Operational flexibility-entrepreneurial orientation relationship: Effects and consequences

Journal of Business Research 105 (2019) 154–167

Contents lists available at ScienceDirect

Journal of Business Research journal homepage: www.elsevier.com/locate/jbusres

Operational flexibility-entrepreneurial orientation relationship: Effects and consequences

T

Hardeep Chahala, Mahesh Guptab, , Subhash Lonialc, Swati Rainad ⁎

a

Post-Graduate Department of Commerce, University of Jammu, Jammu and Kashmir State, India Department of Management, University of Louisville, Louisville, KY, United States of America c Department of Marketing, University of Louisville, Louisville, KY, United States of America d Mittal School of Business, Lovely Professional University, Phagwara, Punjab b

ARTICLE INFO

ABSTRACT

Keywords: Entrepreneurial orientation Operational flexibility Structural equation modeling Hospital industry

The purpose of this paper is to investigate entrepreneurial orientation (EO)—a performance relationship in the hospital industry, an important part of the world economy. We also examine the moderating role of operational flexibility (OF), a relatively new multi-dimensional construct grounded in the theory of operations management. We empirically investigate the EO and performance research model using survey data from 152 hospital administrators in the US. The findings confirm EO as a three-dimensional construct comprising innovativeness, proactiveness, and risk-taking, with proactiveness as the most significant dimension in enhancing hospital performance. Results indicate that OF and its three dimensions (input, process and output) moderate the effects of EO on hospital performance. This study contributes to the research stream by investigating the intersection of the corporate entrepreneurship and operations management fields. It provides a novel extension to the entrepreneurship literature, based upon the role of OF as a moderator in the EO-performance relationship.

1. Introduction Cross-disciplinary empirical research is considered significant in building theoretical underpinnings in operations management (Field et al., 2018; Whetten, 1989). Most recently, Victorino et al. (2018) and Field et al. (2018) identified and discussed future research themes in the service operations management field. Particularly, Field et al. (2018) have called on operations management researchers to conduct collaborative research on the identified themes, including supply networks, operational performance, etc. Entrepreneurship, a source of innovation and change, spurs growth and economic competitiveness. We find plethora of studies that have been conducted in the operations management and entrepreneurship fields. However, despite growing research in the two fields and a strong linkage between the two, we see quite limited scholarly research on entrepreneurship in operations management, as evidenced by the calls for such research in journals such as Journal of Operations Management (Kickul, Griffiths, Jayaram, & Wagner, 2011) and Production and Operations Management (Joglekar & Levesque, 2013). Particularly, the literature that integrates operations management with entrepreneurship is scarce in general (Goodale, Kuratko, Hornsby, & Covin, 2011; Hsu, Tan, Jayaram, & Laosirihongthong, 2014; Hsu, Tan, Laosirihongthong, & Leong, 2011;

⁎

Mortara & Parisot, 2016), especially in the service industry (Hinz & Ingerfurth, 2013; Krause, 2011; Ratten, 2012; Vecchiarini & Mussolino, 2013). Researchers have suggested conducting blended analyses of operations management and entrepreneurship to strengthen theory building (Shepherd & Patzelt, 2013; Shepherd & Patzelt, 2017). Our paper provides an answer to theorists' need for established and tested theories in operations management (Field et al., 2018; Schmenner, Wassenhove Van, Ketokivi, Heyl, & Lusch, 2009; Schroeder, 2008) and in entrepreneurship (Martens, Lacerda, & Belfort, 2016; Welter, 2011). Operational flexibility (OF) and entrepreneurial orientation (EO) are considered significant representatives of underlying theories in respective operations management (Vokurka & O'Leary-Kelly, 2000; Yu, Cadeaux, & Luo, 2015) and entrepreneurship fields (Rauch, Wiklund, Lumpkin, & Frese, 2009; Vega-Vazquez, Cossío-Silva, & RevillaCamacho, 2016). OF captures the intricacies involved in transforming inputs into a finished product/service and refers to the process-driven capability to cope with or react to environmental uncertainties, both external (e.g., competitors' actions) and internal (e.g., skill set) (Yu et al., 2015; Gupta & Somers, 1996), to create competitive advantage enhancing opportunities. Whereas EO refers to a market-driving exploratory capability, reflecting a firm's ability to take risks and operate innovatively and proactively to alter the market structure (Avlonitis &

Corresponding author. E-mail addresses: [email protected] (M. Gupta), [email protected] (S. Lonial), [email protected] (S. Raina).

https://doi.org/10.1016/j.jbusres.2019.08.011 Received 6 February 2019; Received in revised form 6 August 2019; Accepted 8 August 2019 0148-2963/ © 2019 Published by Elsevier Inc.

Journal of Business Research 105 (2019) 154–167

H. Chahal, et al.

Salavou, 2007). Both OF and EO concepts are not as well explored in the service sector. The extant literature on EO does not capture the essence of a form of entrepreneurship within specific contexts, particularly outside the purview of real for-profit settings. Martens et al. (2016) state that research on EO, understanding what drives it in service organizations and how it impacts firm performance, needs researchers' attention in order to develop and extend its theory. Our study also responds to the call of papers to understand how to strengthen the effect of EO on performance (Welter, 2011). Over the last two decades, there is a strong resurgence of interest in the OF concept and its implementation in operations management (OM) literature (e.g., Yu et al., 2015; Jain, Jain, Chan, & Singh, 2013; Beach, Muhlemann, Price, Paterson, & Sharp, 2000; Zhang, Vonderembse, & Lim, 2003; etc.). OM literature discusses the role of OF as a tool to improve business performance and to sustain competitive advantage (Koste, Malhotra, & Sharma, 2004; Nakane & Hall, 1991) in a dynamic environment by confronting uncertainties. Meta-analytical studies on OF (e.g., Jain et al., 2013; Vokurka & O'Leary-Kelly, 2000; Yu et al., 2015) have established a positive relationship between OF and business performance in the manufacturing sector, but this relationship is yet to be confirmed and validated in service contexts (Aranda, 2003; Zhang et al., 2003). Since theory development and its implementation are considered the most prolific research areas in the field of OM (Choi & Wacker, 2011; Ketchen & Hult, 2007; etc.), we seek to further this interest by establishing a relationship between OF and EO in the hospital industry. Further, we argue that OF as an organizational moderator will provide impetus to OM and entrepreneurship scholars to establish the positive impact of EO. Accordingly, the paper's primary contribution to the discipline is to fill and recognize an important gap by investigating how OF moderates the EO and hospital performance relationship. Hence the first major research question of the study pertains to understanding EO dimensionality and its relationship with performance in the hospital industry, and the second question focuses on laying down the role of OF in the EO and hospital performance relationship. We contribute to the literature and provide theoretical reasoning and empirical evidence of the importance of OF in the EO-performance relationship. Specifically, our findings make three contributions to the entrepreneurship literature. First, we answer calls to explore EO in the service context (Martens et al., 2016). Since the seminal studies by Miller (1983), Covin and Slevin (1989, 1991), and Lumpkin and Dess (1996), an increasing number of studies have been conducted on EO and its potential effect on business performance in industrial sectors (Rauch et al., 2009; Wales, Monsen, & McKelvie, 2011). We see few empirical works conducted on entrepreneurship in the hospital industry (Lee & Lim, 2009; Martens et al., 2016; Stewart, Castrogiovanni, & Hudson, 2016; Vecchiarini & Mussolino, 2013). Specifically, Guo (2003) states that “there is lack of research on the use of entrepreneurship as an important and creative technique for dealing with the implementation in healthcare environment” (p. 45). Schlaegel and Koenig (2014) suggest that research focusing on EO development process may enhance its explanatory power and will provide more consistency and theoretical clarity, which is required for its theoretical underpinning. Thus, our study contributes to the theoretical clarity of the EO concept in the services context. Second, we extend the theoretical framework of Miller (1983) to establish innovativeness, proactiveness, and risk-taking as three significant dimensions of EO. Additionally, we discuss their effect on business performance to validate EO findings in hospital settings. Although entrepreneurship literature suggests that EO improves business performance, the empirical results are mixed and differ across different types of environments (Lumpkin & Dess, 1996; Wiklund & Shepherd, 2005). Scholars such as Welter (2011) remark that the effect of entrepreneurship on firms' economic behavior varies in specific contexts that may be social, institutional and spatial. Our results confirm and validate the positive impact of EO on performance and, as expected, we have found that innovativeness and proactiveness have substantial impact on performance, in comparison

to riskiness, in the context of the hospital industry. Third, the paper provides novel contribution to the EO and OM literature by establishing the effect of flexibility in the EO-performance relationship. The role of OF as a moderator in the EO-business performance relationship is specifically examined to extend operations management and entrepreneurship theories. This empirical evidence is valuable as extant literature typically discusses the possible impact of organizational moderators to unearth the EO-performance black box. Chang, Lin, Chang, and Chen (2007) report that EO's relationship with flexibility has not been convincingly documented in the literature. They have also examined the influence of EO on specific flexibility types and did not consider the impact of EO and OF on business performance. Moreover, how EO dimensions influence performance through three dimensions (input, process, and output) of operational flexibility contribute to the business literature. Our results establish the positive role of OF in the EO-performance relationship. For instance, we find that entrepreneurial hospitals characterized by a high aversion to risk and low levels of proactiveness and innovation are more likely to respond to opportunities to meet unarticulated customer needs and fully exploit new ideas in the presence of high levels of OF. Fourth, from a practical point of view, the present paper is important because it provides entrepreneurs insight into their strategic posture, i.e. EO. Knowledge and awareness about operational flexibility and EO could provide insights on enhancing hospital performance. The paper is structured as follows: Section 2 presents the research context. Section 3 introduces the theoretical background of the model, including hypotheses development. The sample and methodology are presented in section 4, while data analysis findings are discussed in section 5. Sections 6 and 7 center, respectively, on results and implications. Limitations and future research opportunities are discussed in the concluding section. 2. Research framework: hospital industry - a landscape of entrepreneurial opportunity The United States (US) hospital industry is considered an appropriate area for our research to understand the relationship between EO, OF, and business performance. The US hospital industry is extensively privatized, unlike the majority of countries where the service sector is either greatly influenced by the government or is entirely public (Folland, Goodman, & Stano, 2001). It constitutes a large part of the healthcare sector - 31% of healthcare spending and 5.6% of GDP (Hartman, Martin, Lassman, & Catlin, 2015). Over the last decade, the hospital industry has faced significant competitive challenges in the form of workforce shortages, increasing health care costs, changing customer needs, increases in technological advances, inequities in access to healthcare, and the shifting of power from physicians to managers, etc. (Adams, 2016; Lee, Lee, & Schniederjans, 2011; Matopoulos & Michailidou, 2013). Research in the field of healthcare services, though, has advanced considerably in recent years; however, challenges to responding to an increasingly competitive environment persist. We argue that to build competitive advantage, fast, differentiated, and innovative services need to be developed in the form of managerial and entrepreneurial resources in hospitals. However, relatively little attention has been focused on entrepreneurial initiatives undertaken in this industry (Ratten, 2012). Jambulingam, Kathuria, and Doucette (2005) remark that successful development and delivery of services to sustain competitive advantage depends upon EO of the managers. Looking into these issues, we believe that EO of hospital administrators can help in overcoming challenges and providing proactive and innovative services to internal and external customers in order to sustain competition. Recently, Chahal, Gupta, and Lonial (2018) have revealed that OF is important for strengthening hospital performance. Hence, we also argue that OF can have substantial effect on the EO-performance relationship. 155

Journal of Business Research 105 (2019) 154–167

H. Chahal, et al.

Input (INF)

Output (OTF)

Operaonal Flexibility (OF)

Innovativeness (INV)

Riskiness (RK)

Process (PRF)

Financial Performance (FP)

Hospital Performance (HP)

Entrepreneurial Orientaon (EO)

Non-Financial Performance (NFP)

Proactiveness (PR)

Fig. 1. Research framework.

implementation of strategic initiatives (p. 198). However, the majority of the scholars have considered Miller's perspective to define a firm's EO using proactiveness, innovativeness, and riskiness dimensions.

