Exploring the dual effect of effectuation on new product development speed and quality

Journal of Business Research 106 (2020) 82–93

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Exploring the dual effect of effectuation on new product development speed and quality☆

T

⁎

Liang Wua,b, Heng Liub, , Kun Suc a

Center for Cantonese Merchants Research, Guangdong University of Foreign Studies, No. 178 Waihuan Dong Road, Guangzhou 510006, China. Lingnan College, Sun Yat-sen University, No. 135 Xingang Xi Road, Guangzhou 510275, China. c School of Management, Northwestern Polytechnical University, No. 127 Youyi Xi Road, Xi'an 710072, China. b

A R T I C LE I N FO

A B S T R A C T

Keywords: Effectuation New product advantages NPD speed New product quality Competitive intensity

Existing research provides mixed insights concerning the impact of effectuation on new product advantages. We address these contradictions by highlighting the dual nature of the relationship between effectuation and new product advantages. By arguing that effectuation differentially affects two types of new product advantages, namely new product development (NPD) speed and new product quality (NPQ), we hypothesize that effectuation is positively related to NPD speed but has an inverted U-shaped relationship with NPQ. Moreover, we reveal that competitive intensity strengthens the relationship between effectuation and these two types of new product advantages. Using a questionnaire survey of 180 sample firms in China, we test and find strong support for our hypotheses. This study highlights trade-offs in new product advantages associated with effectuation and provides important theoretical and managerial implications.

1. Introduction Developing new products successfully is important for firms' economic performance and growth in a competitive marketplace (Calantone, Randhawa, & Voorhees, 2014; Santos-Vijande, LópezSánchez, & Rudd, 2016), especially for firms in emerging economies such as China, where competitive advantages can be acquired by developing creative and high-quality products within short product development cycles (Wu, Liu, & Zhang, 2017). However, many firms in emerging economies face continuous technical changes and increasingly diverse customer demands, and often lack sufficient technological and human resources (e.g., Zhang & Zhang, 2018), which may impede their ability to cope with uncertainty and complexity during the process of new product development (NPD). Emerging studies suggest that firms' decision-making logic can affect product innovation under conditions of uncertainty (Sarasvathy, 2001, 2008; Sarasvathy & Dew, 2005). In particular, the role of effectuation on NPD is an emerging research focus (e.g., Berends, Jelinek, Reymen, & Stultiëns, 2014; Im, 2013) with mixed findings (Blauth, Mauer, & Brettel, 2014; Coviello & Joseph, 2012; Im, 2013; Roach, Ryman, & Makani, 2016). Effectuation is a type of decision-making

logic that “takes a set of means as given and focuses on selecting between possible effects that can be created with that set of means” (Sarasvathy, 2001, p. 245). Some studies highlight the positive impact of effectuation on NPD (Brettel, Mauer, Engelen, & Küpper, 2012; Im, 2013). For instance, effectuation may help ventures meet innovation requirements by utilizing the means at hand to redesign the goals of innovation (Sarasvathy, 2001) and deal with innovation constraints by setting a level of affordable loss (Futterer, Schmidt, & Heidenreich, 2018). Effectuation also helps in acquiring heterogeneous resources, information, and knowledge by forming alliances or collaborating with third parties (Read, Dew, Sarasvathy, Song, & Wiltbank, 2009), and embracing unexpected external circumstances to cope with changes in consumer demand or changes in financing conditions (Lawton, Rajwani, & Reinmoeller, 2012). However, effectuation usually adopts a means-driven approach that focuses on existing resources and often does not have concrete goals in advance (Sarasvathy, 2001). Thus, engaging in excessive effectuation may result in more time spent to accumulate useful knowledge, which impedes innovation efficiency (Brettel et al., 2012). In addition, effectuation relies heavily on pre-commitments with existing customers and

☆ The authors thank the National Natural Science Foundation of China (71802059, 71672197, 71773088), Key Research Project of Guangdong Province (2016WZDXM001), and Social Sciences Foundation of Guangdong Province (XJZX201713, 2017WQNCX034) for the generous financial support. The authors are grateful to the anonymous reviewers, and JBR editors for their constructive comments on the study. ⁎ Corresponding author at: Lingnan College, Sun Yat-sen University, No. 135 Xingang Xi Road, Guangzhou 510275, China. E-mail addresses: [email protected] (L. Wu), [email protected] (H. Liu), [email protected] (K. Su).

https://doi.org/10.1016/j.jbusres.2019.09.016 Received 30 January 2019; Received in revised form 5 September 2019; Accepted 6 September 2019 0148-2963/ © 2019 Published by Elsevier Inc.

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2. Theory and hypotheses

suppliers, which potentially sets a limit on creativity (Sarasvathy, 2001). This study argues that one of the possible reasons for the inconsistent findings is that prior studies have neglected to distinguish different dimensions of new product advantages. According to previous studies (e.g., Atuahene-Gima & Wei, 2011; Wu et al., 2017), new product advantages can be classified into two important dimensions: new product development speed (NPD speed) and new product quality (NPQ). Distinguishing between these two types of new product advantages is important because they purportedly differ with regard to their logic of defending competition and their diverse capacity requirements. The effectuation logic that a firm leverages can bring different kinds of problem-solving competencies, product-developmentrelated knowledge, and relevant resources from stakeholders, which may have different influences on the two dimensions of new product advantages. Since most existing works tend to lump new product advantages together, the issues of how and why effectuation may differentially affect these two dimensions remain under-examined. Moreover, despite the importance of contextualizing competitive environments to establish boundaries for evaluating the impact of effectuation, little is known about how variations in external environments may influence the effectiveness of effectuation for successfully boosting NPD, and how they may generate useful managerial insights to guide NPD activities. To address these research gaps, this study develops a framework (see Fig. 1) to examine how effectuation affects two different types of new product advantages (i.e., NPD speed vs. NPQ) under the moderating context of competitive intensity, and uses empirical evidence from China to verify the proposed hypotheses. This study makes the following theoretical contributions. First, while previous research mainly focus on the product innovation processes led by effectuation (Berends et al., 2014; Coviello & Joseph, 2012), we specifically investigate how the logic of effectuation differentially affects NPD speed and NPQ. We build on the integration of the decision-making–position framework (Day & Wensley, 1988; Slotegraaf & Atuahene-Gima, 2011), which states that a firm's positional advantages depend on its decision-making logic, as well as the current effectuation literature (Brettel et al., 2012; Roach et al., 2016; Sarasvathy, 2001). We predict that a positive linear relationship exists between effectuation and NPD speed, while an inverted U-shaped relationship exists between effectuation and NPQ. Second, this study identifies competitive intensity as a critical moderator for explaining under what circumstances effectuation affects new product advantages. Most prior research suggests that effectuation is an effective decision-making logic under uncertain conditions (Chandler, DeTienne, McKelvie, & Mumford, 2011; Sarasvathy, 2001). Meanwhile, our work (based on an insight from the contingency view) examines whether the impact of effectuation on new product advantages also depends on competitive intensity. In doing so, this study shows that new product advantage (NPA) may not only be influenced by the firm's decision-making logic but also be contingent upon the conditions of the competitive environment (see Fig. 1).

