of the firm stemming from in- novativeness as a strategic priority, has been conduct- ed (e.g. Anderson & Eshima, 2013; Atuahene-Gima & Ko, 2001; Tang, Chen, & Jin, 2015) there is still oppor- tunity to examine these constructs both in terms of IEO and within the small firm context. This is particularly salient since innovation within small firms generally links to superior performance (McDowell et al., 2018), yet the linking mechanisms between entrepreneurial strategy and translating that strategy into innovation activity merits further exploration. There are likely important mediating mechanisms to this relationship since innovation is a process facil- itated by priorities, behaviors, and organizational pro- cesses within the firm (Lumpkin & Dess, 1996). Man- agement control systems, when considered as “formal, information-based routines and procedures managers use to maintain or alter patterns in organizational activ- ities” (Simons, 1995, p. 5), have been argued to serve as an important facilitator between the strategy of the firm and innovation outcomes (Davila, Foster, & Oyon, 2009). These systems were traditionally more oriented towards accounting systems but have morphed to in- clude a broader variety of activities (Otley, 2016). The role of MCS in promoting or stifling innovation within small and/or start-up firms is a source of debate and is argued to be context-dependent. Early research on MCS suggest that accounting and control are purely a hindrance to innovation (Ama- bile, 1998) since MCS may lead to overly detailed, bu- reaucratic processes, which suppress innovation (Da- vila et al., 2009). Such research suggests that informal controls based in social norms tend to be more effec- tive in small businesses (e.g., Abernethy & Brownell, 1997; Abernethy & Lillis, 1995; Ouchi, 1979). More recently, empirical evidence suggests there is evidence counter to these decades-old assumptions (Bisbe & Otley, 2004; Henri, 2006; Li, Li, Liu, & Wang, 2005; Sandino, 2007) since MCS provide processes that codify and disseminate information, streamline inno- vation processes, and lend accountability for relevant employees and across departments (e.g., Barringer & Bluedorn, 1999; Davila et al., 2009; Greiner, 1972); thus improving innovation levels. Additionally, Davila et al. (2009) suggest that innovation level is contingent upon owner/manager characteristics and priorities, firm characteristics, and the level of management control implemented within the firm. Further, when considered from a contingen- cy theory perspective, Davila et al. (2009) suggest that perhaps innovation levels may differ across entrepre- neurial and/or small firms based on these preceding factors, indicating the need for more analysis focused on this area. Studies related to MCS have traditionally focused on established, larger firms embedded in sta- ble contexts (Davila et al., 2009); however, with the importance of small businesses to the global economy, the field is remiss to overlook MCS adopted by or em- bedded within smaller firms by the owner/manager. Small firms must be flexible, agile, and innovative but also organized to codify information and streamline processes. Further, the founders’ psychological frame likely impacts adoption of management controls as op- posed to the more informal forms of control. The small amount of prior research integrating MCS into this discussion provides inconclusive evi- dence for whether such controls benefit or harm the innovation level within small, entrepreneurial firms, and for how owner/manager characteristics may influ- ence the implementation of such controls. Part of this divergence in the literature may be due to the theory basis used to explore such efforts (Davila et al., 2009). Since universal solutions to strategy implementation and control for small businesses is unavailable, con- tingency theory may provide an updated and useful lens for examining these phenomena. In particular, contingency theory has been studied and applied in a variety of contexts including large and small firms for 60 years (Lawrence & Lorsch, 1967; Thompson, 1967; Tosi & Slocum, 1984; Woodward, 1965). It is based on the idea that fit between organizational structural variables, such as formalization, decentralization, etc. and contextual variables, such as technology, individ- ual predispositions, the external environment, culture, etc. are critical to organizational success. We examine four mediator models couched in contingency theory for 185 small firms in the South- eastern United States. We first explore the role of fi- nancial management control mechanisms as a media- 3 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 tor between the IEO level of the firm’s owner/manager and the different innovation approaches reported for the firm. We then follow with an exploration of the mediating role (or lack thereof) of non-financial, pri- marily human resources oriented, management con- trols on the relationship between owner/manager IEO and reported innovation levels for the firm. Our results suggest that although higher levels of IEO are signifi- cantly associated with greater levels of management control, non-financial controls, (i.e., human resourc- es-oriented controls) do partially mediate the relation- ship between IEO and different innovation approaches. Given these results, it appears that investments in man- agement control of personnel issues plays a substantial and important role in facilitating innovative activity within the firm. The remainder of the paper is organized as fol- lows. In the subsequent section, we provide a theoret- ical basis and develop hypotheses for analysis. Then, we outline the methods employed in our analyses, fol- lowed by presentation of the results. Finally, we con- clude with a discussion, outlining both academic and practical implications of this research, as well as lim- itations and opportunities for future research. Theory And Hypothesis Development Contingency Theory Contingency theory has been one of the most wide- ly utilized theories in organizational research, dating back to the 1960s. The fundamental assumption is that there is no one best way to manage an organization. According to Tosi and Slocum (1984), organizational outcomes are the consequence of the fit between sev- eral structural and contextual variables. Van de Ven, Ganco, and Hinings provide a critical examination of contingency theory and state “contingency theory pro- poses that performance outcomes of an organizational unit are a result of the fit between the unit’s external context and internal arrangements” (Van de Ven et al. 2013, p. 394). Contingency theory has been one of the dominant theories in management control research for many years. In particular, research suggests that there is not one universally appropriate control system that works in every situation. In fact, control systems must be carefully and uniquely aligned with other organiza- tional factors (Fisher, 1995). The theory has also been used to explain various relationships involving inno- vation (Fernandes & Solimun, 2017; Huang, 2009; Teasley & Robinson, 