3. Theoretical framework and hypotheses development The focal point of this research is to investigate the significance and roles of EO and OF in the hospital industry. The following section discusses hypotheses development in the context of EO dimensionality: the influence of EO on hospital performance and the role of OF in strengthening the EO-performance relationship (Fig. 1).

3.1.1. Proactiveness, innovativeness and riskiness dimensions Because of a scarcity of literature on EO, there is uncertainty regarding dimensions of entrepreneurship and their role in the service sector (Krause, 2011; Sundbo, 2007). Dobon and Soriano (2008) conclude that there is a general lack of entrepreneurship literature that specifically concentrates on the service sector. Various dimensions of EO are proposed in the entrepreneurship literature. Among these, risktaking, proactiveness, and innovativeness, given by Miller (1983), are the most prominent and best researched (Rauch et al., 2009). Scholars have also considered competitive aggressiveness; autonomy (Lumpkin & Dess, 1996; Venkatraman, 1989a, 1989b); adaptability (Salunke, Weerawardena, & McColl-Kennedy, 2013); and corporate venturing and self-renewal (Antoncic & Hisrich, 2001) to measure entrepreneurial intensity. However, these have not gained much acceptance with their operationalizations (Krause, 2011). The majority of scholars consider EO as a combination of three dimensions: innovativeness, proactiveness, and risk-taking (e.g., Covin & Slevin, 1989; Wiklund, 1999; Zahra & Garvis, 2000). A growing body of literature suggests that each of these three sub-dimensions may make a unique contribution to the entrepreneurial nature of a firm (Lumpkin & Dess, 1996). For example, Kreiser, Marino, and Weaver (2002) state that entrepreneurs might tend to be moderate in their willingness to take risks but quite high in their inclination toward innovation. They remark that these findings raise serious concerns regarding the use of aggregated measures of EO and necessitate a review of each sub-dimension and its potential contribution to a firm's EO (p. 74). Similar to previous studies in the service sector, we consider EO as a multi-dimensional construct comprising proactiveness, innovativeness, and risk-taking in the hospital industry. The level of proactiveness within a firm refers to processes which are aimed at seeking new opportunities, introduction of new products and brands ahead of competition, and strategically eliminating operations that are in mature or declining stages of the life cycle (Venkatraman, 1989b). Simply put, it focuses on looking for new opportunities, in accordance with customers'

3.1. Entrepreneurial orientation The entrepreneurial orientation (EO) literature has experienced a boom in recent years, both in its theoretical development and its empirical application, thus giving rise to a vast body of knowledge (Rauch et al., 2009; Vega-Vazquez et al., 2016). Miller (1983), the pioneer in EO field, states that an entrepreneurial firm is one that “engages in product market innovation, undertakes somewhat risky ventures and is first to come up with ‘proactive’ innovations, beating competitors to the punch” (p. 771). In other words, a firm that continually renews, innovates, and constructively takes risks in its markets and areas of operation is considered to be entrepreneurially oriented in nature (Miller & Friesen, 1982). On the other hand, Lumpkin and Dess (1996) relate the concept of EO with organizational processes, practices, and decision-making activities that lead to the development and delivery of new and innovative services and differentiate the organization from others in the market. Similarly, Hitt, Ireland, Camp, and Sexton (2001) mention that combining existing resources in new ways to develop and commercialize new products or move into new markets to service existing and new customers characterize the EO nature of a firm. The EO concept is quite rich and well explored in the context of the manufacturing sector. However, only a few scholars (e.g., Bhuian, Menguc, & Bell, 2005; Davis, Marino, Aaron, & Tolbert, 2011; Hinz & Ingerfurth, 2013; Krause, 2011; Vecchiarini & Mussolino, 2013) have examined EO in the context of the healthcare sector. Different conceptualizations exist to define EO. For instance, Bhuian et al. (2005) described EO as an organizational capability that has a modifying impact on market intelligence processing competence, whereas Davis et al. (2011) conceptualize it as a firm-level concept that captures perceptions of top managers who govern the formation and 156

Journal of Business Research 105 (2019) 154–167

H. Chahal, et al.

future need, to stay ahead of the competition (Rowley, Baregheh, & Sambrook, 2011). Innovativeness involves the use of new technology in new products/services and reflects product innovation (Rowley et al., 2011). It is defined as the “willingness to support creativity and experimentation in introducing new products/services, and novelty, technological leadership and R&D in developing new processes” (Lumpkin & Dess, 2001, p. 431). Risk-taking is one of the managers' disciplines to invest resources in an unfamiliar industry. It involves implementing bold actions that require investment of considerable resources yet provide no certainty about obtaining profits (Lumpkin & Dess, 1996). Further, a debate has raged over dimensionality of the measure (Covin & Lumpkin, 2011; Lumpkin & Dess, 1996; Zahra, 1993a) and interdependence of the sub-dimensions (Dess, Lumpkin, & McGee, 1999; Kreiser et al., 2002; Lumpkin & Dess, 1996). Issues regarding dimensionality are centered on whether to consider EO as an aggregated, uni-dimensional measure (consistent with Covin & Slevin, 1989) or a multi-dimensional measure (e.g., Krause, 2011; Kreiser et al., 2002; Lumpkin & Dess, 1996). These studies confirm that while risk-taking, proactiveness, and innovativeness are interrelated, they are indeed separate dimensions of EO in small and medium enterprises. More recently, Anderson, Kreiser, Kuratko, Hornsby, and Eshima (2015) re-conceptualize EO as a bi-dimensional construct comprising two interchangeable dimensions: the first includes proactiveness and innovativeness characteristics that reflect entrepreneurship behaviors, and the second centers on managerial attitude toward risk. Both are essential to the existence of EO. Further, there is a debate on the validity of the EO scale and the context in which it is operational. For instance, Kreiser et al. (2002) have raised concerns about psychometric properties of the measures. Being context specific, Gartner (1988) states that entrepreneurship research needs to acknowledge the context in which entrepreneurship takes place. This view is also supported recently by Welter (2011), who remarks that context is important for understanding when, how, and why entrepreneurship happens. According to Morris, Webb, and Franklin (2011), EO research has not yet adapted to differences in entrepreneurship across contexts. As such, extant studies on EO have not captured the essence of the specific forms of entrepreneurship within specific settings, particularly those outside real, for-profit contexts. While examining the impact of EO on performance, Lumpkin and Dess (1996) argue that “dimensions of EO may vary independently of each other in a given context” (p. 136). Based on this backdrop, this study presents EO as a multidimensional phenomenon in which dimensions (risk-taking, proactiveness, and innovativeness) represent independent predictors and may occur in different combinations and co-vary in the hospital industry. Hence:

performance implications of EO are considered context-specific, i.e., dependent upon internal organizational and external environmental characteristics. While examining the impact of EO on performance, Welter (2011) states that economic behavior of an organization can be better understood (when framed) within specific contexts. Although a plethora of cross-sectional studies on EO is available, little research has examined the impact of EO on performance in services and, specifically, in the healthcare sector (Hinz & Ingerfurth, 2013; Krause, 2011; Vecchiarini & Mussolino, 2013). Hinz and Ingerfurth (2013) and Krause (2011) have established the positive relationship between EO dimensions (risk-taking, proactiveness, and innovativeness) and performance in the service industry. Further, Krause (2011) finds innovativeness as the most crucial sub-dimension among the three, that most influences firm performance. Hence, we propose that EO and its three dimensions have a positive impact on both financial and non-financial measures of hospitals. Specifically: H2. There is a positive relationship between EO and (i) overall hospital performance; (ii) financial performance; and (iii) non-financial performance. H2a. Innovativeness followed by proactiveness and risk-taking are differentially related to (i) overall hospital performance; (ii) financial performance; and (iii) non-financial performance. 3.1.3. Operational flexibility (OF) as a moderator OF is considered a valuable organizational capability in overcoming unpredictable environmental conditions that may relate to technology, services/products, etc., by using organizational resources and capacities to adapt to changing conditions. In general parlance, OF refers to a firm's ability to cope with or react to environmental uncertainties, external as well as internal. Previous research suggests that firms with adequate flexibility enjoy several advantages that contribute to higher performance (e.g. De Toni & Tonchia, 1998; Jain et al., 2013; Tiwari, Tiwari, & Samuel, 2015; Yu et al., 2015). In the context of EO's relationship with flexibility, Li, Zhao, Tan, and Liu (2008) remark that entrepreneurially oriented firms, with the help of strategic flexibility, can introduce innovative practices better than traditional firms by reducing uncertainty and risk in fast-changing environments. Hence, we argue that the use of OF by entrepreneurial firms can help strengthen the relationship between EO and performance. That is, a high degree of OF in competitive environments can provide entrepreneurs with access to various alternative strategic actions to meet the dynamic needs of internal and external customers. Such strategic operations can be particularly beneficial to highly entrepreneurial firms, since EO focuses on proactive and innovative actions that involve much uncertainty and risk. We consider OF as a moderator between EO and performance. Unlike specific flexibility types studied by scholars such as Swafford, Ghosh, and Murthy (2006); Chen and Paulraj (2004); Koste et al. (2004); Zhang et al. (2003); and Gupta and Somers (1996), etc., in the OM literature, we used an OF construct developed by Chahal et al. (2018), based on the Sawhney (2006) transformation model, to define operational flexibility. Primarily because it is a global and comprehensive framework, this construct overcomes various limitations on the measurement of flexibility. For instance, Sawhney (2006) highlights various conflicting approaches to classify flexibility dimensions, i.e., the same flexibility types but comprising different dimensions and identical flexibility terms representing different flexibility types are used by scholars, which adds to the measurement issue. He further added that all flexibility dimensions discussed in the literature are embedded in hierarchical conceptualizations and move in one direction only, i.e., from lower to higher levels within the same organization. Please refer to the literature review and meta-analytic studies such as Yu et al. (2015); Jain et al. (2013); Zhang et al. (2003); Beach et al. (2000); and Vokurka and O'Leary-Kelly (2000), etc. Considering these limitations,