2.1. Effectuation as a source of NPA in an uncertain environment The decision-making–position framework proposes that a firm's decision-making logic may yield positional advantages (Day & Wensley, 1988; Slotegraaf & Atuahene-Gima, 2011). For instance, a firm's decision-making speed enables it to outperform competing firms through effective implementation and quick adaptation to changes in the market or supply chain requirements, while decision-making flexibility can bring superior value to customers and lower costs versus the competitors' (Fredrickson & Mitchell, 1984), thereby allowing a firm to gain and maintain positional advantage. Recently, several scholars have followed this intriguing framework to examine several antecedents of NPD positional advantages. For example, Slotegraaf and Atuahene-Gima (2011) suggest that decisionmaking comprehensiveness is a critical source of new product advantage, which can generate a larger pool of new NPD ideas by analyzing the behavior of competitors and the underlying needs of customers to develop a more advantageous product. Likewise, Wu et al. (2017) find that bricolage, a resource-construction tactic of capitalizing on resources at hand, is a vital source of advantages. It enables firms to cope with resource constraints by focusing on available resources, refusing to endorse limitations, and utilizing creative recombination to acquire positional advantages. Although these studies shed light by showing the effects of a firm's decision-making behavior on positional advantages, they provide relatively limited insights into which kinds of decision-making logic help firms build positional advantages under conditions of uncertainty and unpredictability. Meanwhile, current effectuation literature suggests that effectuation, as a unique decision-making logic, can help a firm build product advantages in uncertain, or difficult-to-predict environments (Cai, Guo, Fei, & Liu, 2017; Chandler et al., 2011; Roach et al., 2016; Sarasvathy, 2001). For example, in performing an experiment, effectuation can facilitate a venture's iteration, testing, and trial-and-error processes for quickly introducing products in uncertain environments (Im, 2013). Moreover, by embracing the unexpected, effectual thinking enables a firm to accept unplanned contingencies in NPD, be open to alternatives that may not be known at an earlier stage of NPD, and break “out of a preconceived perception or expectation,” which can all boost creativity in NPD (Blauth et al., 2014). Furthermore, with the practice of precommitments, unique strategic resources and information can be acquired through effectuation, by building agreements with suppliers and customers for firms' product innovation processes (Berends et al., 2014). Based on the abovementioned reasoning, effectuation can be a critical source of new product advantages. According to the entrepreneurship and new product development literature, NPA refers to a firm's product outperforming its competitors, driven by the firm's continuous efforts to attain better performance outcomes (Day & Wensley, 1988; Wu et al., 2017). There are many types of NPA. Some scholars highlight that product speed to market, reliability, innovativeness, product differentiation, and cost advantages are all forms of NPAs (e.g., Im & Workman, 2004; Kim & AtuaheneGima, 2010; Zhao, Cavusgil, & Cavusgil, 2014). In this study, through a dual focus on competitors and customers, NPD speed and NPQ are considered as the two most critical dimensions of new product positional advantage. Some scholars also agree that NPD speed and NPQ are two important indicators of a product's competitive advantage (e.g., Atuahene-Gima & Wei, 2011; Wu et al., 2017; Zhang & Wu, 2017). NPD speed, a key reflection of NPA (Wu et al., 2017), refers to “the speed with which new products are developed” (Ganesan, Malter, & Rindfleisch, 2005, P.57). NPD speed is critical because it can increase proficiency in market-entry timing (Langerak, Hultink, & Griffin, 2008), lower development costs (Stanko, Molina-Castillo, & Munuera-Aleman, 2012), enhanced product competitive advantage (Carbonell & Rodriguez, 2006), and improved new product outcomes in terms of

Competitive Intensity H3 +

H4 +

H1 + NPD Speed

Effectuation

U

H2

NPQ

Fig. 1. Conceptual model. 83

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customer base (Sheng, Zhou, & Lessassy, 2013) and financial measures (Cankurtaran, Langerak, & Griffin, 2013). In uncertain environments, to remain competitive, firms should furnish the desired value proposition with less time and before its prime contender does (Wu et al., 2017). NPQ, another key reflection of NPA (Atuahene-Gima & Wei, 2011), refers to “the degree to which a product satisfies customer requirements” (Stanko et al., 2012, p. 753). NPQ is critical because it can offer superior final customer value and better service experience (SantosVijande et al., 2016), help the firm command a price premium (Calantone et al., 2014), determine market share (Gretz & Basuroy, 2013), and affect both present and future product performance, especially when competition intensifies (Molina-Castillo, Munuera-Alemán, & Calantone, 2011). While the improvement of NPD speed and NPQ depends on different competencies, knowledge, and resources, they both play an important role for successful new product performance (Calantone et al., 2014; Santos-Vijande et al., 2016). Thus, firms should consider the means and conditions for acquiring these new product advantages under uncertain conditions. As mentioned above, the latest market information and process knowledge brought about by effectuation (Cai et al., 2017; Chandler et al., 2011; Roach et al., 2016) can impact the speed of NPD, while the problem-solving creativity and related product knowledge brought about by effectuation may affect NPQ (Brettel et al., 2012; Im, 2013). The next section discusses in detail how effectuation affects these two dimensions of NPA.

(Wu et al., 2017), which are essential for providing critical materials, human resources, and technologies for rapid NPD. The flexibility logic of effectuation, which focuses on “doing to the resources they had,” enables quick resource construction (Chandler et al., 2011, p. 382), which in turn facilitates acquiring critical materials and technologies in a timely manner during the NPD process (Kahn et al., 2006). For example, a steel products manufacturing firm acquires timely customer feedback about its prototypes by “having local horse riders try their newly developed stirrup suspender” (Berends et al., 2014, p. 629). Third, NPD speed often depends on refined process knowledge (Wu et al., 2017) that can speed up the prototype development and manufacturing design stages of NPD (Ganesan et al., 2005). Regarding experimentation, effectuation can refine manufacturing and process knowledge by experimenting with existing and emerging product-development processes (Roach et al., 2016) to improve NPD speed. For example, a study of small and medium sized firms (SMEs) shows that effectuation is used during the process of product innovation, specifically by the creative use of existing resources to conduct product innovation in a short time through testing and refinement iterations (Berends et al., 2014). Meanwhile, effectuation can also accelerate the production of a minimum viable product (MVP) in order to quickly test a product's value and growth potential, and then promptly confirm the final version of the new product (Stringham, Miller, Clark, & Clark, 2015). MVP is “a version of (a) product that is complete enough to demonstrate the value it brings to the users” (Moogk, 2012, p. 24). During the process of designing an MVP, a number of tests are required. In experimentation, firms can practice effectuation to creatively utilize available resources and use existing means for novel applications, thereby facilitating the supply of resources for each MVP version's test (Roach et al., 2016). Meanwhile, with regard to partnerships with stakeholders, a firm can also collect the latest information about customer preferences from customers, suppliers, and competitors (Berends et al., 2014; Chandler et al., 2011). This helps in quickly modifying the MVP, which in turn facilitates confirmation of the product's final version. Therefore, we formulate the following hypothesis:

2.2. How effectuation affects NPA This study integrates the decision-making–position framework (Day & Wensley, 1988; Slotegraaf & Atuahene-Gima, 2011) with the literature on effectuation (Brettel et al., 2012; Roach et al., 2016; Sarasvathy, 2001) to understand the relationship between effectuation and the abovementioned two types of NPA. This decision-making–position framework posits that a firm's positional advantages depend on its decision-making logic, which enables it to outperform competing firms by being more efficient or providing superior customer value at a relatively lower cost (Day & Wensley, 1988; Slotegraaf & Atuahene-Gima, 2011). In this framework, effectuation is a vital source of advantages; it can facilitate the cultivation of problem-solving competence and help achieve timely resource construction under unpredictable conditions (Cai et al., 2017; Chandler et al., 2011; Roach et al., 2016). However, others have argued that the focus of effectuation is mostly on existing resources, with no clear goals, specific schedules, or particular financial budgets (Arend, Sarooghi, & Burkemper, 2015), which may have negative effects on innovation efficiency (Berends et al., 2014). Thus, Brettel et al. (2012) speculated that the relationship between effectuation and research and development (R&D) project performance might depend on different kinds of innovation performance. Extrapolating from their expectations, we explore whether effectuation can facilitate NPD speed and improve NPQ differently. NPD speed reflects an advantage in a time-based competition with competitors (Chen, Damanpour, & Reilly, 2010; Wu et al., 2017). We expect that effectuation positively affects NPD speed for the following reasons. First, NPD speed often depends on the latest market information in uncertain environments (Atuahene-Gima & Li, 2004) and requires firms to respond quickly to volatile market demands (AtuaheneGima & Wei, 2011; Chen et al., 2010). With regard to partnership with stakeholders, firms practicing effectuation can build reciprocal relationships with customers, suppliers, and even competitors (Berends et al., 2014; Chandler et al., 2011) to collect information about customer needs and operational requirements in a timely manner (Im, 2013). Meanwhile, with regard to exploitation of contingencies, effectuation can help firms acquire the latest feedback (Brettel et al., 2012). This can accelerate the “idea-screen” and “second-screen” stages of the NPD process (Kahn, Barczak, & Moss, 2006). Second, improving NPD speed depends on fast resource solutions

H1. Effectuation is positively related to NPD speed. NPQ reflects an advantage in providing superior value and better service for customers (Atuahene-Gima & Wei, 2011; Stanko et al., 2012). We explain why effectuation has an inverted U-shaped relationship with NPQ as follows. First, the improvement of NPQ often depends on a firm's problem-solving creativity, which is “the ability to discover and implement novel and cost-effective solutions” (AtuaheneGima & Wei, 2011, p. 83). In the case of pre-commitments, it is expected that the agreements signed with customers can facilitate the collection and combination of knowledge related to customers' present and future needs, which in turn aids in early product definition (Cai et al., 2017). Pre-commitments can also help improve the probability of detecting novel and low-cost solutions and increase the availability of potential solutions related to the design of product features (Sheremata, 2000). Meanwhile, from agreements signed with suppliers, a firm is enlightened as to the technology and product solution trends in its industry (Chandler et al., 2011). This enhances the production of original solutions to problems, and thus, improves a firm's problem-solving creativity in NPD and ensures that new products have higher reliability, longer durability, and more functionality to satisfy customers' needs (Berends et al., 2014). However, effectuation relies heavily on agreements with existing customers and suppliers, as well as the means at hand, and does not have concrete goals (Sarasvathy, 2001). Therefore, adopting excessive effectuation may limit the heterogeneity of knowledge about customers' needs and the latest technological and product solution trends in the industry (Brettel et al., 2012). This will eventually hamper a firm's problem-solving creativity and impair the quality of new products. Second, the improvement of NPQ often rests in refining product 84

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knowledge (Wu et al., 2017). Experimentation focuses on “experimenting with different products,” through which a firm learns to refine its existing product knowledge, thereby facilitating the testing and validation of new products, which in turn can improve NPQ. Meanwhile, experimentation also focuses on “experimenting with business models.” The new customer relationships established during this process can help a firm solicit valuable customer feedback on the concept, product, or market throughout the NPD process (Coviello & Joseph, 2012), which can reduce errors in product development. Nevertheless, given that effectuation depends heavily on experimentation, this may cause a firm to sink into an “endless cycle of failure and unrewarding change” due to excessive exploration (Levinthal & March, 1993, p. 106). Eventually, this may lead to a firm's costs of experimentation outweighing the associated benefits, without gaining enough distinctive competence to ensure the new product's final quality. Therefore, we formulate the following hypothesis:

Table 1 Sample description. N= Firm age (years) ≤3 3–8 8–15 > 15 Number of employees ≤300 300–500 500–2000 2000–5000 > 5000

Percentage

12 33 56 79

6.67% 18.33% 31.11% 43.89%

113 17 34 3 13

62.78% 9.44% 18.89% 1.67% 7.22%

Firm stage Input Growth Mature and stable Decline R&D intensity 0–1% 1–3% 3–10% > 10%

N=

Percentage

6 76 96 2

3.33% 42.22% 53.33% 1.11%

24 59 59 38

13.33% 32.78% 32.78% 21.11%

Firm size (compared with industry rivals) Super-large 15 8.33% Large 50 27.78% Medium 61 33.89% Small 54 30.00%

H2. There is a curvilinear relationship between effectuation and NPQ, such that effectuation initially has a positive effect on NPQ, but this positive influence flattens out and then declines at a high level of effectuation.

Note. N = 180. Table 2 Respondents' statistics.

2.3. The contingent role of competitive intensity Competitive intensity is defined as “the degree of market competition faced by a firm” (Tsai & Hsu, 2014, p. 295). Competitive intensity may create opportunities for firms to develop new high-quality products to gain a market advantage (Tsai & Hsu, 2014). It may also create challenges in that many firms in the industry find it difficult to maintain a sufficient flow of information and resources to develop new products (Moyano-Fuentes & Martínez-Jurado, 2016). Highly competitive environments compel firms to collect the latest information and furnish resource solutions quickly to maintain competitive advantage (Tsai & Yang, 2013). Therefore, highly competitive environments compel firms to rely on effective decision-making logic such as effectuation to utilize resources and reduce uncertainty during NPD. We hypothesize that competitive intensity can enhance the positive effect of effectuation on NPD speed. Intense competition requires a firm to be highly responsive. Effectuation enhances a firm's ability to respond quickly to NPD opportunities in highly competitive environments by improving its problem-solving efficiency (Atuahene-Gima & Wei, 2011; Chandler et al., 2011), providing fast resource solutions (Reymen, Berends, Oudehand, & Stultiëns, 2017; Wu et al., 2017) and enabling the firm to frequently refine its manufacturing and process knowledge (Ganesan et al., 2005; Roach et al., 2016). Moreover, with increased competitive intensity, opportunities and threats are constantly reallocated and customer-based brand equity erodes, resulting in tighter margins and a more pronounced information asymmetry (Lavie, Stettner, & Tushman, 2010; Zhao, Calantone, & Voorhees, 2018). Thus, it may be inefficient to rely on goals-driven decisionmaking logic for NPD (Blauth et al., 2014). Instead, means-driven effectuation becomes a key option for collecting information about customers' latest needs and competitors' actions (Tsai & Yang, 2013) to reduce uncertainty and add workable alternatives during the NPD process. Thus, under intense competition, effectuation can better accelerate NPD. As such, we formulate the following hypothesis:

N=

Percentage

Position Chairman General manager Senior management

57 55 68

31.67% 30.56% 37.78%

Tenure 1–2 years 3 to 8 years 9 to 15 years > 15 years

14 66 65 35

7.78% 36.67% 36.11% 19.44%

Note. N = 180.