2005). Chen, Liu, and Cheung (2014) use contingency theory in their examination of managerial ties, radical innovation, and market forces. In particular, they produce a model that suggests mana- gerial ties have a positive impact on radical innovation and that market forces may have a positive or negative effect on these relationships. Van de Ven, et al. (2013) suggest that technology (i.e. innovation), in particular, is a critical boundary that needs further examination under the contingency theory lens. Finally, while contingency theory has mainly fo- cused on large organizations, it has been applied in various contexts to small businesses. “Complex relationships exist among environmen- tal, organizational, and individual/group variables, and these relationships and their salience change with the strategic and organizational design choic- es made by members of the dominant coalition” (Tosi & Slocum, 1984, p. 9). For small businesses, the dominant coalition is usually the small business owner, and as stated earlier, individ- ual predispositions are a contextual variable that are often included in contingency theory-based research. In particular, the IEO of the small business owner like- ly impacts the innovation levels of the firm. As such, we suggest that contingency theory is most appropriate when examining the relationship between IEO, mana- gerial control systems, and innovation. Small Business Context Although there is much research on innovation and small businesses, there is a noticeable gap specifi- cally examining variance in incremental versus radical innovation among small businesses as well as the ef- fect of control systems on small business innovation. Innovation in general has been shown to have a pos- itive effect on small business performance (Keskin, 2006). However, given that incremental innovation fo- cuses on extensions to and building on current product or service offerings (Subramaniam & Youndt, 2005), while radical innovation focuses on disrupting current product or service offerings (Subramaniam & Youndt, 2005), differential performance effects are possible. Past research, however, does suggest that radical and 4 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 incremental innovation are highly correlated, with 90% of firms in a study conducted by Plotnikova, Romero, and Martínez-Román (2016) reporting that incremen- tal innovation complements their radical innovation processes. As such, it is unsurprising that small firms engage in both types of innovation, with positive ef- fects often found for both, depending on the context. In their study, Keskin (2006) surveyed 157 Turk- ish SMEs and measured innovativeness and firm per- formance using a model from Calantone, Cavusgil, and Zhou (2002). Keskin (2006) found that when SMEs frequently try new ideas, seek out new ways to do things, develop new product/services, and try to be creative in their methods of operations, they become more profitable, get higher market share, and grow at a higher rate. These findings are consistent with those found in similar studies (Forsman & Annala, 2011; Saunila, Ukko, & Rantanen, 2014). Bhaskaran (2006) examined incremental innova- tion in 87 seafood retail SMEs in Australia. Results of this study suggest incremental innovation positively associated with both profitability and sales growth. In their study of 108 UK SME’s in manufacturing, tech- nology, and information industries, Oke, Burke, and Myers (2007) found that these SMEs tend to focus on incremental innovations and that such innovations are positively related to sales growth. Additionally, research has attempted to identify controls and forces that influence the innovation level of small businesses. While it has been found that many external factors such as government policy, resource scarcity, and economic climate can have a significant effect on innovation in small businesses, there is a no- ticeable lack of research focusing on internal factors and controls (Foreman-Peck, 2013; Madrid-Guijarro, García-Pérez-de-Lema, & Auken, 2016; Woschke, Haase, & Kratzer, 2017). EO versus IEO EO has been widely studied (see Rauch et al., 2009 for a meta-analysis) and the subject of a special issue of Entrepreneurship Theory and Practice (Covin & Lumpkin, 2011). The EO scale was constructed us- ing behaviors identified in business strategy and entre- preneurship literature (Covin & Slevin, 1989; Miller, 1983) and generally includes three to five dimensions: innovativeness, willingness to take risks, proactiveness, competitive aggressiveness and autonomy (Lumpkin & Dess, 1996). The Rauch et al. (2009) meta-analysis looked at 51 studies with 14,259 companies and found that in the majority of the studies, only innovativeness, risk-taking, and proactivness were used, and that the EO construct was studied as unidimensional (in 37 studies) as opposed to multidimensional (in 14 stud- ies). Rauch et al. (2009) found EO was correlated with performance (“moderately large” r = 0.242) and robust to different operationalizations of key constructs as well as cultural contexts. Research on small business strategy uses EO also. Messersmith and Wales (2013) looked at EO and the role of human resource management in young firms, and Lechner and Gudmundsson (2014) looked at EO, firm strategy, and small firm performance. In such studies, EO is measured at the firm-level where the re- sponse of one individual became the measure of EO for the entire firm. In the small business context, EO is generally studied as a firm-level construct. For exam- ple, Wiklund, (1998) defines EO as a “willingness of a firm to engage in entrepreneurial behavior” (Wiklund, 1998, p. 65) and Lumpkin and Dess (1996) suggest that EO reflects how an organization operates. Primar- ily studied for its relationship to firm performance, EO has been shown to explain on average, 24% of varia- tion in performance of the firm (Rauch et al., 2009). Additionally, Rauch et al. (2009) concluded that other factors are likely and recommended examining other variables. Prior research suggests a gap between EO and entrepreneurial behavior in the organization (Ki- lenthong, Hultman, & Hills, 2016), such as innovation level (Kollmann & Stöckmann, 2014). Bolton and Lane (2012) proposed measuring an individual’s EO with their IEO scale. Initially using all five dimensions of EO, Bolton and Lane (2012), adapt- ed the EO scale by asking participants to respond to Likert scale statements referring to the individual rath- er than to the firm (e.g., changed “my firm” to “I”). In- novativeness, risk-taking, and proactiveness emerged as three distinct factors resulting in the ten-item IEO scale which demonstrated validity and reliability. As such, this appears to be a more appropriate measure in the small-firm context, where the owner/manager sets the strategic posture for the firm (Andries & Czarnitz- ki, 2014; Madison, Runyan, & Swinney, 2014; Nejati, 5 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 Quazi, Amran, & Ahmad, 2017). In their analysis, Strese, Keller, Flatten, & Brettel (2018) examined the effects of a given CEO’s passion for inventing on the radical