H1. Proactiveness, innovativeness, and risk-taking are three distinct dimensions of EO. H1a. Proactiveness and innovativeness are better EO predictors than risk-taking. 3.1.2. EO and hospital performance Current literature reviews and statistical meta-analyses, such as Rauch et al. (2009) and Saeed, Yousafzai, and Engelen (2014) find that EO has positive effects on a firm's performance when it captures the entrepreneurial practices of risk-taking, proactiveness, and innovativeness. Further, Lumpkin and Dess (1996) caution that when using a multi-dimensional construct of firm performance, EO may have a positive effect on one dimension (such as new product development), and an adverse effect on another dimension (such as short-run profitability). Nevertheless, the predominant evidence indicates positive correlations between EO and firm performance (Covin & Slevin, 1991; Wiklund, 1999). At the same time, the literature reveals empirical results that are different in different types of external environments (Lumpkin & Dess, 2001; Shan, Song, & Ju, 2016; Wiklund & Shepherd, 2005). The 157

Journal of Business Research 105 (2019) 154–167

H. Chahal, et al.

Sawhney (2006) proposed a unifying theory of flexibility, using value chain typology to comprehend operational flexibility. He identified reactive, proactive, and concurrent uses of flexibility to cope with the short-term and long-term needs of the organization. He referred to this process as transformation framework, comprising inputs, processes and outputs. He also suggested an ongoing exchange of flexibilities between creating opportunities and countering uncertainties along the value chain, which if consistently monitored, results in enhanced performance based upon input, process, and output flexibilities. He defines input flexibility as the firm's responsiveness to the desired changes in its service delivery process. The ability of a firm to handle a wide range of activities relating to labor, equipment, routing, expansion, etc., to meet the needs and requirements of its customers reflects process flexibility. Output flexibility refers to demands placed on firms by their customers. Based on this typology, Chahal et al. (2018) have recently developed and validated OF as a global measure in the hospital industry. According to Chahal et al. (2018), OF represents the process-driven capability to cope with or react to environmental uncertainties, using input, process, and output flexibility types. We argue that high OF, in terms of input, process, or output, can facilitate EO and help in quickly identifying, accessing, and mobilizing internal and external resources. Further, using OF, firms with high EO can develop new routines, competencies, and technologies that result in new combinations of productive factors for meeting organizational needs. Further, entrepreneurial firms equipped with OF will be in a better position to detect new trends and asymmetries in a market faster than firms lacking in such capabilities. Such firms ultimately will be in a better situation to overcome risky ventures and competitive pressures through their innovative and proactive activities. Hence,

Table 1 Sample profile of hospitals. Particulars

Number

Percentage

Types of the hospitals i) Multi-hospital system-affiliated ii) Community hospital iii) Major teaching hospitals iv) Minor teaching hospitals v) Non-teaching vi) Non-government/non-profit vii) Investor owned/for-profit vii) Member of an alliance hospital viii) Standalone hospital ix) General medical x) Specialized hospitals

86 26 06 64 28 06 14 68 20 130 22

56.6 17.1 65.3 28.6 6.1 15.9 22.7 54.8 16.0 85.5 14.5

Number of distinct services offered by a hospital i) 0–10 ii) 11–20 iii) 21–30 iv) Above 31

42 40 14 68

27.6 18.5 9.2 44.7

Number of licensed beds in a hospital i) 15–50 ii) 51–100 iii) 101–200 iv) 200–500 v) Above 500

42 32 42 22 14

2.6 46.5 27.6 14.57 9.2

of the constructs are given in Table 2. 4.2.1. Entrepreneurial orientation A scale with seven indicators, which the study adapts from Morris and Paul (1987); Covin and Slevin (1989); and Matsuno, Mentzer, and Ozsomer (2002), and which previous studies have validated, measures EO. The EO construct comprises three dimensions: Riskiness (EOR), Innovativeness (EOI), and Proactiveness (EOP). These dimensions may vary independently or co-vary to describe a firm as entrepreneurial. The dimensions, along with item details and their respective values, are given in Table 3.

H3. The relationship between EO and hospital performance is stronger for hospitals with high OF. H3a. The relationship between EO and hospital performance is stronger for hospitals with high (i) input flexibility, (ii) process flexibility, and (iii) output flexibility. 4. Research methods

4.2.2. Operational flexibility The scale developed by Chahal et al. (2018) is used to measure OF. The scale comprises three dimensions: input flexibility, process flexibility, and output flexibility. The scale items used are given in Table 3.

4.1. Sample and data collection To test our hypotheses, we randomly selected 800 healthcare facilities from a list of 1500 healthcare facilities provided by the American Health Association. We used Dillman's (2011) Total Design Method to manage our data collection. The initial mailing included a cover letter explaining the purpose of the study, an unmarked questionnaire, and a business reply envelope. The second mailing consisted of a reminder post card, which requested the respondent complete and return the questionnaire if they had not already done so. The third mailing consisted of the second wave of questionnaires, along with cover letters and return envelopes. A total of 170 organizations returned the questionnaire; however, only 152 questionnaires could be used for our analysis due to missing data. The usable questionnaires represent a 20% response rate, which is considered adequate for studies in which senior executives are respondents (see George, Wiklund, & Zahra, 2005; Gupta & Lonial, 1998). The sample profile of hospitals, encompassing information on the types of hospitals contacted, number of distinct services offered, and bed capacity, is given in Table 1.

4.2.3. Hospital performance Financial and non-financial measures of performance are used to measure hospital performance (Li, Benton, & Leong, 2002; Raju, Lonial, Gupta, & Ziegler, 2000). Hospital performance is considered a bi-dimensional construct comprising financial performance (profitability, ROI, and ROA) and non-financial performance (clinical quality, customer satisfaction, and responding to patients' problems and their requests) (Table 3). 5. Data analysis The data analysis of this study follows a two-step procedure: assessing measurement models using confirmatory factor analysis (CFA), followed by assessing path relationships using structural equation modeling (SEM) (Anderson & Gerbing, 1988). The statistical software AMOS 6.0 was employed and the Maximum Likelihood estimation method was used. The model fit was assessed using χ2/df, goodness-offit index (GFI), comparative fit index (CFI), normed fit index (NFI) Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). The threshold for χ2/df should be < 3.0, and respective bad fit indices for RMSEA and SRMR should be < 0.08 and 0.05, while values of GFI, CFI, NFI, and TLI should be over 0.90.

4.2. Measurement scales The current study relies on previous research for items to measure key constructs examined. The measurements of EO, OF, and hospital performance (HP) variables use 5-point Likert-type scales, with 1 as “strongly disagree” and 5 as “strongly agree.” The descriptive statistics 158

Journal of Business Research 105 (2019) 154–167

H. Chahal, et al.

Table 2 Descriptive statistics and validity results (Note: *values in the diagonal of correlation matrix are the square root of AVE). Dimensions

Mean

S.D

Skewness

Kurtosis

Entrepreneurial orientation (EO) Innovation Proactive Riskiness Operations flexibility (OF) Input Process Output Hospital performance (BP) Financial performance Non-financial performance

3.26

0.71

−0.61

−0.16

3.36 3.49 3.03 3.51 3.41 3.78 3.39 3.89 3.72 4.12

0.86 0.76 0.88 0.50 0.74 0.55 0.69 0.54 0.61 0.65

−0.47 −0.52 −0.17 −0.96 −0.92 −1.36 −1.06 −0.32 −0.30 −0.49

−0.69 0.13 −0.54 2.23 1.14 3.02 2.56 0.43 0.27 0.11

Innovation

Proactive

Riskiness

0.80* 0.60 0.59

0.77* 0.50

0.85*

0.70 0.13 0.03

0.15 0.15 0.12

0.48 0.19

0.61 0.35

5.1. Measurement model analysis

Input Flex.

Process Flex.

Output Flex.

0.10 0.50 0.30

0.86* 0.51 0.27

0.86* 0.24

0.75*

0.39 0.09

0.16 0.24

0.18 0.15

0.14 0.18

Financial Perf.

0.66* 0.50

Non-financial perf.