Through increased effectuation, the knowledge that relates to customers' present and future needs for early product definition are collected (Cai et al., 2017), providing creative solutions for timely checkup and feedback about product design and quality (Atuahene-Gima & Wei, 2011). Furthermore, under intense competition, product knowledge becomes difficult to collect due to lesser flow of information and resources in the industry (Moyano-Fuentes & Martínez-Jurado, 2016). However, through increased effectuation, refined product knowledge is collected, facilitating the testing and validation of new products (Ganesan et al., 2005) to ensure that the product's characteristics meet the established industry standards. Moreover, under intense competition, time pressure may increase the likelihood of conflicts between the R&D and marketing departments (Tsai & Hsu, 2014), while the flexibility allowed by effectuation enables coordination of these departments' functions to improve NPQ (Chandler et al., 2011). Second, competitive intensity may also amplify the latent adverse effects of excessive effectuation on NPQ. An intensely competitive environment is filled with imitation (Chen, Lin, & Michel, 2010; Cui et al., 2005). In such situations, developing highly stable new products that are differentiated from the competitors' products through effectuation becomes a viable alternative for gaining competitive advantage (Im, 2013). However, market competition may cause less organizational slack and information asymmetry (Lavie et al., 2010; Zhao et al., 2018). This may lead to excessive effectuation and result in heavy reliance on agreements with existing customers and suppliers to acquire heterogeneous knowledge about customers' potential needs (Cai et al., 2017) and the latest technological and product solutions (Brettel et al., 2012) for improving a firm's problem-solving creativity to enhance its NPQ. Furthermore, increased advertising and product offerings emerge in highly competitive environments (Li, Poppo, & Zhou, 2008; Tsai &

H3. The positive relationship between effectuation and NPD speed is strengthened by the competitive intensity. This study proposes that competitive intensity can also enhance the impact of effectuation on NPQ. First, competitive intensity can amplify the positive effects of effectuation on NPQ. Competitive intensity provides opportunities for a firm to develop new high-quality products to gain a competitive advantage. However, price competition may make it more difficult to detect and carry out fresh solutions for new highquality products (Auh & Menguc, 2005; Cui, Griffith, & Cavusgil, 2005). 85

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Table 3 Measures and validation. Factors and items

Loading

Experimentation (EFE) (Alpha = 0.76, CR =0.78, and AVE =0.64) 1. We experimented with different products and/or business models 2. We tried a number of different approaches until we found a business model that worked

0.89⁎⁎⁎ 0.69⁎⁎⁎

Affordable loss (Alpha = 0.92, CR = 0.92, and AVE = 0.80) 1. We were careful not to commit more resources than we could afford to lose 2. We were careful not to risk more money than we were willing to lose with our initial idea 3. We were careful not to risk so much money that the company would be in real trouble financially if things did not work out

0.95⁎⁎⁎ 0.87⁎⁎⁎ 0.85⁎⁎⁎

Flexibility (Alpha = 0.96, CR = 0.96, and AVE = 0.85) 1. We allowed the business to evolve as opportunities emerged 2. We adapted what we were doing to the resources we had 3. We were flexible and took advantage of opportunities as they arose 4. We avoided courses of action that restricted our flexibility and adaptability

0.86⁎⁎⁎ 0.89⁎⁎⁎ 0.97⁎⁎⁎ 0.96⁎⁎⁎

Pre-commitments (Alpha = 0.95, CR = 0.95, and AVE = 0.87) 1. We used a substantial number of agreements with customers, suppliers and other organizations and people to reduce the amount of uncertainty 2. We used pre-commitments from customers and suppliers as often as possible 3. The agreements with customers, suppliers and other organizations and people enable the capture of new opportunities in a varied environment

0.93⁎⁎⁎ 0.91⁎⁎⁎ 0.96⁎⁎⁎

New product quality (NQ) (Alpha = 0.98, CR = 0.97, and AVE = 0.91) 1. In our internal tests, the product performed exactly as it was designed to do 2. The product had little probability of malfunctioning while in use 3. The product's performance characteristics met the established industry standards 4. The expected product's useful life met the required specifications

0.94⁎⁎⁎ 0.89⁎⁎⁎ 0.98⁎⁎⁎ 0.99⁎⁎⁎

NPD Speed (NS) (Alpha = 0.92, CR = 0.93, and AVE = 0.76) 1. Time spent on new product development time was far ahead of our project timeline 2. We developed new products faster than the industry norm 3. We developed new products much faster than we expected 4. We developed new products faster than our typical product development time

0.92⁎⁎⁎ 0.86⁎⁎⁎ 0.94⁎⁎⁎ 0.74⁎⁎⁎

Competitive Intensity (CI) (Alpha = 0.62, CR = 0.68, and AVE = 0.43) 1. There are many “promotion wars” in our industry 2. Whatever one competitor can offer, others can match readily 3. Price competition is a hallmark of our industry

0.45⁎⁎⁎ 0.77⁎⁎⁎ 0.70⁎⁎⁎

Technology Uncertainty (TU) (Alpha = 0.95, CR = 0.95, and AVE = 0.87) 1. Technologies in our industry are changing rapidly 2. It was very difficult to forecast technological developments in our industry 3. Newly developed technologies and processes in our industry can easily become out of date

0.92⁎⁎⁎ 0.93⁎⁎⁎ 0.94⁎⁎⁎

Market turbulence (MT) (Alpha = 0.84, CR = 0.84, and AVE = 0.64) 1. In our kind of business, customers' product preferences change quite a bit over time 2. Our customers tend to look for new products all the time 3. It was very difficult to forecast market change in our industry Model Fit:χ2 (341) =575.86, p < .001, CFI = 0.96, TLI = 0.95, RMSEA = 0.06, and SRMR = 0.05

0.71⁎⁎⁎ 0.83⁎⁎⁎ 0.85⁎⁎⁎

Note. N = 180. ⁎⁎⁎ p < .001. Table 4 Measurement of control variables. Measurement Firm age Less than three years Three to eight years Eight to fifteen years More than fifteen years Firm employees < 300 people 300–500 people 500–2000 people 2000–5000 people > 5000 people

1 2 3 4 1 2 3 4 5

Measurement Firm stage Input stage Growth stage Mature and stable stage Declining stage R&D intensity 0 0%–1% 1%–3% 3%–10% > 10%

Yang, 2013). This helps a firm confirm the goals of NPD (Sarasvathy, 2001) and provides valuable information about NPQ improvement. However, information redundancy (e.g., pricing and promotion information) due to market competition may increase the risk that effectuation becomes excessive and dependent on experimentation (Li et al., 2008). This may cause a firm to sink deeply into a “failure trap” (Levinthal & March, 1993, p. 105), endlessly exploring new product ideas and incorporating components that emerge in the competitive

1 2 3 4 1 2 3 4 5

Measurement Firm size (compared with industry rivals) Small firm Medium firm Large firm Super-large firm Executive position Senior management General manager Chairman

1 2 3 4 1 2 3

environment but without high stability and feasible income source, thereby hampering the firm's competence and financial foundation for improving its NPQ (Arend et al., 2015). Thus, we formulate the following hypothesis: H4. The curvilinear relationship between effectuation and NPQ is strengthened by the competitive intensity.

86

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(Armstrong & Overton, 1977). The results show that questionnaires collected in two batches had no major differences in terms of firm age, firm employees, R&D intensity, and firm stage, implying that firms that responded and did not respond had no significant differences. The t-test comparison between responses by mail and email did not show a significant difference either.