innovation of SMEs as well as a hypothesized moderating effect from shared vision, defined as “the extent to which organizational members have collective goals and common aspira- tions with regard to their firm’s future development.” Surveying a sample of 388 German SMEs, it was found that CEO’s with a high passion for invention represent- ed a strong correlation with the radical innovation of their firm, and that this relationship was strengthened by the firm’s shared vision (Strese, et al., 2018). While not completely congruent, the measure of passion for invention could be stated to be comparable to that of IEO; thus, further validating the relevance of individu- al level constructs in the small firm setting. IEO and Innovation Level Innovation can vary in terms of the “newness” to the organization or unit (Dewar & Dutton, 1986). In particular, it can be categorized as radical vs. incre- mental. Radical innovation involves clear departures from existing technology or practice (Duchesneau, Cohn & Dutton, 1979; Ettlie, 1983) while incremental innovation is considered to be minor improvements or simple adjustments in current technology or practices (Munson & Pelz, 1979). There is a dearth of research examining the relationship between EO and innovation level and to our knowledge, no prior studies have ex- amined IEO, management control, and innovation lev- el simultaneously. This is particularly relevant given the importance of the owner/manager’s role in a small business context. In line with the upper echelons view (Hambrick & Mason, 1984), small business research suggests that the owner/manager sets the firm’s stra- tegic posture or orientation for important arenas of operation (Aloulou & Fayolle, 2005; Chaganti, Watts, Chaganti, & Zimmerman-Treichel, 2008), including the pursuit of innovation. Given IEO has not been frequently used in such investigations, however, we develop our hypotheses based on evidence provided by the firm-level EO construct. Given that EO is the firm-level operationalization of the individual-level construct, we anticipate the same direction of effects. Researchers have examined the EO and innova- tion link across different contexts, although most are specialized contexts, such as particular industries and look only at innovation as a mediator rather than an outcome. For example, Avlonitis and Salavou (2007) investigate the relationship between EO, product in- novativeness, and performance in 143 Greek firms. They found that entrepreneurs with high EO correlated with new product uniqueness and product newness to the firm, and as such indicates a relationship between EO and innovation within the firm. In their study of EO and innovation in exporting, Boso, Cadogan and Story (2013) found that EO in export behavior led to export product innovation success. In their study of EO in creative industries, Parkman, Holloway, and Sebas- tiao (2012) found that a highly significant association existed between EO and innovation capacity in their larger study. In their examination of the Italian and Spanish tile industries Alegre and Chiva (2013) found a positive and significant link between EO and inno- vation performance of the firm, although distinctions in types of innovation were not made and the ultimate goal was to examine firm performance. Although the parameters of their study differ quite markedly from most EO-innovation work, Kollmann and Stöckmann (2014) found that the three dimensions of EO signifi- cantly correlate with exploration activities within the firm. This suggests a positive relationship between EO and innovative activities. Further, Kollmann and Stöckmann (2014) argue that it is critical to examine how EO manifests into entrepreneurial behavior. Additionally, some research has been conducted on the relationship between spin-offs and innovation (Scaringella, Miles, & Truong, 2017). Spin-offs are de- fined as business ventures stemming from technolog- ical knowledge originating from universities, research centers, and corporations (Scaringella, et al., 2017). Scaringella et al. (2017) concluded that spin-offs’ abil- ity to capture knowledge from both customers and the originating research center directly and positively af- fects their ability to radically innovate. Although not a direct link between IEO and innovation, Scaringella et al. (2017) provides a valuable backdrop for this study. Despite the myriad of contexts, studies exploring EO and innovation have generally reported a signifi- cant effect for EO as a precursor to innovation of all types. As such, we hypothesize the following relation- ship using IEO as the individual-level operationaliza- tion of the EO construct. 6 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 Hypothesis 1A. IEO of the owner/manager is posi- tively associated with reported incremental innovation level for the firm. Hypothesis 1B. IEO of the owner/manager is positive- ly associated with reported radical innovation level for the firm. Management Control Processes Simons (1990) states “management control sys- tems are the formalized procedures and systems that use information to maintain or alter patterns in organi- zational activity” (Simons, 1990, p. 128). There have been several categorizations of MCS in the literature, ranging from formal/informal, behavioral/input/out- put, to financial/nonfinancial. Financial controls in- volve traditional accounting-based methods such as budgets, cash flow, sales, etc. Nonfinancial controls include performance evaluations, policies/procedures, and customer feedback. Companies generally begin by adopting informal and nonfinancial control sys- tems according to Davila and Foster (2007). As they grow there is a move toward more formal and financial MCS because the constant interaction and observation required of many nonfinancial MCS become cumber- some. MCS have evolved in ways to assist in increasing innovation. “The need for organizations to be innova- tive has added to the challenges for control systems to help managers accomplish innovation” (Chenhall & Moers, 2015, p. 2). Additionally, Bedford (2015) sug- gests that MCS play a central role in the management of innovation. MCS actually increase the capacity of an organization to derive benefits from innovation (Bisbe & Otley, 2004; Jørgensen & Messner, 2010). Research on the relationship between MCS and innovation is generally lacking, but some studies have been conducted on the matter. Bisbe and Otley (2004) examined the effect of interactive MCS on project in- novation and defined interactive control systems as “formal control systems that managers use to become personally and regularly involved in the decision activ- ities of subordinates…” (Bisbe & Otley, 2004, p. 717). Overall, they found that MCS do not significantly affect the relationship between innovation and performance in low-innovating firms but do negatively mediate the relationship between innovation and performance in high-innovating firms (Bisbe & Otley, 2004). As such, in highly innovating firms MCS appear to be a detri- ment to product innovation level. Similarly, Dunk (2011) examined the relationship between