0.92*

performance. The three measurement models (EO, OF, and HP) offer support for convergent validity, as all the overall goodness-of-fit indices demonstrate a proper fit of the hypothesized relationships to the data. The majority of the factor and item loadings are high (> 0.7) and significant (Anderson & Gerbing, 1988) (Table 3). The results exhibit a proper fit of the measurement models to the data and high standardized loadings significant at p < .01. Further, average variance extracted (AVE) estimates for the measures range from 0.63 to 0.68 (Table 2). Overall composite reliability coefficients for EO, OF, and HP are recorded as

We used CFA with the maximum likelihood estimation procedure to examine the psychometric properties of EO, OF and HP scales. Table 2 displays overall and dimension-wise descriptive statistics of the three measurement subsets. The first set included three dimensions of EO: innovativeness, proactiveness and riskiness. The second set comprised three dimensions of OF: input flexibility, process flexibility and output flexibility. The third set (hospital performance) included financial and non-financial Table 3 Measurement models and measures. Measurement models and measures

Entrepreneurial orientation (CR = 0.93, AVE = 0.67, Cronbach Alpha = 0.87) Innovativeness (CR = 0.69, AVE = 0.64, Cronbach Alpha = 0.73) We value creative new solutions more than the solutions of conventional wisdom when it comes to problem solving. Top managers encourage the development of innovative marketing strategies, knowing well that some will fail. Proactiveness (CR = 0.67, AVE = 0.59, Cronbach Alpha = 0.89) We firmly believe a change in market creates a positive opportunity for us. Everyone in our hospital tends to talk more about opportunities rather than problems. Riskiness (CR = 0.77, AVE = 0.74, Cronbach Alpha = 0.68) We value the orderly and risk-reducing management process much more highly than leadership initiatives for change. Top managers in our hospital like to “play it safe.” Top managers in our hospital like to implement plans only if they are very certain the plans will work. Goodness-of-fit statistics: χ2/df = 1.571 (42), GFI = 0.97; NFI =0.96; CFI = 0.98; TLI = 0.97; RMSEA = 0.062 Operations flexibility (CR = 0.96, AVE = 0.68, Cronbach Alpha = 0.84) Input flexibility (CR = 0.79, AVE = 0.75, Cronbach Alpha = 0.89) Suppliers' ability to respond to our request for changes in order mix. Suppliers' ability to respond to our request for changes in volume. Suppliers' ability to respond to our request for changes in delivery time. Suppliers' ability to respond to our request for changes in service modifications. Process flexibility (CR = 0.79, AVE = 0.74, Cronbach Alpha = 0.79) Ability of our employees to handle a range of tasks. Ability of our processes to perform procedures on patients in varied sequences. Ability to expand capacity through overtime and/or temporary hiring. Output flexibility (CR = 0.65, AVE = 0.57, Cronbach Alpha = 0.80) Ability to produce a wide range of service lines within the minimum planning period used by the hospital. Ability to introduce new and/or modifying existing services within the minimum planning period used by the hospital. Ability to shorten service times for procedures. Ability to produce varying levels of output at a profit within the minimum planning period used by the hospital. Goodness-of-fit statistics: χ2/df = 2.636 (41), GFI = 0.893; NFI = 0.893; CFI = 0.916; TLI = 0.888; RMSEA = 0.108. Hospital performance (CR = 0.93, AVE = 0.66, Cronbach Alpha = 0.79) Financial performance (CR = 0.78, AVE = 0.54, Cronbach Alpha = 0.95) Net profits. Return on investments. Return on assets. Non-financial performance (CR = 0.80, AVE = 0.76, Cronbach Alpha = 0.84) Clinical quality. Customer (patient) satisfaction. Responding to patient requests. Responding to patient complaints. Goodness-of-fit statistics: χ2/df = 1.770 (14), GFI = 0.96, NFI =0.95; CFI = 0.98; TLI = 0.97; RMSEA = 0.071.

159

CR

SRW

7.791 Constrained

0.659 0.889

7.146 Constrained

0.724 0.734

11.104 13.471 Constrained

0.783 0.934 0.830

Constrained 14.666 11.742 10.193

0.865 0.914 0.787 0.717

7.482 7.456 Constrained

0.776 0.769 0.684

6.952 8.671 7.284 Constrained

0.619 0.846 0.650 0.741

8.094 7.230

0.822 0.647 0.599

5.229 12.520 11.655

0.527 0.758 0.957 0.866

Journal of Business Research 105 (2019) 154–167

H. Chahal, et al.

0.93, 0.96, and 0.93, respectively. Dimension-wise, the composite reliability values of the sub-scales range from 0.67 to 0.77 (EO); 0.65 to 0.79 (OF), and are arrived at 0.78 and 0.80 for financial and non-financial performance measures respectively. All the overall and dimension-wise Cronbach alpha values of the three scales are > 0.70, excluding the riskiness dimension, which shows a 0.68 value. All the values suggest satisfactory internal consistency. To satisfy the requirement of the discriminant validity, the square root of a construct's AVE must be higher than the correlations between the constructs and the other ones in the model (Hair, Anderson, Tatham, & Black, 2003; Malhotra & Dash, 2010). The study evaluated the discriminant validity of all the measurement scales, the result of which is shown in Table 2. As shown in the table, no correlation estimates are greater than the square root of AVE, which establishes satisfactory discriminant validity in the study.

SRW of the financial and non-financial measures are 0.95 and 0.69, suggesting robust predicting power. 5.2. Control variables Control variables are used to account for potential influences that might drive the dependent variable from different levels. The current study uses four control variables: type of hospital, measured in terms of multi-hospital system affiliated or non-system affiliated; the number of services offered; number of beds; and total experience. 5.3. Common method bias Since our data were collected via a mail survey and relies on selfreported data from single informant, common method bias was a concern. We tested for common method bias as suggested by Podsakoff and Organ (1986) and Cote and Buckley (1987). We conducted the Harman's one-factor test (Podskoff and Organ, 1986), a technique often adopted by researchers to examine common method bias. All variables of the EO, OF, and HP constructs were entered into an exploratory factor analysis. The results revealed that no single factor emerged from this analysis, nor was there a general factor which could account for the majority of variance in these variables: the first factor accounted for only 14.51% of the total variance. Thus, this indicates that common method bias is not a major problem in this study. Further, based upon the procedure given by Cote and Buckley (1987), three competing models were examined. In the first model (i.e., trait only), all the indicators of EO, OF and HP were loaded on a single latent factor. All the indicators were loaded on their respective construct in the second model (i.e., method only). And the third model (i.e. trait and method only) employed a common latent factor linking all the indicators used in the second model. Comparison of these three models indicate that model 2 (χ2/df = 2.867, NFI = 0.720, CFI = 0.795, RMSEA = 0.111) and model 3 (χ2/df = 3.209, NFI = 0.685, CFI = 0.756, RMSEA = 0.121) are superior to model 1 (χ2/df = 3.064, NFI = 0.697, CFI = 0.770, RMSEA = 0.117), and that model 3 is not substantially better than model 2. This indicates that common method bias is not an issue in the present study.

5.1.1. Entrepreneurial orientation For second order EO model confirmation, we tested alternative factor structures to understand the nature of EO in the hospital industry. Single factor structure (all items loading on a single factor), the two-factor structure comprising riskiness and proactiveness-innovativeness, three-dimensional factor structure (innovativeness, proactiveness and riskiness), and co-variance factor structure were examined for dimension validation. Following this, structural model fitness using chi-square test and fit indices are examined to confirm the factor structure. The model fit indices reveal acceptance of second order and covariance models that provide similar fitness and are close to the recommended threshold values. Both models are consistent with EO literature. The chi-square ratios (χ2/df = 1.571) in these models are aligned with the adequate model fitness with minimum discrepancy (Byrne, 1989). The other fit indices (GFI = 0.970, NFI = 0.968, CFI = 0.988, TLI = 0.977, RMSEA = 0.062, SRMR = 0.0311) suggest that both models fit the data satisfactorily. Dimension-wise, innovativeness and proactiveness dimensions envisage EO more in comparison to riskiness in hospitals. Items of all three dimensions are loaded heavily on their posited constructs with standardized regression weights (SRW) > 0.5. However, the predictive nature of the dimensions is stronger for the multidimensional model in comparison to the covariance model. Hence, the second order multi-dimensional EO model is used for further analysis. The idea that EO dimensions tend to vary independently or co-vary support findings of both schools of thought, given by Covin and Slevin (1989) and Lumpkin and Dess (1996). The Cronbach alpha value of the overall scale is calculated as 0.87.

6. Study results Based on the CFA results discussed previously, Hypothesis 1 is supported, as all three EO dimensions are distinct and significant in predicting EO in the second order three-dimensional model. Among the three dimensions, we find innovativeness (SRW = 0.96, CR = 5.52) and proactiveness (SRW = 0.86, CR = 6.20) to more strongly predict EO than riskiness (SRW = 0.75, CR = 6.20). Hence, hypothesis 1a is also accepted. Hypothesis 2, stating the positive relationship between EO and HP including financial and non-financial performance, is examined using SEM. The overall fit indices reveal the model to be a robust fit, with values such as χ2/df = 2.199, RMSEA = 0.089, NFI = 0.887, CFI = 0.934 (Table 3). The results show that all three dimensions (innovation, proactiveness, and riskiness) significantly predict EO (Critical ratio values > 1.96). Further, the magnitude of SRW (0.81) reflects the overall relationship between EO and HP to be quite robust (Table 4). To support these results, we also used summated measures of EO, financial performance, non-financial performance and overall hospital performance to confirm the EO-performance relationship, using hierarchical multiple regression. All relationships are positive and statistically significant with beta values of 0.58 (financial performance measure), 0.31 (non-financial performance measure), and 0.52 (overall hospital performance), thus supporting hypothesis 2 (Table 5). Further, we confirm the relationship of individual EO dimensions with hospital performance. The three beta values are positive and significant between innovativeness (0.86), proactiveness (0.98), and riskiness (0.71, p = .00)

5.1.2. Operational flexibility OF is used as a moderator in the EO-hospital performance relationship. The obtained fit indices ranged from good to robust fit values. The second measurement model, OF, exhibits a good overall fit to the data (GFI = 0.893, NFI = 0.893, CFI = 0.916, TLI = 0.888, RMSEA = 0.104, SRMR = 0.0590), despite a significant chi-square (χ2 = 108.076; df = 41), which might be expected given the sensitivity of the test statistic to sample size (Bagozzi & Yi, 1988). The chi-square ratio (χ2/df = 2.636) is aligned with the adequate fit for minimum discrepancy. The SRW values of the input flexibility (0.86, p = .005), process flexibility (0.64, p = .003) and output flexibility (0.44, p < .01) are significant. All sub-scale items are loaded heavily on their respective posited constructs with a value > 0.50. The results established OF as a second order scale, which possesses robust psychometric properties (Table 2). 5.1.3. Hospital performance The third measurement model—second order hospital performance, comprising financial and non-financial performance measures—shows good fit values (GFI = 0.972, NFI = 0.971, CFI = 0.989, TLI = 0.979, RMSEA = 0.064, SRMR = 0.0338) with a chi-square ratio (χ2/ df = 1.612) aligned with the adequate fit (Byrne, 1989). The respective 160

Journal of Business Research 105 (2019) 154–167

H. Chahal, et al.

Table 4 SEM results for entrepreneurial orientation and hospital performance relationship. Relationships

Critical ratio

Beta value

Entrepreneurial orientation - hospital performance Innovativeness We value creative new solutions more than the solutions of conventional wisdom when it comes to problem solving. Top managers encourage the development of innovative marketing strategies, knowing well that some will fail. Proactiveness We firmly believe a change in market creates a positive opportunity for us. Everyone in our hospital tends to talk more about opportunities rather than problems. Riskiness We value the orderly and risk-reducing management process much more highly than leadership initiatives for change. Top managers in our hospital like to “play it safe”. Top managers in our hospital like to implement plans only if they are very certain the plans will work. Financial performance Net profits. Return on investments. Return on assets. Non-financial performance Clinical quality. Customer (patient) satisfaction. Responding to patient requests. Responding to patient complaints. Goodness-of-fit statistics: χ2/df = 2.199 (60), GFI = 0.884, NFI = 0.887; CFI = 0.934; TLI = 0.914; RMSEA = 0.089.