Table 5 Second-order hierarchical measurement model results. Second-order construct

First-order construct

Effectuation

Loading ⁎⁎⁎

Experimentation 0.79 Affordable loss 0.88⁎⁎⁎ Flexibility 0.86⁎⁎⁎ Pre-commitments 0.94⁎⁎⁎ Model Fit:χ2 (50) =157.31, p < .001, CFI = 0.96, TLI = 0.94, RMSEA = 0.11, and SRMR = 0.04

3.2. Measures All scales were based on existing literature. A seven-point Likerttype scale was used, ranging from 1 (“strongly disagree”) to 7 (“strongly agree”). Table 3 shows all items measured. We measured NPD speed based on existing studies (e.g., Ganesan et al., 2005). Following Atuahene-Gima and Wei (2011), we used four items to measure NPQ. Moreover, based on previous studies, we employed fourteen items to measure effectuation (Chandler et al., 2011). Consistent with Jaworski and Kohli (1993), we separately employed three items to measure competitive intensity, market turbulence, and technology uncertainty. The control variables firm age, firm employees (last year's total firm employees), R&D intensity (R&D expenditure to sales ratio during the previous year), firm size (compared with industry rivals), firm stage (core business development stages), and executive position, which affect decision-making and new product introduction (Hambrick, 2007; Ridge, Johnson, Hill, & Bolton, 2017) are displayed in Table 4. We also controlled for market turbulence and technology uncertainty as they affect product innovation (Kim & Atuahene-Gima, 2010; Wu et al., 2017). All items concerning market turbulence and technology uncertainty are displayed in Table 3.

Note. N = 180. ⁎⁎⁎ p < .001.

3. Methodology 3.1. Sample and research setting We referred to previous studies to design the questionnaire (Atuahene-Gima & Wei, 2011; Chandler et al., 2011; Ganesan et al., 2005; Jaworski & Kohli, 1993). These questionnaires were first translated to English, then translated to Chinese, and finally translated back to English to ensure their equivalence (Brislin, 1980). We then consulted with senior managers and sought their advice on the propriety of the questionnaire. We made some amendments based on their recommendations so that our questionnaire reflects the situation faced by firms in China. Subsequently, we surveyed 30 senior managers to implement a pilot test; these responses were removed from the final sample. Based on the pilot test, we carefully amended and improved the questionnaire. We implemented this survey in 2017, obtained the list of firms from the Chinese Ministry of Commerce's local branch, and chose enterprises in Guangdong and Jiangsu provinces. These provinces are two of the top places in China that are dynamically reforming and opening-up and show high levels of entrepreneurship, innovation, and fierce market competition. First, we sent introductory participation request letters to these firms and promised to provide them a feedback report. Second, we called them to ensure they wanted to participate in this survey. For interested firms, we sent our formalized questionnaire by mail (with a return envelope) or by email. Those who sent out the mail or email were PhD students in business studies; they were informed of the relevant background information and taught the necessary survey skills. Finally, 250 firms (out of 1500 samples) returned the questionnaires, while 70 questionnaires were removed due to incompleteness. This translates to a final response rate of 12.00% (180/1500). Sample descriptive statistics and respondent information are listed in Tables 1 and 2, respectively. A t-test was used to check the non-response bias of this study

3.3. Reliability and validity Cronbach's alpha was used to test the inter-item consistency. As Table 3 shows, most alphas exceed the cut-off point of 0.70 (Nunnally, 1978) and the lowest alpha value is 0.62. Confirmatory factor analysis (CFA) was used to estimate the convergent validity. Table 3 shows that most factor loadings exceed the 0.70 criterion (Nunnally, 1978). Only two items' loadings are slightly lower than 0.70, and all items' loading are statistically significant, which is one of the most important criteria for a construct's convergent validity (DiStefano & Hess, 2005). In addition, Table 5 shows that the second-order construct (effectuation) loadings are higher than the 0.70 benchmark, which verifies the convergent validity of this study. Meanwhile, only one construct's average variance extracted (AVE) is slightly lower than 0.50, with a value of 0.43. Other constructs' AVE all surpass the 0.50 level (Fornell & Larcker, 1981). Therefore, these CFA results verify the convergent validity of this study. Confirmatory factor analysis was used to estimate the discriminant

Table 6 Model comparison in CFA analysis. Model factor

χ2

df

△χ2

CFI

TLI

RMSEA

SRMR

Model Model Model Model Model Model Model Model Model Model Model Model Model

919.23 1456.25 1000.43 1024.36 955.05 1092.80 1586.02 964.98 1586.02 1628.65 1110.50 1934.63 1072.48

415 420 420 420 422 424 424 423 424 427 426 428 424

537.01(5)⁎⁎⁎ 81.20(5)⁎⁎⁎ 105.13(5)⁎⁎⁎ 35.81(7)⁎⁎⁎ 173.56(9)⁎⁎⁎ 666.79(9)⁎⁎⁎ 45.75(8)⁎⁎⁎ 666.79(9)⁎⁎⁎ 709.42(12)⁎⁎⁎ 191.27(11)⁎⁎⁎ 1015.40(13)⁎⁎⁎ 153.24(9)⁎⁎⁎

0.91 0.82 0.90 0.90 0.91 0.88 0.80 0.91 0.80 0.79 0.88 0.74 0.89

0.90 0.80 0.89 0.88 0.90 0.87 0.78 0.90 0.78 0.77 0.87 0.72 0.88

0.08 0.12 0.09 0.09 0.08 0.09 0.12 0.08 0.12 0.13 0.09 0.14 0.09

0.09 0.14 0.10 0.10 0.10 0.11 0.15 0.11 0.15 0.15 0.11 0.17 0.13

1 six factor model 2 five factor model (EFE, NS + NQ, CI, TU, MT) 3 five factor model (EFE, NS, NQ, CI + TU, MT) 4 five factor model (EFE, NS, NQ, CI, TU + MT) 5 four factor model (EFE + NS + NQ, CI, TU, MT) 6 four factor model (EFE, NS, NQ, CI + TU + MT) 7 four factor model (EFE, NS + NQ + CI, TU, MT) 8 three factor model (EFE + NS + NQ + CI, TU, MT) 9 three factor model (EFE, NS + NQ + CI, TU + MT) 10 three factor model (EFE, NS + NQ, CI + TU + MT) 11 two factor model (EFE + NS + NQ, CI + TU + MT) 12 two factor model (EFE + NS, NQ + CI + TU + MT) 13 one factor model (EFE + NS + NQ + CI + TU + MT)

Note. N = 180. + indicates factor combined. ⁎⁎⁎ p < .001. 87

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Table 7 Descriptive statistics and correlation matrix. Variables

Mean

SD

1

2

3

4

5

6

7

8

9

10

11

12

1. Firm age 2. Firm employees 3. R&D intensity 4. Firm size 5. Firm stage 6. Executive position 7. Market turbulence 8. Technological uncertainty 9. Competitive intensity 10. Effectuation 11. NPD speed 12. NPQ