budget controls and product innovation and performance. Specifically, he hypothesized that when budgeting is used as a control measure as opposed to a planning measure, it would have a negative effect on innovation and performance. Dunk (2011) found that when used as a planning measure, budgets had a pos- itive effect on product innovation and performance. Conversely budgets were determined to have an ad- verse effect on product innovation and performance when used strictly as a control measure (Dunk, 2011). This is consistent with findings in other related research (Abernethy & Brownell, 1997; Bisbe & Otley, 2004). When considering nonfinancial MCS, Rockness and Shields (1984) found that nonfinancial controls, such as rules and procedures, were most important in R&D when there were high levels of knowledge in the transformation process. Additionally, Abernethy and Brownell (1997) reported that personnel controls were more effective than accounting controls when task un- certainty was high within R&D. Finally, Rockness and Shields (1988) discovered that social controls can sub- stitute for expenditure budgets in R&D settings. These findings are all relevant as R&D functions often rely on innovation. Merchant (1990) found that financial controls re- sulted in a discouragement of new ideas because of the short-term focus, while Govindarajan (1988) found that product differentiation strategies, which often rely on innovation, resulted in the diminished role of budgetary controls. These studies all point towards the significance of nonfinancial MCS in the creation of in- novation in firms. In fact, Chenhall and Moers (2015) conclude: …research into the role of performance mea- surement in settings where innovation is import- ant confirms that the traditional use of financial controls for evaluation is insufficient and poten- tially ineffective. Rather, broader controls, such as nonfinancial metrics and subjective measures, are more useful. This is because these measures are able to encourage and evaluate innovative effort, the effects of which have a longer time horizon. (Chenhall & Moers, 2015, p. 4) 7 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 Using a case study method, Chiesa, Frattini, Lam- berti, and Noci (2009) found that top management utilized more informal controls throughout radical innovation projects such as belief systems, especial- ly during the concept generation and launch phases, where such radical innovation and change were most apparent. Conversely, incremental innovation projects were defined by more formal control systems, name- ly diagnostic controls and quantitative indicators of performance, due to the more predictable natures and outcomes of the projects. McDermott and O’Connor (2002) found that most firms based their radical in- novation projects on already familiar internal knowl- edge in competencies, that they then launched into new products or processes (McDermott & O’Connor, 2002). Finally, the authors noted that traditional man- agement and controls seemed to play a minor role in these projects, with informal networks and controls serving as a much more important backdrop. Generally, there is a lack of research oriented to- wards owner or owner/manager psychological frame and the implementation of financial and nonfinancial management controls. In small businesses, it is the owner/manager that determines the types of controls to be implemented. The product innovation and research and development orientation of most of the prior stud- ies mentioned suggest that there is a relationship be- tween strategy of the firm, management control system implementation and innovation level as the entrepre- neurial strategy is manifest through such behavior. In their seminal piece, Neimark and Tinker (1986) argued that MCS are socially constructed, which suggests it is critical to consider the culture and orientation of the firm when examining these phenomena. For example, in software development, a naturally more innovative industry, Ditillo (2004) found that knowledge com- plexity influenced the configuration of management controls; thus, suggesting that the culture and strate- gy of the firm influence level of management control implementation, which then manifests in a behavioral outcome, such as innovation, software development, new product development, etc. In summary, the re- search suggests that the owner and/or owner/manager set the strategic posture for the firm through their IEO, and as such, the implementation of MCS, as a socially constructed phenomena will be affected. Further, MCS exhibits effects on both incremental and radical inno- vation levels. As such, we expect a partial mediation effect for both types of management control systems on the relationship between IEO and radical and incre- mental innovation levels. Hypothesis 2A. Non-financial management control systems for the firm partially mediates the relationship between IEO and incremental innovation level of the firm. Hypothesis 2B. Financial management control sys- tems for the firm partially mediates the relationship between IEO and incremental innovation level of the firm. Hypothesis 2C. Non-financial management control systems for the firm partially mediates the relationship between IEO and radical innovation level of the firm. Hypothesis 2D. Financial management control sys- tems for the firm partially mediates the relationship between IEO and radical innovation level of the firm. Method Sample and Procedure Data were collected via survey over the course of two semesters across the mid-south region of the Unit- ed States using a peer recruitment sampling technique known as network sampling (e.g., Ingram, Peake, Stewart & Watson, 2017; McGee, Peterson, Mueller & Sequeira, 2009). Students in entrepreneurship and hu- man resource courses at a mid-major university were asked to identify entrepreneurs and managers affiliated with small firms as part of an entrepreneur interview project required for their respective courses. Students contacted small business owners and managers in ad- vance of the interview to ask them to complete a survey as part of the interview process. Since students served as the initial point of contact, students were instruct- ed on the research objectives of the survey instrument. Additionally, through this initial contact, respondents were assured that their survey responses would re- main confidential and that any potentially identifiable information would be held separately from the sur- veys. Students were instructed that follow-up would be made with the small business owners and managers to ensure that surveys were completed as instructed. 