4.700

0.805 0.858 0.654 0.881 0.889 0.714 0.761 0.714 0.781 0.929 0.838 0.954 0.868 0.647 0.527 0.695 0.590 0.808 0.956 0.888

7.475 1.0 8.929 1.0 11.134 13.477 1.0 1.0 7.661 6.177 1.0 9.305 10.769 10.131

Similarly, to test the effects of individual dimensions of OF, hierarchical regression analysis is performed with overall hospital performance as a dependent variable, with input flexibility, process flexibility, and output flexibility as moderators, and EO as the independent variable (Table 6). Similar to overall OF results, we found that the three flexibility dimensions—input, process, and output—moderate the EO and HP relationship. Further, increased change in the explanatory power is observed in all the models. To better understand the nature of EO, relationships of individual EO dimensions with overall hospital performance are also examined. The results indicate positive and significant influence of innovativeness and proactiveness on hospital performance, while riskiness has positive but insignificant impact on performance. Results also show the moderating effect of OF between overall hospital performance and two EO dimensions—innovativeness and proactiveness. Last, to support hierarchical regression analysis results, moderation was examined using SEM (Table 7). The sample was split into two groups (low and high) at median level (3.60) of OF, and structural model was re-estimated (Hewett & Bearden, 2001). Two models were estimated: (i) path coefficients between EO and hospital performance were constrained to be equal across the two groups (i.e., constrained model); and (ii) path coefficients between EO and hospital performance were allowed to vary freely (i.e., unconstrained model). A highly significant chi-square difference (Δχ2 (12) = 26.14) signifies a much better fit for the unconstrained model, thus indicating that the relationship between EO and HP is different in the two groups. In the unconstrained model (low OF group), the EO and HP relationship is positive and significant (path coefficient = 0.714, p < .05), and in the high OF group, the same relationship is positive and significant (path coefficient = 0.809, p < .05). Results indicate that OF moderates the EO-HP relationship.

and hospital performance, leading to the acceptance of hypothesis 2a. The role of OF as a moderator between EO and hospital performance is tested using multiple regression and structured equation modeling methods. The relationship is analyzed for both overall and dimensionwise constructs. We used moderated hierarchical regression analysis (Cohen and Cohen, Cohen, West, & Aiken, 1983) with a median-centric procedure for the independent and moderating variable to minimize multicollinearity (Aiken & West, 1991). The variance inflation factor (VIF) values are below 3 in all the estimated models, suggesting that multicollinearity is not an issue in our analysis. In Table 5, we provide results for a regression model with EO as an independent variable and financial performance (FPa), non-financial performance (NFPb) and overall hospital performance (HPc) as dependent variables, and four control factors, namely type of hospital, number of services, number of beds and total experience in the healthcare industry. Model 1 contains only control variables. Model 2 adds effects of EO to model 1; model 3 adds direct effects of EO and OF; and model 4 adds interaction effects of EO and OF. In model 2, consistent with literature results, we find a positive effect of EO on hospital performance (0.52, p = .00), including financial (0.58 p = .00) and non-financial (0.31, p = .000) performances, and the EO variable explains variance of 0.30, 0.25, and 0.29 respectively for Model 2a, 2b and 2c. In model 3, the addition of OF increased the explained variance to 0.33 (Model 3a), 0.27 (Model 3b), and 0.32 (Model 3c), suggesting that operational flexibility positively affects performance. The main effects of OF are positive and significant for all the measures of hospital performance. To test the interaction effects of EO and OF (Model 4), we note that interaction further improves the explanatory power of the models—0.027 (Model 4a), 0.04 (Model 4b), and 0.22 (Model 4c). Hence, hypothesis 3 is accepted. The f-square effect sizes ranged between 0.03 and 0.5 in the models with significant change in f statistics (< 0.02). To understand the nature of interaction, we plotted the effects of EO on overall hospital performance and individually on financial performance and non-financial performance for high and low levels of operational flexibility, as shown in Fig. 2a, b and c, respectively. As the plots suggest, EO's relationship with overall hospital performance is stronger at higher levels of operational flexibility in comparison to lower levels of operational flexibility. The corresponding plots for financial performance (Fig. 2b) and non-financial performance (Fig. 2c) also show that the EO relationship with performance measures is stronger at high levels of operations flexibility. These findings provide support for hypothesis 3a.

7. Implications This study has significant academic and research implications. First, our study finds that all three components of EO—innovativeness, proactiveness and riskiness—are significant in constituting EO, thus validating the findings of the studies, namely Zahra and Garvis (2000); Wiklund (1999); Covin and Slevin (1989); and Miller (1983), etc., in the hospital industry. Among the three dimensions, proactiveness is the most significant contributor. Individually, as well, proactiveness yields larger values for overall, financial, and non-financial performance 161

162

2 2 RAB RA 2 1 RAB

Sig of change in F-statistics

Change in F-statistics

Effect size (f 2 ) =

Change in Adj R2

R Adj R2 F-statistics (p value)

2

Intercept

Interaction effect Entrepreneurial orientation × operational flexibility

Moderator Operational flexibility

Direct effect Entrepreneurial orientation

Experience (Current hospital)

Experience (Healthcare industry)

Hospital size (Number of beds)

Hospital size (Number of services)

Control variables

(p

(p

(p

(p

3.772 (SE = 0.193) 0.011 −0.017 0.384 (p0.820)

−0.087 (SE 0.001) 0.318) 0.044 (SE 0.000) 0.612) 0.010 (SE 0.050) 0.912) −0.072 (SE 0.041) 0.424) (p

(p

(p

3.862 (SE = 0.166) 0.183 0.160 8.007 (p0.000)

−0.130 (SE 0.001) 0.099) −0.326 (SE 0.000) 0.000) 0.250 (SE 0.043) 0.003) −0.058 (SE 0.035) 0.479) (p

3.817 (SE = 0.159) 0.063 0.036 2.388 (p0.054)

−0.125 (SE 0.001) (p 0.141) −0.157 (SE 0.000) (p 0.062) 0.147 (SE 0.041) (p 0.095) −0.076 (SE 0.034) (p 0.391) (p

(p

(p

(p

(p

(p

(p

(p

0.000

0.000

2.75

0.127

0.470 13.40

3.949 (SE = 0.159) 0.275 0.249 10.760 (p0.000)

0.312 (SE 0.043) (p 0.000)

−0.129 (SE 0.001) 0.084) −0.372 (SE 0.000) 0.000) 0.197 (SE 0.041) 0.013) −0.008 (SE 0.034) 0.921)

NFP(b)

3.942 (SE = 0.161) 0.327 0.303 13.792 (p0.000)

0.579 (SE 0.043) (p 0.000)

−0.085 (SE 0.001) 0.239) −0.042 (SE 0.000) 0.561) −0.090 (SE 0.042) 0.233) 0.021 (SE 0.034) 0.779)

FP (a)

HP(c)

FP (a)

NFP(b)

Model 2 (Direct effects)

Model 1 (Control variables)

Table 5 Hierarchical regression results using operational flexibility as a moderator. (Note: FP - financial performance; NFP – non-financial performance; HP - hospital performance).

10.73 [Model 2 minus Model 1] 0.000

0.370

3.945 (SE = 0.137) 0.316 0.292 13.126 (p 0.000)

0.519 (SE 0.037) (p 0.000)

−0.123 (SE 0.000) (p 0.091) −0.234 (SE 0.000) (p 0.002) 0.057 (SE 0.035) (p 0.453) 0.008 (SE 0.029) (p. 914)

HP(c)

(p

(p

(p

(p

0.018

12.44

0.040

4.012 (SE = 0.161) 0.353 0.326 12.830 (p0.000) 0.309

0.167 (SE 0.042) (p 0.018)

0.563 (SE 0.043) (p 0.000)

−0.086 (SE 0.001) 0.227) −0.032 (SE 0.000) 0.649) −0.116 (SE 0.041) 0.122) 0.005 (SE 0.034) 0.952)

FP (a)

Model 3 (Moderator)

(p

(p

(p

(p

0.029

2.01

0.034

4.012 (SE = 0.159) 0.299 0.269 10.024 (p0.000) 0.109

0.160 (SE 0.042) (p 0.029)

0.297 (SE 0.042) (p 0.000)

−0.130 (SE 0.000) 0.078) −0.363 (SE 0.000) 0.000) 0.171 (SE 0.041) 0.029) −0.024 (SE 0.033) 0.760)

NFP(b)

0.008

10.25 [Model 3 minus Model 1]

4.012 (SE = 0.136) 0.350 0.322 12.641 (p 0.000) 0.286 [Model 3 minus Model 1] 0.052

0.189 (SE 0.036) (p 0.008)

0.500 (SE 0.036) (p 0.000)

−0.124 (SE 0.000) (p 0.082) −0.223 (SE 0.000) (p 0.002) 0.027 (SE 0.035) (p 0.716) −0.011 (SE 0.029) (p 0.887)

HP(c)

(p

(p

(p

(p

0.009

1.33

0.050

0.201 (SE 0.028) (p 0.009) 3.971 (SE = 0.159) 0.384 0.353 12.457 (p0.000) 0.05

0.231 (SE 0.044) (p 0.002)

0.601 (SE 0.043) (p 0.000)

−0.068 (SE 0.000) 0.332) −0.069 (SE 0.000) 0.331) −0.118 (SE 0.041) 0.109) 0.055 (SE 0.034) 0.471)

FP (a)

(p

(p

(p

(p

0.159

1.82

0.014

0.114 (SE 0.028) (p 0.159) 3.990 (SE = 0.159) 0.309 0.309 8.940 (p0.000) 0.06

0.197 (SE 0.044) (p 0.011)

0.318 (SE 0.043) (p 0.000)

−0.120 (SE 0.000) 0.105) −0.384 (SE 0.000) 0.000) 0.170 (SE 0.041) 0.030) −0.005 (SE 0.034) 0.954)

NFP(b)

Model 4 (Direct Effects & Moderator)

0.018

1.11 [Model 4 minus Model 2]

0.183 (SE 0.024) (p 0.018) 3.980 (SE = 0.135) 0.344 0.344 12.014 (p 0.000) 0.052 [Model 4 minus Model 2] −0.009

0.248 (SE 0.037) (p 0.001)

0.535 (SE 0.036) (p 0.000)