3.12 1.81 3.54 2.86 2.52 1.93 4.76 4.02 5.15 5.50 4.77 5.20

0.94 1.23 1.11 0.95 0.58 0.83 1.20 1.38 0.93 0.79 1.24 1.53

0.39⁎⁎ 0.16⁎ −0.28⁎⁎ 0.34⁎⁎ −0.18⁎ −0.05 −0.19⁎ −0.04 0.02 −0.01 −0.01

0.11 −0.50 0.15⁎ −0.39⁎⁎ 0.03 −0.05 −0.05 0.00 0.01 0.01

−0.10 −0.10 −0.14 −0.02 −0.07 −0.02 0.15⁎ 0.21⁎⁎ 0.20⁎⁎

−0.21⁎⁎ 0.34⁎⁎ −0.01 0.03 0.06 −0.07 −0.08 −0.09

−0.14 0.07 −0.06 −0.12 −0.03 −0.13 −0.00

0.01 −0.00 0.05 0.11 0.03 −0.02

0.80 0.68⁎⁎ 0.46⁎⁎ 0.23⁎⁎ 0.17⁎ −0.11

0.93 0.37⁎⁎ 0.15⁎ 0.01 −0.25⁎⁎

0.66 0.28⁎⁎ 0.19⁎ −0.02

0.87 0.50⁎⁎ 0.32⁎⁎

0.87 0.47⁎⁎

0.95

Note. N = 180. The diagonal elements in bold are the square roots of the AVE for constructs measured with multiple items. ⁎ p < .05 (two-tailed). ⁎⁎ p < .01 (two-tailed). Table 8 Results of regression analysis. Variables

Firm age Firm employees R&D intensity Firm size Firm stage Market turbulence Technological uncertainty Executive position

NPD Speed

NPQ

Model 1

Model 2 (H1)

Model 3 (H3)

Model 4

Model 5 (H2)

Model 6 (H4)

0.16⁎ (0.10) 0.18⁎⁎ (0.08) 0.28⁎⁎⁎ (0.09) 0.16⁎⁎ (0.08) 0.24⁎⁎ (0.11) 0.64⁎⁎⁎ (0.16) 0.42⁎⁎⁎ (0.13) 0.03 (0.06)

0.17⁎⁎ (0.09) 0.16⁎ (0.09) 0.23⁎⁎⁎ (0.08) 0.16⁎⁎ (0.08) 0.26⁎⁎ (0.11) 0.48⁎⁎⁎ (0.17) 0.34⁎⁎⁎ (0.13) 0.07 (0.07) 0.37⁎⁎⁎ (0.10)

0.15⁎ (0.09) 0.15⁎ (0.10) 0.20⁎⁎⁎ (0.08) 0.13+ (0.08) 0.24⁎⁎ (0.12) 0.47⁎⁎⁎ (0.16) 0.36⁎⁎⁎ (0.15) 0.05 (0.07) 0.40⁎⁎⁎ (0.12)

−0.18 (0.14) −0.05 (0.11) 0.26⁎ (0.10) −0.17 (0.14) 0.02 (0.21) 0.15 (0.13) −0.38⁎⁎⁎ (0.11) 0.01 (0.15)

0.15⁎ (0.10)

0.17 (0.13) 0.17⁎ (0.11)

−0.24+ (0.13) −0.07 (0.10) 0.23⁎ (0.10) −0.19 (0.13) 0.04 (0.19) 0.07 (0.12) −0.40⁎⁎⁎ (0.10) −0.05 (0.14) 0.43⁎⁎ (0.15) −0.36⁎⁎⁎ (0.10) 0.09 (0.13)

3.23⁎⁎⁎ 0.51 0.35 0.08⁎⁎⁎

2.84⁎⁎⁎ 0.52 0.34 0.01⁎

2.91⁎⁎⁎ 0.12 0.08

5.93⁎⁎⁎ 0.28 0.23 0.16⁎⁎⁎

−0.20 (0.13) −0.08 (0.10) 0.24⁎ (0.10) −0.20+ (0.13) 0.02 (0.19) 0.05 (0.12) −0.39⁎⁎⁎ (0.10) −0.03 (0.14) 0.49⁎⁎ (0.16) −0.34⁎⁎ (0.11) 0.24 (0.16) 0.06 (0.15) −0.26+ (0.15) 5.72⁎⁎⁎ 0.29 0.24 0.01

Effectuation Effectuation squared Competitive intensity Effectuation × competitive intensity Effectuation squared × competitive intensity F R2 Adjusted R2 R2 change

3.53⁎⁎⁎ 0.43 0.30

Note. N = 180. Significance levels based on two-tailed tests for all models and coefficients. Robust standard errors are in the brackets. + p < .10. ⁎ p < .05. ⁎⁎ p < .01. ⁎⁎⁎ p < .001.

validity (Farrell, 2010), for which we chose the indicators of χ2/df, df, CFI, TLI, RMSEA, SRMR (Wang & Takeuchi, 2007). As shown in Table 6, the hypothesized six-factor model has a good fit compared with the other five-, four-, three-, two-, and one-factor models, which indicates there is good discriminant validity between each construct. In addition, Table 7 shows that all AVE surpassed other variables' squared correlation (Hair, Black, Babin, Anderson, & Tatham, 2006), which further confirms that there is good discriminant validity between each construct.

3.4. Common method bias Two methods were used to check common method bias (CMV). First, Harman's one-factor test method was used to assess CMV (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). The results indicate that no one general factor explained most of the covariance among the variables (Podsakoff et al., 2003), signifying no material concerns (Podsakoff et al., 2003). Second, a CFA comparison was performed to test our model (Mossholder, Bennett, Kemery, & Wesolowski, 1998). The results (Table 6) indicate that the one-factor model, where all 88

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suggests that under high levels of competitive intensity, effectuation is an effective way for firms to accelerate the NPD process. As shown in Table 8, control variables were included in Model 4. In Model 5, the coefficient of effectuation on NPQ is positive and marginally significant (β = 0.43; p < .01), the coefficient of effectuation squared on NPQ is negative and significant (β = −0.36; p < .001). This verifies H2, which suggests an inverted U-shaped relationship between effectuation and NPQ. In addition, robustness check results show that when effectuation3 was added in the model, the coefficient of effectuation squared on NPQ is still negative and significant (β = −0.40; p < .05). Meanwhile, the coefficient of effectuation3 on NPQ is not significant (β = −0.02; ns), and the model fit does not improve as compared with Model 5 (ΔR2 = 0.0003, F (1, 167), ns). This further verifies that an inverted U-shaped (not an S-shaped) relationship between effectuation and NPQ exists (Haans, Pieters, & He, 2016; Zhou & Wu, 2010). The interaction term of effectuation squared with competitive intensity was included in Model 6. The coefficient of the interaction term of effectuation squared with competitive intensity is negative and marginally significant (β = −0.26; p < .10). The addition of the interaction term in Model 6 does not significantly increase the R-square value as compared with Model 5 (ΔR2 = 0.01, F (2, 166), ns). This partly provides support for H4, which states that competitive intensity boosts the inverted U-shaped relationship between effectuation and NPQ. To clarify the moderating effects of competitive intensity in H4, we also plotted the interaction in Fig. 3 based on the procedure of Godart, Maddux, Shipilov, and Galinsky (2015). Fig. 3 illustrates that an upward trend exists for the relationship between effectuation and NPQ at lower levels of effectuation, while a downward trend exists at higher levels of effectuation. In addition, this plot indicates that under a high degree of competitive intensity, the curvilinear relationship between effectuation and NPQ becomes steeper. These results support H4.

Fig. 2. The moderating role of competitive intensity on the relationship between effectuation and NPD speed.