8 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 Prior research suggests that such methods may lead to greater diversity of ethnic and socioeconom- ic backgrounds than traditional mail survey methods that rely on Small Business Administration databas- es or local Chambers of Commerce (Cooper, Peake, & Watson, 2016; Ingram et al., 2017; McGee et al., 2009; Peake, Davis, & Cox, 2015a; Peake, Harris, Mc- Dowell, & Davis, 2015b; Sequeira, Mueller & McGee, 2007). With students serving as the point of contact, researchers suggest that business owners may be iden- tified who would not have been available via lists from entities such as Chambers of Commerce, since the per- sonal contact made through this methodology may ap- peal to respondents (Ingram et al., 2017). Using this technique, 265 surveys were returned. We only retained respondents who were owners or owner/managers active in the day-to-day operations of the firm and who indicated that s/he and his/her em- ployees made major decisions affecting the firm, giv- en the importance of the influence of the owner and/or owner/manager on the firm’s use of MCS, as well as its innovation levels. Additionally, to maintain focus on small firms, we deleted cases in which the total num- ber of employees were greater than 250 (Chowdhury, Schulz, Milner, & Van De Voort, 2014; McDowell, et al., 2018; Thurik, Khedhaouria, Torres, & Verheul, 2016), as well as any survey observations where an en- tire construct or more was incomplete on the survey. After removing data points which did not adhere to the aforementioned criteria, 212 survey observations remained. For any analysis, when missing cases were deleted listwise, a range of 185-194 observations were utilized. Although the sample size is below N = 200, Paterson, Harms, Steel, and Credé (2016) determined recommended sample sizes for 0.95 statistical power in performance studies is 168. Given expected effect sizes given prior literature of (0.12 – small, 0.20 – me- dium, 0.31 – large), our study appears to possess the credibility to find significance. While our sample holds many similarities to the most recent data reported by the Small Business Ad- ministration (2015) in its Issue Brief on “Demographic Characteristics of Business Owners and Employees,” we see many differences that may be a result of our sampling methodology. For example, as shown in Ta- ble 1, our sample skews younger than the SBA sam- ple, with a higher percentage of male respondents, who are generally more educated than those reported via the SBA. Other studies utilizing network sampling likewise report differences in similar areas (Ingram et al., 2017; Peake & Watson, 2015; Peake et al., 2015b), given that students tend to approach younger, better educated entrepreneurs. However, we do not believe these differences affect the quality of our analyses, giv- en prior researchers likewise collected data with simi- lar features using this methodology. Table 1 Samples compared to SBA (2013) data SBA Sample* (%) Sample (%) Age Under 35 15.6 27.8 35 to 49 32.7 39.2 50+ 51.7 33.0 Gender Male 64.6 71.3 Female 35.4 28.7 Race Minority 14.1 10.1 Non-Minority 85.9 88.9 Education High School or Less 28.0 30.0 Some College 32.8 16.7 Bachelor’s or Higher 39.2 53.4 *Source: Demographic Characteristics of Business Owners and Employees: 2013, SBA Office of Advo- cacy The data collected via our survey are cross-sec- tional, since a single individual provided responses to the survey at a single point in time. As such, common method bias may be a concern (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). To mitigate potential con- cerns associated with common method bias, follow- ing Podsakoff et al. (2003), we employed procedural techniques during the survey phase. We took care that wording of items was clear and to the point, avoided the use of dichotomous scales, and ensured respon- dents their anonymity would be protected through the 9 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 data reporting process since results are only reported in the aggregate, and we provided careful examination of the data via statistical techniques (Podsakoff, et al., 2003). Although we cannot ensure all biases are omit- ted, our precautions associated with data collection, as well as our statistical procedures detailed in the Re- sults section indicate that such biases does not impede us from meaningful analysis and interpretation of the results. Measures and Validity Innovation level. The innovation level of the firm serves as the dependent measure. This is further broken into two separate measures, radical innovation and in- cremental innovation per the development of the mea- sures detailed by Subramaniam and Youndt (2005). Radical innovation addresses innovations that dis- rupt the firm and its products and services (Dewar & Dutton, 1986; Meyers & Tucker, 1989; Subramaniam & Youndt, 2005), with the following items: innovations that make your prevailing product/service lines obso- lete, innovations that fundamentally change your pre- vailing products/services, and innovations that make your existing expertise in prevailing products/services obsolete. Respondents were asked to indicate the num- ber on a scale from 1 to 7, with 1 = much stronger and 7 = much weaker, that best represents their organiza- tion’s capacity to generate innovations in products/ser- vices compared to the competition. After respondents had completed the surveys, responses were recoded to 1 = much weaker and 7 = much stronger. Incremental innovation indicates the level to which the firm builds on and further develops its cur- rent product and/or service offering(s) (Chandy & Tel- lis, 2000; Subramaniam & Youndt, 2005). Items ad- dressing incremental innovation include: innovations that reinforce your prevailing product/service lines, innovations that reinforce your expertise in prevailing products/services, and innovations that reinforce how you currently operate. Given that radical and incremental innovation are highly correlated (0.511), yet are distinctly different approaches to innovation strategy (Forés & Camisón, 2016; Subramaniam & Youndt, 2005), we examine two separate models for the level of innovation associ- ated with each type. Both measures exhibit solid reli- ability, with a Cronbach’s alpha of 0.845 for the three incremental innovation items and a Cronbach’s alpha of 0.844 for the three radical innovation items. Individual entrepreneurial orientation (IEO). Bolton and Lane (2012) developed a ten-item IEO scale with subscales of risk-taking, innovativeness, and proactiveness all with Cronbach’s alphas above the generally accepted thresholds for scale develop- ment (Nunnally & Bernstein, 1994). Averages of all scale items correlated with entrepreneurial propensity, which Bolton and Lane (2012) used to establish con- struct validity for the IEO scale in addition to its con- tent and face validity and its internally consistent set of items. Respondents were asked to indicate their level of agreement with the 10 items on a 7 point Likert scale, where 1 = strongly agree and 7 = strongly disagree. Once respondents had completed the survey, responses were recoded to represent 1 = strongly disagree and 7 = strongly agree. We averaged the 10 items as devel- oped and validated by Bolton and Lane (2012) to form a single construct, which exhibited a Cronbach’s alpha of 0.875. Management control systems. Using the man- agement control systems (MCS) aspects of Davila et al. (2009), we examine two types of MCS, financial management control systems and non-financial man- agement control systems. We created two