−0.107 (SE 0.000) (p 0.126) −0.257 (SE 0.000) (p 0.000) 0.026 (SE 0.035) (p 0.730) 0.035 (SE 0.029) (p 0.647)

HP(c)

H. Chahal, et al.

Journal of Business Research 105 (2019) 154–167

Journal of Business Research 105 (2019) 154–167

H. Chahal, et al.

a: Moderating effect of OF on the EO– HP relationship

b: Moderating effect of OF on the EO– FP relationship

c: Moderating effect of operational flexibility on the EO-NFP relationship Fig. 2. a: Moderating effect of OF on the EO–HP relationship. b: Moderating effect of OF on the EO– FP relationship. c: Moderating effect of operational flexibility on the EO-NFP relationship.

models. The findings imply that hospitals are keen to take proactive actions and generate innovations in service procedures and operations which impact hospital performance. This interest in proactiveness and innovation may be due to fierce competition in the US hospital industry, reflecting hospital administrators' need to offer customers new opportunities for fast and effective treatments and also their need to ensure the impact of new changes in hospital procedures in overcoming risks. Second, the study supports the positive impact of EO on hospital performance (financial and non-financial), as established by the metaanalytical results in the literature, namely Rauch et al. (2009) and Rosenbusch, Rauch, and Bausch (2013). Third, the study offers a novel extension to the understanding of EOperformance by establishing the role of OF as a moderator. It contributes to the EO-performance framework by bringing forward the role of OF to enhance this relationship in the hospital industry, as considered by Li et al. (2008). The study reveals that the impact of EO on financial and non-financial performance of hospitals is higher in the presence of higher levels of OF. Our study concludes that for hospitals, both EO and OF are instrumental in enhancing their level of performance. Although a higher level of EO shows greater innovativeness and proactiveness to deliver maximum satisfaction to internal and external customers, these activities also entail significant uncertainties and risks, which can easily be overcome using OF. The efficient execution of OF by leveraging input flexibility, process flexibility, and output flexibility by hospitals can pave the way for competitive results. A high level of OF is critical for rapid response to current market needs and preferences through/by using an adequate degree of input flexibility (centering

upon order mix, volume mix, delivery time, and service modifications, etc.) and process flexibility (centralizing upon multi-tasking employees, multi-tasking equipment, effective human resource policies service modifications, timely innovations, etc.). Such efforts can result in meeting customers' needs in a better manner through a range of service lines and rapid innovations in service lines (i.e., modifications in the lines), service procedures (i.e., shortened), etc. A high level of OF complements with high uncertainties and risks of service business operations in hospitals, which enhances performance benefits of their entrepreneurial efforts through proactive and innovative initiatives. Further, our research examines whether and how OF may ensure that the positive effects of EO are fully actualized. Hence, a significant implication of this study is that hospital managers can design strategies that encourage efforts to develop simultaneously high levels of entrepreneurial orientation and operational flexibility in hospitals. The present study informs top management about the importance of input flexibility, process flexibility, and output flexibility in fully actualizing EO's performance potential. Without management's pursuit of OF application, the conversion of EO into superior performance can be underrated. Thus, it follows that top management should develop flexibility skills among employees to respond the customers quickly and effectively. There are implications for policymakers, too. That is, they can promote design education and training programs to help service entrepreneurs understand how to develop higher levels of EO and OF and consequently enhance hospital performance, i.e., knowledge and awareness about the EO and OF dimensions will provide insights on enhancing hospital performance to them.

163

164

2 2 RAB RA 2 1 RAB

0.054

0.063 0.036 2.388 (p 0.054)

Input

0.054

0.063 0.036 2.388 (p 0.054)

Process

3.817 (SE = 0.159)

0.054

0.063 0.036 2.388 (p 0.054)

Output

10.73 0.000

0.370

0.370 10.73 0.000

0.316 0.292 13.126 (p 0.000)

Process

3.990 (SE = 0.138)

0.071 (SE 0.039) (p 0.038)

0.288 (SE 0.039) (p 0.000)

0.025 (SE 080) (p 0.071) −0.119 (SE 049) (p 0.001) 0.000 (SE 038) (p 0.641) 0.015 (SE 0.031) (p 848)

10.73 0.000

0.370

0.316 0.292 13.126 (p 0.000)

Output

3.984 (SE = 0.137)

0.052 (SE 0.039) (p 0.056)

0.293 (SE 0.039) (p 0.000)

0.013 (SE 0.083) (p 0.114) −0.130 (SE 048) (p 0.003) 0.002 (SE 0.039) (p 0.593) 0.024 (SE 0.030) (p 0.809)

9.54 0.038

0.337 0.309 11.931 (p 0.000) 0.027 0.032

Input

9.38 0.056

0.334 0.305 11.769 (p 0.000) 0.026 0.027

Process

3.977 (SE = 0.136)

0.099 (SE 0.039) (p 0.026)

0.298 (SE 0.038) (p 0.000)

−0.017 (SE.082) (p 0.066) −0.146 (SE 047) (p.002) −0.012 (SE 039) (p 0.567) 0.030 (SE 0.030) (p 0.985)

9.70 0.026

0.340 0.312 12.096 (p 0.000) 0.027 0.036

Output

0.076 (SE 0.040) (p.029) 3.970 (SE = 0.138)

0.087 (SE 0.040) (p 0.04)

0.298 (SE 0.039) (p 0.000)

0.042 (SE 0.080) (p.075) −0.129 (SE 049) (p.000) 0.027 (SE 0.031) (p.637) 0.002 (SE 0.038) (p.569)

Input

Output

Input

Process

HP

2.35 0.029

0.350 0.318 10.776 (p 0.000) 0.026 0.020

Input

2.73 0.018

1.73 0.026

0.363 0.331 11.393 (p 0.000) 0.039 0.036

Output

0.011 (SE 0.041) (p 0.026) 3.940 (SE = 0.135)

0.097 (SE 0.040) (p 0.003)

0.302 (SE 0.040) (p 0.000)

−0.017 (SE 082) (p 0.096) −0.150 (SE 049) (p 0.000) −0.012 (SE 0.039) (p 0.553) 0.032 (SE 0.031) (p 0.545)

Output

0.342 0.309 10.395 (p 0.000) 0.017 0.012

Process

0.094 (SE 0.042) (p 0.018) 3.964 (SE = 0.136)

0.086 (SE 0.041) (p 0.025)

0.293 (SE 0.039) (p 0.000)

0.022 (SE 081) (p 0.169) −0.147 (SE 0.048) (p 0.002) 0.007 (SE 0.038) (p 0.570) 0.039 (SE 0.031) (p 0.990)

Process

Model 4 (Direct effects & moderator)

HP

Model 3 (moderator) Operational flexibility dimensions

0.316 0.292 13.126 (p 0.000)

Input

3.945 (SE = 0.137)

0.301 (SE 0.039) (p 0.000)

0.037 (SE 0.081) (p 0.091) −0.137 (SE 0.048) (p 0.002) 0.007 (SE 0.039) (p 0.453) 0.025 (SE 0.031) (p 0.914)

HP

HP

0.067 (SE 0.096) (p 0.141) −0.126 (SE 0.057) (p 0.042) 0.064 (SE 0.045) (p 0.095) −0.028 (SE 0.035) (p 0.391)

Model 2 (Direct effects)

Model 1 (Control variables)

(Note: HP - hospital performance).

Change in F-statistics Sig of change in F-statistics

Effect size (f 2 ) =

Change in Adj R2

R2 Adj R2 F-statistics (p value)

Interaction effect EO*input/EO*process/ EO*output Intercept

Moderator Input/process/output

Direct effect Entrepreneurial orientation (EO)

Hospital size (Number of services) Hospital size (Number of beds) Experience (Healthcare industry) Experience (Current hospital)

Control variables

Table 6 Hierarchical regression results with input/process/output as moderators.

H. Chahal, et al.

Journal of Business Research 105 (2019) 154–167

Journal of Business Research 105 (2019) 154–167

H. Chahal, et al.

importantly, flexibility. On the other hand, Bernardes and Hanna (2009)), argued that flexibility capability refers to an inherent property of the system allowing it to change within pre-established parameters, whereas agility provides for rapid system reconfiguration when faced with unforeseen and unexpected changes. Hence, how these two types of flexibility impact the EO-performance relationship needs to be studied in the future?