4.1. Post hoc test

Fig. 3. The moderating role of competitive intensity on the relationship between effectuation and NPQ.

Our empirical results confirm that effectuation, as a firm's decisionmaking logic, can facilitate product innovation under competitive conditions. Some studies suggest that the effects of effectuation on the product innovation process are more prominent in SMEs because they have limited resources and capabilities and often engage in little formal planning for NPD (Berends et al., 2014; Roach et al., 2016). This prompts SMEs to adopt effectuation logic when conducting NPD in uncertain environments. Other effectuation literature suggests that effectuation might influence innovation in large firms as well (Brettel et al., 2012; Coviello & Joseph, 2012). Thus, existing research is still unclear about how organizational size influences the effects of effectuation on product innovation (Berends et al., 2014). To clarify this influence, we divided our sample into SMEs and large firm groups. The SME group included small and medium firms (n = 115), while the large firm group included large and super-large firms (n = 65). The subgroup analysis results shown in Tables 9 and 10 for both the SMEs and large firm groups show that effectuation is positively related to NPD speed and has a curvilinear relationship with NPQ. Meanwhile, the moderating effects of competitive intensity on the relationship between effectuation and new product advantage (e.g., NPD speed, NPQ) only exist in the SME group. These results indicate that effectuation is an efficient decisionmaking logic for both SMEs and large firms. This partly supports the argument that the effects of effectuation on NPD are not determined by firm age or size (Coviello & Joseph, 2012). These results also tentatively indicate that SMEs can benefit more from effectuation with regard to product innovation under competitive conditions. Meanwhile, large firms (which have relatively abundant resources and multiple projects) pursuing long-term success (Berends et al., 2014) may prefer to adopt the causation logic to conduct NPD under competitive conditions. Thus,

variables (dependent, independent, and moderator variables) are combined into one factor, does not fit well as compared with the fit of the six-factor measurement model. This means that CMV is not a serious issue. 4. Results We started by assessing the descriptive statistics (Table 7). Multicollinearity problems were circumvented by centering both independent and moderating variables (Aiken & West, 1991). Variance inflation factor (VIF) test results show that the VIFs range from 1.09 to 2.15, far below the threshold of 10 (Neter, William, & Kutner, 1985). This means that multicollinearity is not a serious problem in this study. We tested our hypotheses with hierarchical regressions. The control variables included in Model 1 are shown in Table 8. We then added the independent variables in Model 2. The coefficient of effectuation is positive and marginally significant (β = 0.37; p < .001). This verifies H1, which states that effectuation enhances NPD speed by improving problem-solving speed, providing fast resource solutions, and refining process knowledge. The interaction term with competitive intensity was included in Model 3. The coefficient of the interaction term is positive and marginally significant (β = 0.17; p < .05). The addition of the interaction term in Model 3 increases the R-square value significantly as compared with Model 2 (ΔR2 = 0.01, F (1, 168), p < .05). This verifies H3, which states that competitive intensity boosts the positive effectuation-NPD speed relationship. Fig. 2 plots the interaction between effectuation and competitive intensity (based on Cohen, Cohen, West, & Aiken, 2006). This plot 89

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Table 9 Results of regression analysis (SMEs). Variables

Firm age Firm employees R&D intensity Firm stage Technological uncertainty

NPD Speed

NPQ

Model 1

Model 2 (H1)

Model 3 (H3)

Model 4

Model 5 (H2)

Model 6 (H4)

0.26⁎ (0.13) 0.10 (0.09) 0.15 (0.13) 0.37⁎ (0.21) 0.43⁎⁎⁎ (0.15)

0.39⁎⁎⁎ (0.13) 0.16⁎ (0.09) 0.17⁎ (0.11) 0.38⁎ (0.22) 0.32⁎⁎⁎ (0.13) 0.46⁎⁎⁎ (0.16)

0.35⁎⁎ (0.14) 0.19⁎ (0.11) 0.17⁎ (0.11) 0.41⁎ (0.20) 0.50⁎⁎⁎ (0.17) 0.57⁎⁎⁎ (0.20)

−0.11 (0.15) −0.01 (0.16) 0.23⁎ (0.12) −0.12 (0.24) −0.32⁎⁎ (0.10)

0.40⁎⁎⁎ (0.13)

0.26⁎ (0.16) 0.46⁎⁎⁎ (0.19)

−0.02 (0.15) 0.04 (0.15) 0.14 (0.11) −0.20 (0.23) −0.35⁎⁎⁎ (0.10) 0.49⁎⁎ (0.18) −0.42⁎ (0.18) 0.02 (0.15)

4.17⁎⁎⁎ 0.62 0.47 0.21⁎⁎⁎

4.72⁎⁎⁎ 0.70 0.55 0.08⁎

3.42⁎⁎⁎ 0.14 0.10

4.63⁎⁎⁎ 0.26 0.20 0.12⁎⁎⁎

0.02 (0.15) −0.01 (0.15) 0.15⁎ (0.11) −0.24 (0.23) −0.33⁎⁎⁎ (0.10) 0.53⁎⁎ (0.18) −0.43⁎ (0.19) 0.23 (0.20) 0.01 (0.18) −0.32+ (0.18) 4.05⁎⁎⁎ 0.28 0.21 0.02

Effectuation Effectuation squared Competitive intensity Effectuation × competitive intensity Effectuation squared × competitive intensity F R2 Adjusted R2 R2 change

3.33⁎⁎⁎ 0.42 0.29

Note. N = 115. Significance levels based on two-tailed tests for all models and coefficients. Robust standard errors are in the brackets. + p < .10. ⁎ p < .05. ⁎⁎ p < .01. ⁎⁎⁎ p < .001.

2012), this study finds that NPQ will likely be impaired when a firm excessively relies on effectuation. This is mainly because excessive effectuation is inevitably accompanied by excessive leveraging of precommitments within an existing network (Cui, Wen, Xu, & Qin, 2013) or adoption of shorter-term plans with too many unsuccessful tests, which negatively affects the quality of new products (Brettel et al., 2012). Third, although competitive intensity often brings challenges, we found it can boost effectuation's positive effects on NPD speed. A high degree of competitive intensity can prompt firms to further improve problem-solving speed, provide fast resource solutions, and refine process knowledge by more efficiently using effectuation tactics to hasten NPD. Furthermore, our results verify the curvilinear moderating hypothesis that a high level of competitive intensity can enhance effectuation's positive effects on NPQ and amplify its adverse effects on NPQ. Consequently, in a competitive environment, effectuation may play a more important role in affecting both NPD speed and NPQ.

competitive intensity may have lesser effect on the relationship between effectuation and new product advantage. These post hoc tests verify that organization size matters with regard to the impact of effectuation on product innovation under competitive conditions. However, given that only 65 samples in this sub-group analysis are considered large firms, future research should enlarge the sample to verify whether the impact of effectuation on product innovation depends on organization size and the external environment (Blauth et al., 2014). 5. Discussion To tackle unresolved issues regarding the effects of decision-making logic on product innovation outcomes (e.g., Berends et al., 2014; Blauth et al., 2014; Coviello & Joseph, 2012), this study used survey data from 180 Chinese firms to test the effects of effectuation on two types of NPA and to verify certain decision-making logic hypotheses in the context of NPD. Specifically, a research model was proposed and tested to explore the effects of effectuation on NPD speed and NPQ, with competitive intensity as a contingency factor. First, our results verify that effectuation can facilitate NPD speed. Existing research emphasizes that up-front planning proficiency sometimes does not significantly improve NPD speed, especially when the future is difficult to predict (Cankurtaran et al., 2013). Thus, firms cannot rely solely on goals-driven decision-making logic to expedite NPD (Blauth et al., 2014). Instead, they could rely on effectuation's means-driven decision-making logic to improve NPD speed by leveraging the benefits of reciprocal information with stakeholders and lowcost experimentation. Second, we find that low to intermediate levels of effectuation can improve NPQ, but excessive effectuation will likely harm the quality of new products. Therefore, employing the proper level of effectuation is essential since excessive effectuation can lower resource diversity and result in goal ambiguity. Similar to existing research (Brettel et al.,