measures, one representing financial management controls with four items, and another comprising non-financial man- agement controls with eight items that have primarily a human resources orientation. (See Appendix 1 for a summary of these measures.) Respondents indicated whether or not they had implemented a particular con- trol for each item. Items were then coded for whether the control was in place (X = 1) or was not in place (X = 0) at the time the survey was completed. Averag- es were calculated for each construct with regards to implementation of the item, and the averages for both items ranged from 0 to 1. Such coding has been com- mon in the human resources literature with regards to High Performance Work Systems (e.g., Patel & Conk- lin, 2012). In examining reliability, the four financial monitoring items exhibited a Cronbach’s alpha of 0.923, while the eight other monitoring items exhibited an alpha of 0.902. Given the high correlation between these two constructs (0.686), and the potentially differ- ent implications derived from each determined per the 10 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 literature review, we examine these measures separate- ly for their potential effects on innovation level. Controls. There are likely many other factors at play in the examination of the relationships about which we hypothesize. As such, we examine a number of other controlling factors that relate to both the indi- vidual owner/manager and the business itself. Business level factors include the family business status of the firm, the number of employees, and busi- ness age. Just under 50% of the respondents indicated that their business was a family business. Given the literature’s examination of innovation within family businesses versus other small businesses (e.g., Calabró et al. 2018; De Massis, Frattini, Pizzurno, & Cassia 2015), we look to family business status as a potential- ly important control. Further, the number of employees as a proxy for firm size is often an important controlling factor that helps to account for resources at hand. We examine the total number of employees reported by the owner/managers. We also assess the business age as a proxy for stability, as older firms have overcome the threshold for survival. The business age was reported by the respondent in terms of number of years the busi- ness has been in operation as of the time the survey was completed. Additionally, we examine individual-level factors of the owner/manager to account for influences aside from IEO that may hold important impacts. Like many other studies in this realm, we asked respondents to report their gender, education level, and experience in previously starting a business. Prior studies suggest that gender plays an important role in managerial deci- sions (Kakabadse et al., 2015; Quintana-García & Be- navides-Velasco, 2016). As such, there may be import- ant gender effects for implementation of MCS. Gender of the respondent is in binary form, coded with 1 = Female and 0 = Male. Because more educated busi- ness owners may adopt higher levels of management control, respondents were asked to indicate their high- est level of education completed, on a scale with 1 = less than high school and 7 = doctorate or professional degree. Since prior experience may give owner/man- agers more incentive to implement MCS, we examine whether the individual had previously started or owned anther business, reported as yes (X = 1)/no (X = 0). Results To ensure the data are appropriate for undertak- ing our statistical procedures, we conducted precurso- ry analyses regarding multicollinearity and common method bias. Although efforts were taken with the methods to ensure the mitigation of common meth- od bias to the extent possible, we examined the data for such biases via a Harman one-factor test (Chen, Chang, & Lee, 2015; Roxas, Ashill, & Chadee, 2017; Virick, Basu, & Rogers, 2015). The Harman one-factor test suggests that no single factor dominates the anal- ysis, since the items loaded onto eight factors with ei- genvalues greater than one, and no factor accounted for more than 23% of the variance. As such, common method bias does not appear to preclude meaningful analyses with our data. Additionally, multicollineari- ty does not appear to pose a serious limitation to the data since all VIFs were less than 1.5 and the condition index was less than the commonly accepted threshold of 30 (Hair, Anderson, Tatham & Black, 1998; Hair, Black, Babin & Anderson, 2010). Mean, standard de- viation and correlations for the variables of interest are available in Table 2. We tested our hypotheses via four models, with Models 1 and 2 shown in Table 3 and Models 3 and 4 highlighted in Table 4. For an overview of controlling variable effects only, please see the Model 0 regres- sions in Appendix 2. These two regressions suggest that the control variables do not unduly affect the asso- ciations in the analyses that follow. Model 1 explores the relationship of IEO on incremental innovation, with financial management controls as a mediator. IEO (β = 0.4477, p < 0.001) has a powerful, positive direct effect on level of incremental innovation as hypothe- sized. Further, IEO has a positive and significant effect on the implementation level of financial management controls (β = 0.0783, p < 0.01). However, there is no indirect effect for financial management controls on the relationship between IEO (β = 0.4273, p < 0.001) and level of incremental innovation for the firm. Model 2 examines the effect of non-financial man- agement controls as a mediator between IEO and in- cremental innovation level. This model indicates that IEO has a strong, positive effect on both the imple- mentation of non-financial management controls (β = 0.0729, p < 0.01) and level of incremental innova- 11 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 tion (β = 0.4486, p < 0.001). Further, as hypothesized, non-financial management controls (β = 0.6551, p < 0.01) partially mediates the relationship between IEO (β = 0.4008, p < 0.001) and level of incremental inno- vation, such that the direct effect is lessened but still significant. In Model 3, we see that IEO exhibits a significant and direct effect on both the adoption of financial man- agement controls (β = 0.0844, p < 0.01), as well as on radical innovation level (β = 0.4477, p < 0.001). Finan- cial management controls, however, do not mediate the relationship between IEO (β = 0.5000, p < 0.001) and radical innovation level. Model 4 exhibits a strong direct effect of IEO on the implementation of non-financial management con- trols (β = 0.0742, p < 0.01) and level of radical inno- vation (β = 0.5116, p < 0.001). The implementation of non-financial management controls (β = 0.5988, p < 0.05) exhibits partial mediation of the relationship be- tween IEO (β = 0.4672, p < 0.001) and level of radical innovation. Our results indicate support of both Hypothe- ses 1A and 1B in that IEO is positively and significant- ly associated with incremental and radical innovation, respectively. Further, we find support for Hypotheses 2A and 2C, given that non-financial