Table 7 Moderation results based on SEM. Chi-square

Unconstrained Constrained Difference

328.839 354.986 26.147

Degree of freedom

124 136 12

SRW Low

High

0.714 0.799

0.809 0.758

References

8. Limitations and directions for future research

Abdelilah, B., El Korchi, A., & Balambo, M. A. (2018). Flexibility and agility: Evolution and relationship. Journal of Manufacturing Technology Management, 29(7), 1138–1162. Adams, A. (2016). Collaborating in an evolving health care system—Opportunities for redesigning health care delivery. Journal of the American Psychiatric Nurses Association, 22(1), 62–69. Aiken, L. S., & West, S. G. (1991). Multiple regression testing and interpreting interactions. London: Sage. Anderson, B. S., Kreiser, P. M., Kuratko, D. F., Hornsby, J. S., & Eshima, Y. (2015). Reconceptualizing entrepreneurial orientation. Strategic Management Journal, 36(10), 1579–1596. Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411–423. Antoncic, B., & Hisrich, R. D. (2001). Intrapreneurship: Construct refinement and crosscultural validation. Journal of Business Venturing, 16(5), 495–527. Aranda, D. A. (2003). Service operations strategy, flexibility and performance in engineering consulting firms. International Journal of Operations & Production Management, 23(11), 1401–1421. Avlonitis, G. J., & Salavou, H. E. (2007). Entrepreneurial orientation of SMEs, product innovativeness, and performance. Journal of Business Research, 60(5) (566-57). Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. Beach, R., Muhlemann, A. P., Price, D. H. R., Paterson, A., & Sharp, J. A. (2000). A review of manufacturing flexibility. European Journal of Operational Research, 122(1), 41–57. Bernardes, E. S., & Hanna, M. D. (2009). A theoretical review of flexibility, agility and responsiveness in the operations management literature: Toward a conceptual definition of customer responsiveness. International Journal of Operations & Production Management, 29(1), 30–53. Bhuian, S. N., Menguc, B., & Bell, S. J. (2005). Just entrepreneurial enough: The moderating effect of entrepreneurship on the relationship between market orientation and performance. Journal of Business Research, 58, 9–17. Byrne, B. M. (1989). A Primer of LISREL. Basic Applications and Programming for Confirmatory Factor Analytic Models. New York: Springer-Verlag. Chahal, H., Gupta, M., & Lonial, S. (2018). Operational flexibility in hospitals: Scale development and validation. International Journal of Production Research, 56(10), 3733–3755. Chang, S. C., Lin, R. J., Chang, F. J., & Chen, R. H. (2007). Achieving manufacturing flexibility through entrepreneurial orientation. Industrial Management & Data Systems, 107(7), 997–1017. Chen, I. J., & Paulraj, A. (2004). Towards a theory of supply chain management: The constructs and measurement. Journal of Operations Management, 22(2), 119–150. Choi, T. Y., & Wacker, J. G. (2011). Theory building in the OM/SCM field: Pointing to the future by looking at the past. Journal of Supply Chain Management, 47, 8–11. Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (1983). Multiple regression/correlation analysis for the behavioral sciences. Hillsdale, NJ: Earlbaum. Cote, J. A., & Buckley, M. R. (1987). Estimating trait, method, and error variance: Generalizing across 70 construct validation studies. Journal of Marketing Research, 24, 315–318. Covin, J. G., & Lumpkin, G. T. (2011). Entrepreneurial orientation theory and research: Reflections on a needed construct. Entrepreneurship Theory and Practice, 35(5), 855–872. Covin, J. G., & Slevin, D. P. (1989). Strategic management of small firms in hostile and benign environments. Strategic Management Journal, 10(1), 75–87. Covin, J. G., & Slevin, D. P. (1991). A conceptual model of entrepreneurship as firm behavior. Entrepreneurship: Critical Perspectives on Business and Management, 3, 5–28. Davis, J. A., Marino, L. D., Aaron, J. R., & Tolbert, C. L. (2011). An examination of entrepreneurial orientation, environmental scanning, and market strategies of nonprofit and for-profit nursing home administrators. Nonprofit and Voluntary Sector Quarterly, 40, 197–211. De Toni, A., & Tonchia, S. (1998). Manufacturing flexibility: A literature review. International Journal of Production Research, 36(6), 1587–1617. Dess, G. G., Lumpkin, G. T., & McGee, J. E. (1999). Linking corporate entrepreneurship to strategy, structure, and process: Suggested research directions. Entrepreneurship Theory and Practice, 23(3), 85–102. Dillman, D. A. (2011). Mail and internet surveys: The tailored design method–2007 update with new internet, visual, and mixed-mode guide. New York: John Wiley & Sons. Dobon, S. R., & Soriano, D. R. (2008). Exploring alternative approaches in service industries: The role of entrepreneurship. The Service Industries Journal, 28(7), 877–882. Field, J. M., Victorino, L., Buell, R. W., Dixon, M. J., Goldstein, S. M., Menor, L. J., ... Zhang, J. J. (2018). Service operations: What’s next? Management Faculty Publications. Paper, 367https://digitalcommons.usu.edu/manage_facpub/367. Folland, S., Goodman, A. C., and Stano, M. (2001), “Government regulation-principal regulatory mechanisms”, Folland, S., A. C., Goodman, and M. Stano. (eds.). The

This work offers a novel attempt to advance the understanding of the role of OF and could be considered a stepping stone for a better understanding of how firms can translate their entrepreneurial posture into stronger market and competitive positions. It shows that an alignment of high levels of EO and OF can produce differential performance outcomes for hospitals. We acknowledge several limitations, whose consideration may offer rich potential for researchers looking to advance the understanding of EO and OF, particularly in service contexts. First, since the current model has been tested in the US hospital industry, future research can offer a rich context to test the impact of EO and OF in other service and manufacturing sectors of developed and developing countries. These countries may possess unique and varied contextual elements that may allow for additional insights and theory development. Moreover, an important question that needs answering concerns the generalizability of findings, especially as to whether the hospital industry is influenced by the three EO dimensions (proactiveness, innovativeness, and risktaking). Moreover, in addition to these three dimensions, the role of degree of autonomy and aggressiveness (Lumpkin & Dess, 1996) can be explored. The effects of these dimensions on hospital performance and on the financial and non-financial performance deserve further research. It would be interesting to see whether these dimensions have a separate, different, or additional effect on EO-OF-performance relationships. Further, to broaden our understanding of the role of OF in facilitating outcomes of EO activities, additional research is required to clarify the extent to which OF and its three dimensions (input, process, and output) are beneficial to entrepreneurial ventures across different performance outcomes, such as marketing, etc., in different industry and country contexts. Second, results of the current study imply that entrepreneurs in service contexts should become more entrepreneurially oriented and more flexible. However, development of these capabilities is not necessarily straightforward or intuitive. They take time and come at a cost. Research that can directly assist hospitals to overcome these barriers is needed. In this context, research needs to consider trade-offs that entrepreneurs may need to make when considering how best to invest in different strategic orientations and organizational capabilities. Third, methodologically, this study relied on self-reported data from single informants. Although the Harman Single Factor model and competing models results indicated that common method bias is not a major problem in this study, the interpretation of the findings must be viewed in light of this limitation. Fourth, the cross-sectional nature of our data demands caution when drawing inferences, because relationships we examine may be susceptible to a time factor. Fifth and last, the paper considers an interplay between EO and OF in enhancing hospital performance. However, the role of agility as a strategic concept can also be investigated to understand the EO-hospital performance relationship in future research. Although both agility and flexibility have been used interchangeably in the literature, recently researchers have made attempts to distinguish the two. Abdelilah, El Korchi, and Balambo (2018) synthesized the relationship between the two capabilities and argued that agility is a long-term strategic capability which encompasses responsiveness, cost, quality, and, 165

Journal of Business Research 105 (2019) 154–167

H. Chahal, et al. economics of health and health care, Prentice Hall Inc. pp. 445–469. Gartner, W. B. (1988). “Who is an entrepreneur?” is the wrong question. American Journal of Small Business, 12(4), 11–32. George, G., Wiklund, J., & Zahra, S. A. (2005). Ownership and the internationalization of small firms. Journal of Management, 31(2), 210–233. Goodale, J. C., Kuratko, D. F., Hornsby, J. S., & Covin, J. G. (2011). Operations management and corporate entrepreneurship: The moderating effect of operations control on the antecedents of corporate entrepreneurial activity in relation to innovation performance. Journal of Operations Management, 29(1–2), 116–127. Guo, K. (2003). “Applying entrepreneurship to health care organizations”. New England Journal of Entrepreneurship, 6(1), 45. Gupta, Y. P., & Lonial, S. C. (1998). Exploring linkages between manufacturing strategy, business strategy, and organizational strategy. Production and Operations Management, 7(3), 243–264. Gupta, Y. P., & Somers, T. M. (1996). Business strategy, manufacturing flexibility, and organizational performance relationships: A path analysis approach. Production and Operations Management, 5(3), 204–233. Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (2003). Multivariate data analysis with readings. New Jersey: Prentice Hall. Hartman, M., Martin, A. B., Lassman, D., & Catlin, A. (2015). National health spending in 2013: Growth slows, remains in step with the overall economy. Health Aff (Millwood), 34(1), 150–160. Hewett, K., & Bearden, W. O. (2001). Dependence, trust, and relational behavior on the part of foreign subsidiary marketing operations: Implications for managing global marketing operations. Journal of Marketing, 65(4), 51–66. Hinz, V., & Ingerfurth, S. (2013). Does ownership matter under challenging conditions? On the relationship between organizational entrepreneurship and performance in the healthcare sector. Public Management Review, 15(7), 969–991. Hitt, M. A., Ireland, R. D., Camp, S. M., & Sexton, D. L. (2001). Strategic entrepreneurship: Entrepreneurial strategies for wealth creation. Strategic Management Journal, 22(6–7), 479–491. Hsu, C.-C., Tan, K. C., Jayaram, J., & Laosirihongthong, T. (2014). Corporate entrepreneurship, operations core competency and innovation in emerging economies. International Journal of Production Research, 52(18), 5467–5483. Hsu, C.-C., Tan, K. C., Laosirihongthong, T., & Leong, G. K. (2011). Entrepreneurial SCM competence and performance of manufacturing SMEs. International Journal of Production Research, 49(22), 6629–6649. Jain, A., Jain, P. K., Chan, F. T. S., & Singh, S. (2013). A review on manufacturing flexibility. International Journal of Production Research, 51(19), 5946–5970. Jambulingam, T., Kathuria, R., & Doucette, W. R. (2005). Entrepreneurial orientation as a basis for classification within a service industry: The case of retail pharmacy industry. Journal of Operations Management, 23(1), 23–42. Joglekar, N., & Levesque, M. (2013). The role of operations management across the entrepreneurial value chain. Production and Operations Management, 22(6), 1321–1335. Ketchen, D. J., & Hult, T. (2007). Bridging organization theory and supply chain management: The case of best value supply chains. Journal of Operations Management, 25(2), 573–580. Kickul, J. R., Griffiths, M. D., Jayaram, J., & Wagner, S. M. (2011). Operations management, entrepreneurship, and value creation: Emerging opportunities in a crossdisciplinary context. Journal of Operations Management, 29(1–2), 78–85. Koste, L. L., Malhotra, M. K., & Sharma, S. (2004). Measuring dimensions of manufacturing flexibility. Journal of Operations Management, 22(2), 171–196. Krause, R. M. (2011). Symbolic or substantive policy? Measuring the extent of local commitment to climate protection. Environment and Planning C: Government and Policy, 29(1), 46–62. Kreiser, P. M., Marino, L. D., & Weaver, K. M. (2002). Assessing the psychometric properties of the entrepreneurial orientation scale: A multi-country analysis. Entrepreneurship: Theory and Practice, 26(4), 71–95. Lee, S. M., Lee, D., & Schniederjans, M. J. (2011). Supply chain innovation and organizational performance in the healthcare industry. International Journal of Operations & Production Management, 31(11), 1193–1214. Lee, S. M., & Lim, S. (2009). Entrepreneurial orientation and the performance of service business. Service Business, 3(1), 1–13. Li, L. X., Benton, W. C., & Leong, G. K. (2002). The impact of strategic operations management decisions on community hospital performance. Journal of Operations Management, 20(4), 389–408. Li, Y., Zhao, Tan, Y. J., & Liu, Y. (2008). Moderating effects of entrepreneurial orientation on market orientation-performance linkage: Evidence from Chinese small firms. Journal of Small Business Management, 46(1), 113–133. Lumpkin, G. T., & Dess, G. G. (1996). Clarifying the entrepreneurial orientation construct and linking it to performance. Academy of Management Review, 21(1), 135–172. Lumpkin, G. T., & Dess, G. G. (2001). Linking two dimensions of entrepreneurial orientation to firm performance: The moderating role of environment and industry life cycle. Journal of Business Venturing, 16(5), 429–451. Malhotra, N., & Dash, S. (2010). Marketing research: An applied orientation (6th ed.). New Delhi: Pearson Publications. Martens, C. P., Lacerda, F. M., Belfort, A.C. and de Freitas, H.M.R. (2016), “Research on entrepreneurial orientation: Current status and future agenda”, International Journal of Entrepreneurial Behavior and Research, Vol. 22 No. 4, pp. 556 – 583. Matopoulos, A., & Michailidou, L. (2013). Healthcare supply chains: A case study of hospital-vendor collaborative practices. International Journal of Logistics Systems and Management, 15(2), 288–303. Matsuno, K., Mentzer, J. T., & Ozsomer, A. (2002). The effects of entrepreneurial proclivity and market orientation on business performance. Journal of Marketing, 66(3), 18–32. Miller, D. (1983). The correlates of entrepreneurship in three types of firms. Management

Science, 29(7), 770–791. Miller, D., & Friesen, P. H. (1982). Innovation in conservative and entrepreneurial firms: Two models of strategic momentum. Strategic Management Journal, 3(1), 1–25. Morris, M. H., & Paul, G. W. (1987). The relationship between entrepreneurship and marketing in established firms. Journal of Business Venturing, 2(3), 247–259. Morris, M. H., Webb, J. W., & Franklin, R. J. (2011). Understanding the manifestation of entrepreneurial orientation in the nonprofit context. Entrepreneurship Theory and Practice, 35(50), 947–971. Mortara, L., & Parisot, N. G. (2016). Through entrepreneur’s eyes: The fab-spaces constellation. International Journal of Production Research, 54(23), 7158–7180. Nakane, J., & Hall, R. W. (1991). Holonic manufacturing: Flexibility—The competitive battle in the 1990s. Production Planning & Control, 2(1), 2–13. Podsakoff, P. M., & Organ, D. W. (1986). “Self-reports in organizational research: Problems and prospects”. Journal of Management, 12, 69–82. Raju, P. S., Lonial, S. C., Gupta, Y. P., & Ziegler, C. (2000). The relationship between market orientation and performance in hospital industry: A structural equations modeling approach. Health Care Management Science, 3(3), 237–247. Ratten, V. (2012). A theoretical framework of entrepreneurship and innovation in healthcare organization. International Journal of Social Entrepreneurship and Innovation, 1(3), 223–238. Rauch, A., Wiklund, J., Lumpkin, G. T., & Frese, M. (2009). Entrepreneurial orientation and business performance: An assessment of past research and suggestions for the future. Entrepreneurship Theory and Practice, 33(3), 761–787. Rosenbusch, N., Rauch, A., & Bausch, A. (2013). The mediating role of entrepreneurial orientation in the task environment–performance relationship: A meta-analysis. Journal of Management, 39(3), 633–659. Rowley, J., Baregheh, A., & Sambrook, S. (2011). Towards an innovation-type mapping tool. Management Decision, 49(1), 73–86. Saeed, S., Yousafzai, S. Y., & Engelen, A. (2014). On cultural and macroeconomic contingencies of the entrepreneurial orientation–performance relationship. Entrepreneurship Theory and Practice, 38(2), 255–290. Salunke, S., Weerawardena, J., & McColl-Kennedy, J. R. (2013). Competing through service innovation: The role of bricolage and entrepreneurship in project-oriented firms. Journal of Business Research, 66(8), 1085–1097. Sawhney, R. (2006). Interplay between uncertainty and flexibility across the value-chain: Towards a transformation model of manufacturing flexibility. Journal of Operations Management, 24(5), 476–493. Schlaegel, C., & Koenig, M. (2014). Determinants of entrepreneurial intent: A metaanalytic test and integration of competing models. Entrepreneurship Theory and Practice, 38(2), 291–332. Schmenner, R. W., Wassenhove Van, L., Ketokivi, M., Heyl, J., & Lusch, R. F. (2009). Too much theory, not enough understanding. Journal of Operations Management, 27(5), 339–343. Schroeder, R. G. (2008). Introduction to the special issue on theory development in operations management. Production and Operations Management, 17(3), 354–356. Shan, P., Song, M., & Ju, X. (2016). Entrepreneurial orientation and performance: Is innovation speed a missing link? Journal of Business Research, 69(2), 683–690. Shepherd, D. A., & Patzelt, H. (2013). Operational entrepreneurship: How operations management research can advance entrepreneurship. Production and Operations Management, 22(6), 1416–1422. Shepherd, D. A., & Patzelt, H. (2017). Researching at the intersection of innovation, operations management, and entrepreneurship. In A. Dean, Shepherd, & H. Patzelt (Eds.). Trailblazing in entrepreneurship: Creating new paths for understanding the Field (pp. 103–147). Springer. Stewart, S. A., Castrogiovanni, G. J., & Hudson, B. A. (2016). A foot in both camps: Role identity and entrepreneurial orientation in professional service firms. International Journal of Entrepreneurial Behavior & Research, 22(5), 718–744. Sundbo, J. (2007). Innovation and learning in services-the involvement of employees. Advances in services innovations (pp. 131–150). Berlin Heidelberg: Springer. Swafford, P. M., Ghosh, S., & Murthy, N. (2006). The antecedents of supply chain agility of a firm: Scale development and model testing. Journal of Operations Management, 24(2), 170–188. Tiwari, A. K., Tiwari, A., & Samuel, C. (2015). Supply chain flexibility: A comprehensive review. Management Research Review, 38(7), 767–792. Vecchiarini, M., & Mussolino, D. (2013). Determinants of entrepreneurial orientation in family-owned healthcare organizations. International Journal of Healthcare Management, 6(4), 237–251. Vega-Vazquez, M., Cossío-Silva, F. J., & Revilla-Camacho, M. (2016). Entrepreneurial orientation–hotel performance: Has market orientation anything to say. Journal of Business Research, 69(11), 5089–5094. Venkatraman, N. (1989a). Strategic orientation of business enterprises: The construct, dimensionality, and measurement. Management Science, 35(8), 942–962. Venkatraman, N. (1989b). The concept of fit in strategy research: Toward verbal and statistical correspondence. Academy of Management Review, 14(3), 423–444. Victorino, L., Field, J. M., Buell, R. W., Dixon, M. J., Meyer Goldstein, S., Menor, L. J., ... Zhang, J. J. (2018). Service operations: What have we learned? Journal of Service Management. https://doi.org/10.1108/JOSM-08-2017-0192. Vokurka, R. J., & O’Leary-Kelly, S. W. (2000). A review of empirical research on manufacturing flexibility. Journal of Operations Management, 18(4), 485–501. Wales, W., Monsen, E., & McKelvie, A. (2011). The organizational pervasiveness of entrepreneurial orientation. Entrepreneurship Theory and Practice, 35(5), 895–923. Welter, F. (2011). Contextualizing entrepreneurship—Conceptual challenges and ways forward. Entrepreneurship Theory and Practice, 35(1), 165–184. Whetten, D. (1989). What constitutes a theoretical contribution. Academy of Management Review, 14(4), 490–495. Wiklund, J. (1999). The sustainability of the entrepreneurial orientation-performance

166

Journal of Business Research 105 (2019) 154–167

H. Chahal, et al.

in-Time, Activity Based Management, Theory of Constraints, Quality Management, Market Orientation). Dr. Gupta has worked extensively with local companies and published his work in numerous journals including Journal of Operations Management, Decision Sciences Journal, International Journal of Operations and Production Management, International Journal of Production Research, and European Journal of Operational Research, Journal of Operational Research Society, International of Conflict Management, and Journal of Service Marketing.

relationship. Entrepreneurship Theory and Practice, 24(1), 37–48. Wiklund, J., & Shepherd, D. (2005). Entrepreneurial orientation and small business performance: A configurational approach. Journal of Business Venturing, 20(1), 71–91. Yu, K., Cadeaux, J. B., & Luo, B. N. (2015). Operational flexibility: Review and metaanalysis. International Journal of Production Economics, 169, 190–202. Zahra, S. A. (1993a). A conceptual model of entrepreneurship as firm behavior: A critique and extension. Entrepreneurship theory and practice. 16. Entrepreneurship theory and practice (pp. 5–21). Zahra, S. A., & Garvis, D. M. (2000). International corporate entrepreneurship and firm performance: The moderating effect of international environmental hostility. Journal of Business Venturing, 15(5), 469–492. Zhang, Q., Vonderembse, M. A., & Lim, J. (2003). Manufacturing flexibility: Defining and analyzing relationships among competence, capability, and customer satisfaction. Journal of Operations Management, 21, 173–191.

Subhash C. Lonial was an emeritus professor in the marketing department of the College of Business at the University of Louisville. His writings have appeared in Decision Science Journal, Journal of Business Research, Journal of Academy of Marketing Science, Journal of Consumer Psychology, Production and Operations Management, Journal of Advertising Research, and Journal of Service Research. In a long battle with Lung cancer, Dr. Lonial passed away without seeing this work accepted for publication. Whether scholars or not, ‘death is one thing of which we can all be certain’. We, the coauthors, struggled in deciding how to ensure the legacy of Dr. Lonial's work continues without getting bogged down in the realms of socially- and religiously-associated emotions. We all will agree that ‘all scholars alive today will one day no longer be with us’ and thus would like to acknowledge his contribution posthumously to earlier versions of this final manuscript.

Hardeep Chahal is a Professor in the Department of Commerce, University of Jammu, India. Her research interests focus on services marketing with emphasis on consumer satisfaction and loyalty, service quality, brand equity and market orientation. Her research work has been acknowledged in refereed journals like International Journal of Production Research, Journal of Service Theory and Practice, Journal of Strategic Marketing, International Journal of Bank Marketing, Journal of Relationship Marketing, International Journal of Healthcare Quality and Assurance Management Research Review, Total Quality Management and Excellence, and Global Business Review.

Swati Raina is an Assistant Professor, Mittal School of Business, Lovely Professional University, Phagwara, Punjab. She has done her PhD on the topic titled “Antecedents and Consequences of Green Strategic Marketing Orientation in 2016 from Uttarakhand Technical University, Dehradoon, Uttarakhand. She has attended many national and international conferences as well as workshops. Her research is acknowledged in national and international Journals.

Mahesh Gupta is a professor in the management department of the college of Business at the University of Louisville. His areas of expertise and interest include evaluation and improving the organizational performance by using management philosophies (e.g., Just-

167