management con- trols partially mediate the relationship between IEO of the owner/manager and incremental and levels of radi- cal innovation, respectively. Although owner/manager Table 2 Means, standard deviations and correlations Mean Std. Dev. 1 2 3 4 5 6 7 8 9 1. Family Business 0.45 0.50 - 2. Gender (Female) 0.27 0.46 0.01 - 3. Education Level 4.11 1.59 -0.15* 0.09 - 4. No. of Employees 18.94 28.89 -0.10 -0.11 0.19* - 5. Business Age 21.13 27.24 0.06 0.02 0.02 0.34** - 6. Owner Experience 0.39 0.49 0.03 -0.06 -0.05 0.14 -0.13 - 7. IEO 5.60 0.88 -0.01 -0.11 0.02 -0.04 -0.11 0.20** - 8. Financial Mgmt. Controls 0.78 0.36 -0.18 * -0.05 0.10 0.10 -0.15* -0.01 0.22** - 9. Other Mgmt. Controls 0.65 0.36 -0.17 * -0.06 0.13 0.30** -0.10 -0.08 0.18* 0.68** - *p < 0.05, **p < 0.01 IEO is positively and significantly associated with im- plementation of financial management controls, there is no indirect relationship of financial management controls between the relationship of owner/manager IEO and either form of innovation. As such, we fail to find support for Hypotheses 2B and 2D. Discussion Academic Implications Our results indicate that IEO is an important strategic posture to promote both radical and incre- mental innovation levels within small firms. Further, IEO shows a positive and significant association with the implementation of both financial and nonfinancial MCS. However, only nonfinancial MCS partially me- diate the relationship between IEO and innovation lev- el (both incremental and radical). Financial MCS do not exhibit an effect on innovation level, and as such, do not have an indirect effect. To our knowledge, our study is the first to explore the relationships among IEO as a strategic posture, MCS, and innovation level in the small firm context. We believe our results hold important implications both from academic and practi- cal perspectives. Financial MCS suggest a control of resources, and greater access and control of financial resources can be expected to promote innovation within the firm. Our 12 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 Table 3 Regression models examining incremental innovation MODEL 1 MODEL 2 Financial Mgmt Controls Incremental Innovation Level Incremental Innovation Level Other Mgmt Controls Incremental Innovation Level Incremental Innovation Level Family Business -0.0932ᶧ (0.0503) -0.1271 (0.1373) -0.1029 (0.1383) -0.0673 (0.0487) -0.1282 (0.1380) -0.0841 (0.1351) Gender (Female) -0.0293 (0.0558) 0.0264 (0.1525) 0.0341 (0.1523) -0.0112 (0.0540) 0.0270 (0.1530) 0.0343 (0.1492) Education level 0.0088 (0.0160) -(0.0160) (0.0436) -0.0183 (0.0436) 0.0064 (0.0154) -0.0158 (0.0438) -0.0200 (0.0427) No. of Employees 0.0018ᶧ (0.0009) 0.0003 (0.0026) -0.0002 (0.0026) 0.0043*** (0.0009) 0.0003 (0.0026) -0.0026 (0.0027) Business Age -0.0021* (0.0010) 0.0030 (0.0027) 0.0035 (0.0027) -0.0025** (0.0010) 0.0030 (0.0027) 0.0046ᶧ (0.0027) Owner Experience -0.0176 (0.0517) -0.1521 (0.1412) -0.1475 (0.1409) -0.0552 (0.0502) -0.1537 (0.1422) -0.1175 (0.1392) IEO 0.0783** (0.0290) 0.4477*** (0.0792) 0.4273*** (0.0806) 0.0729** (0.0281) 0.4486*** (0.0798) 0.4008*** (0.0792) Financial Mgmt Controls - - 0.2612 (0.1999) - - Non-Financial Mgmt Controls - - - - 0.6551** (0.2032) F 3.1376** 4.7974*** 4.4272*** 5.6157*** 4.7400*** 5.6567*** R2 0.1056 0.1529 0.1607 0.1752 0.1521 0.1974 N 194 193 Total, Direct, and Indirect Effects Total effect X on Y 0.4477 *** (0.0792) 0.4486*** (0.0798) Direct effect X on Y 0.4273*** (0.0806) 0.4008*** (0.0792) Indirect effect X on Y 0.0205 (0.0205) 0.0478* (0.0319) Normal Theory Tests 0.0205 (0.0183) 0.0478* (0.0243) ᶧ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001 results are surprising in that financial MCS do not dis- play a significant link to incremental or radical inno- vation level of the firm. Given that Dunk (2011) found that when used as a planning measure, budgets had a positive effect on product innovation and performance and that budgets were determined to have an adverse effect on product innovation and performance when used strictly as a control measure, we expected to see some effect consistent with findings in related research (Abernethy & Brownell, 1997; Bisbe & Otley, 2004) as well. Our descriptive statistics indicate a high level of financial MCS implementation across our sample, with an average of 0.75. This suggests that, on average, firms in the sample had incorporated three of the four finan- cial MCS items. We believe our results may suggest 13 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 Table 4 Regression models examining radical innovation MODEL 3 MODEL 4 Financial Mgmt Controls Radical Innovation Level Radical Innovation Level Other Mgmt Controls Radical Innovation Level Radical Innovation Level Family Business -0.1010* (0.0509) 0.0780 (0.1579) 0.0975 (0.1598) -0.0829 (0.0497) 0.0845 (0.1587) 0.1341 (0.1576) Gender (Female) -0.0143 (0.0574) 0.1490 (0.1776) 0.1517 (0.1781) -0.0034 (0.0558) 0.1454 (0.1784) 0.1474 (0.1757) Education level 0.0102 (0.0163) -0.0271 (0.0505) -0.0291 (0.0506) 0.0108 (0.0159) -0.0281 (0.0507) -0.0346 (0.0500) No. of Employees 0.0018ᶧ (0.0010) 0.0023 (0.0030) 0.0020 (0.0030) 0.0043*** (0.0009) 0.0024 (0.0030) -0.0001 (0.0031) Business Age -0.0023* (0.0010) 0.0009 (0.0031) 0.0014 (0.0031) -0.0027** (0.0010) 0.0009 (0.0031) 0.0025 (0.0031) Owner Experience -0.0324 (0.0526) -0.2681 (0.1632) -0.2618 (0.1635) -0.0626 (0.0515) -0.2588 (0.1644) -0.2213 (0.1626) IEO 0.0844** (0.0292) 0.4477*** (0.0792) 0.5000*** (0.928) 0.0742** (0.0285) 0.5116*** (0.0912) 0.4672*** (0.0915) Financial Mgmt Controls - - 0.1933 (0.2326) - - - Non-Financial Mgmt Controls - - - - - 0.5988* (0.2366) F 3.4423** 4.8305*** 4.3057*** 5.8528*** 4.6891*** 5.0291*** R2 0.1192 0.1596 0.1629 0.1880 0.1564 0.1861 N 186 185 Total, Direct, and Indirect Effects Total effect X on Y 0.5163 *** (0.0906) 0.5116*** (0.0912) Direct effect X on Y 0.5000 *** (0.0928) 0.4672*** (0.0915) Indirect effect X on Y 0.0163(0.0239) 0.0444ᶧ (0.0295) Normal Theory Tests 0.0163(0.0215) 0.0444ᶧ (0.0254) ᶧ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001 that financial MCS are a basis to pursue innovation, and as such do not have sufficient variability to yield significance in promoting innovation. Some research has found that both personnel and social controls serve as a substitute for accounting (Abernethy & Brownell, 1997) and budgets in R&D settings. The results of our analyses allow an opportunity to build on prior work, which suggests a substitution effect for financial and nonfinancial MCS. Using a contingency-based approach to manage people in organizations has been shown to be related to innovation and firm performance in large organiza- tions. For example, Schuler and Jackson (1987) ar- gued that firms which were more innovative used tai- lored HR policies that better supported their strategic goals rather than a “one size fits all” approach. A study 14 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 by Berends, Jelinek, Reymen, and Stultiëns (2014) ex- tends this use of contingency policy implementation to the small firm context and illustrated how these types of firms were more innovative than their more struc- tured counterparts. As such, from a contingency per- spective we believe our research raises important im- plications for small business researchers in considering nonfinancial MCS configurations. Our results showed that the use of non-financial MCS had positive impacts on innovation in small firms. Support for this can be found in previous liter- ature from the HR domain. Studies by Ceylan (2013), Chadwick, Super, and Kwon (2015), and Chen and Huang (2009) found that using HR policies and prac- tices that demonstrated commitment to the employees resulted in increased innovation and firm performance in large firms. We believe our results suggest what an- ecdotal evidence has also perceived, in that when job functions are described as relating to innovation and are measured based on their innovation output, inno- vation level within the firm will be higher. In addition to important academic implications, we likewise be- lieve our work raises important implications for prac- titioners. Practical Implications The practical implications of our work are im- portant for small business owners. Our results suggest an association between high IEO and the implementa- tion of both financial and non-financial MCS. Howev- er, only nonfinancial MCS appear to mediate the rela- tionship between IEO and both forms of innovation. As such, it appears that the implementation of non- financial, primarily human resources based, MCS are important to the development of both radical and in- cremental innovation in small businesses. As shown in Appendix 1, these items deal with clear objectives for employees and managers, with compensation linked to performance. This suggests that clearly planning man- agers’ and employees’ roles, and ensuring that meeting targets is tied to it, creates an environment in which innovation can take hold. However, additional, longi- tudinal data is needed to address causation rather than association. While IEO was directly tied to the implemen- tation of financial MCS, there was no indirect effect of financial MCS on innovation. As can be seen in Table 2, financial MCS hold an even greater positive associ- ation with IEO than nonfinancial MCS. It appears that high IEO owners and owner/managers implement such measures to monitor and control their businesses; how- ever, the mere implementation of financial controls does not appear to provide an impetus for innovation. MCS associated with the “people” side of the business increases innovation productivity, which is an intuitive element. The lack of significance for financial MCS does not indicate a lack of importance to innovative small firms. Quite the contrary is true. Looking to our descriptive statistics, it is apparent that the small firms in our sample indicate a substantially higher imple- mentation of financial MCS than other MCS. As such, other MCS may serve as a key differentiator or source of variability in innovation level emanating from high IEO owners and owner/managers. Given our results and the data, the implementation of solid financial MCS may be a necessity rather than a differentiator. Limitations and Future Research As is the case with any research study, ours suf- fers from limitations. We believe there are three pri- mary limitations. First, our data are cross-sectional in nature, and as such may be subject to common meth- od bias (Podsakoff et al., 2003). However, precautions were taken during the data collection process to limit the impact of such bias, and statistical analyses suggest such biases do not preclude us from meaningful analy- sis and interpretation of the results. Further, cross-sec- tional data are common in small business research, giv- en the difficulty of collecting data from small business owners (e.g., Patel & Conklin, 2012; Peake, Cooper, Fitzgerald, & Muske, 2017; Peake, McDowell, Harris, & Davis, 2018). Second, our data collection area was con- strained to the Southeastern United States, which may limit inferences to other regions. The data, however, skew similarly from the Small Business Administra- tion (2015) data compared to other recent studies. As such, we believe some valuable generalizations may be made across the United States with our results. Impli- cations for other countries, however, cannot be drawn since comparative data are not available. This is an im- portant future area of investigation, given both poten- tial IEO differences across culture, as well as varying 15 W. O. Peake, D. Barber III, A. McMillan,D. L. Bolton, L. Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 levels of emphasis on innovation. Finally, our MCS measures likewise hold some limitation. Although they are derived from well-cited and recognized sources in the MCS literature (Davila & Foster, 2007), clearly they do not comprehensive- ly address all sources of MCS within the firm. Given the basis in contingency theory, future research would benefit from more than a binary view of whether a particular MCS was implemented. For example, the degree of implementation and the owner/manager’s perspective of its relative importance to other MCS measures would be helpful in showcasing perceived relative importance. Also, since the “people” side of MCS appears to strengthen the relationship between entrepreneurial strategy orientation and both incre- mental and radical innovation level, additional explo- ration of human resources controls for their effect on innovation level would prove a useful avenue of re- search from both academic and practical perspectives. Conclusion This study set out to examine the relationship between IEO and innovation in small businesses. We hypothesized the relationship being mediated by man- agement control systems. Results indicate that IEO is indeed positively and significantly related to both in- cremental and radical innovation in small businesses. Additionally, we found that nonfinancial management control systems partially mediate the relationship be- tween IEO and both incremental and radical innova- tion. 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Coder Journal of Small Business Strategy / Vol. 29, No. 2 (2019) / 1-21 Appendix 1 Management control system variables Financial Management Controls Operating budget α = 0.923 Cash flow projections Sales projections Routine analysis of financial performance against target Non-Financial Management Controls Codes of conduct α = 0.902 Written job descriptions Written performance objectives for managers Written performance evaluation reports Linking compensation to performance Definition of strategic (nonfinancial) milestones Customer development plan (plan to develop market) Headcount/human capital development plan Appendix 2 Regression models with controls only MODEL 0 Incremental Innovation Level Radical Innovation Level Family Business -0.136 (0.147) 0.074 (0.170) Gender (Female) -0.023 (0.162) 0.063 (0.190) Education level -0.010 (0.047) -0.016 (0.055) No. of Employees 0.000 (0.003) 0.002 (0.003) Business Age 0.002 (0.003) 0.000 (0.003) Owner Experience -0.016 (0.149) -0.097 (0.173) F 0.208 0.186 R2 0.007 0.006 N 195 186