5.1. Theoretical contributions This study makes the following theoretical and empirical contributions. First, to better comprehend the relationship between effectuation and NPA, it contributes to the effectuation theory by providing a higher level of granularity through differentiating between two kinds of NPA (Brettel et al., 2012; Sarasvathy, 2001; Wu et al., 2017) and examines effectuation's different effects on NPD speed and NPQ. Our findings echo calls in the NPD literature to move away from treating NPA as a unidimensional concept and to be more precise by exploring differentiation between speed and quality. By doing this, our study reconciles the existing controversy about the relationship between effectuation and NPD outcomes (Brettel et al., 2012; Im, 2013). The results of our study support related suggestions that effectuation can enhance the speed of the first product introduction in the context of new ventures 90

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sources of positional advantage. An important finding of this study is that effectuation is a valid way for firms to gain positional advantages in an uncertain and difficult-to-predict environment. Moreover, prior studies focused on different kinds of product-related positional advantages such as product speed to market, reliability, innovativeness, product differentiation, and cost advantage (e.g., Cui & Wu, 2017; Kim & Atuahene-Gima, 2010; Zhao et al., 2014). Consistent with these studies, this study not only pays attention to NPD speed but also investigates NPQ as another positional advantage. Thus, we suggest that future studies should not take NPD speed and NPQ as simply two items of the NPA construct; they ought to be tested independently (AtuaheneGima & Wei, 2011; Wu et al., 2017) because one type of superior skill or resource may have varying effects on different NPA types.

Table 10 Results of regression analysis (Large firms). Variables

Firm age Firm employees R&D intensity Firm stage Technological uncertainty Effectuation Effectuation squared Competitive intensity Effectuation × competitive intensity Effectuation squared × competitive intensity F R2 Adjusted R2 R2 change

NPD Speed

NPQ

Model 1

Model 2 (H1)

0.28 (0.28) −0.34 (0.34) 0.46 (0.34) −0.30 (0.26) 0.20 (0.35)

−0.04 −0.13 (0.33) (0.34) 0.09 0.12 (0.30) (0.29) 0.52 0.54 (0.49) (0.47) −0.20 −0.17 (0.27) (0.30) 0.21 0.22 (0.38) (0.40) 0.47⁎⁎⁎ 0.47⁎ (0.16) (0.27)

−0.17 (0.37)

Model 3 (H3)

Model 4

Model 5 (H2)

Model 6 (H4)

−0.26 (0.31) 0.06 (0.18) 0.33 (0.21) 0.47 (0.39) −0.22 (0.15)

−0.75⁎ (0.28) 0.12 (0.15) 0.45⁎ (0.19) 0.60+ (0.33) −0.39⁎⁎ (0.13) 0.54+ (0.27) −0.38⁎ (0.14) 0.28 (0.22)

−0.78⁎ (0.30) 0.13 (0.15) 0.41⁎ (0.19) 0.60+ (0.34) −0.44⁎⁎ (0.14) 0.58+ (0.31) −0.37⁎ (0.17) 0.32 (0.26) 0.30 (0.29)

−0.21 (0.47) 0.35 (0.27)

5.2. Managerial implications The findings also provide some important managerial implications for NPD practices. First, firms in emerging economies such as China can use the decision-making logic of effectuation to acquire resources and information and cultivate competence for successful NPD, both in terms of speed and quality. Effectuation is learnable (Read & Sarasvathy, 2005) and can be taught to employees by providing them with appropriate guidance on how to deal with unpredictable situations. After China's entry into the World Trade Organization (WTO) in 2001, more Chinese firms gained the opportunity to compete globally (Fan, Li, & Yeaple, 2018). However, problems such as lower quality and less valueadded of new products made by Chinese firms have prevented these firms from gaining competitive advantage against firms from developed countries that have stronger technological capabilities. Nevertheless, our findings indicate that companies in emerging economies such as China can leverage effectuation to improve the quality of products expeditiously, thereby enhancing their competitiveness against international companies in both domestic and global markets. Second, managers ought to pay attention to the means at hand for acquiring new product advantages by properly forming agreements with existing customers and suppliers, dealing with resource constraints by setting appropriate levels of affordable loss, and embracing unexpected external developments to cope with changes and new opportunities. Moreover, managers should also consider the type of new product advantage that the organization is targeting when using the decision-making logic of effectuation. Specifically, if a firm wants to acquire the advantage of high NPD speed, it should focus on pre-commitments, maintain flexibility, and pay attention to experimentation to cultivate competence and construct new resources for NPD, instead of simply adhering to previous NPD plans. If the firm wants to acquire the advantage of better NPQ, it should be cautious about the negative effects of excessive effectuation. Third, managers should adjust their practice of effectuation in a timely manner to consider the external environment, particularly the changing levels of competitive intensity. For firms in industries characterized by high levels of competitive intensity, the resources and competencies acquired through effectuation, such as the latest market information, product or process knowledge, multiple-path options, and problem-solving creativity, enable firms to improve the speed of NPD and quality of the new product. For example, the IT and smartphone industry is intensely competitive and therefore relies more on effectual principles to gain resources and knowledge for NPD (Shih, Lin, & Luarn, 2014). By contrast, firms in monopolized and regulated industries, such as state-owned banks in China, may instead rely more on goals-driven logics to accumulate competencies for successful new financial service development.

−0.15 (0.32)

5.60⁎⁎⁎ 0.51 0.42

4.17⁎⁎⁎ 0.62 0.47 0.11⁎⁎⁎

4.15⁎⁎⁎ 0.69 0.52 0.07

1.16⁎⁎⁎ 0.09 0.01

4.19⁎⁎⁎ 0.37 0.29 0.29⁎⁎⁎

3.43⁎⁎⁎ 0.39 0.28 0.01

Note. N = 65. Significance levels based on two-tailed tests for all models and coefficients. Robust standard errors are in the brackets. + p < .10. ⁎ p < .05. ⁎⁎ p < .01. ⁎⁎⁎ p < .001.

(Im, 2013) by verifying that effectuation can also boost the speed of NPD for both SMEs and big firms. In addition, while previous case studies suggested that effectuation can bring major innovation to firms with young technology (Coviello & Joseph, 2012), this study supplements these studies by showing that low to intermediate levels of effectuation improve NPQ, but too much effectuation will likely impair NPQ. This new finding supports a few studies that indicated that excessive effectuation might weaken the R&D efficiency of innovation (Brettel et al., 2012). Second, our study adds to effectuation and NPD literature by showing how firms can employ their effectuation tactics in different market conditions, especially with regard to competitive intensity. Although prior research has explored the effects of effectuation contingent on the unpredictability or the dynamism of the environment (Sarasvathy, 2001), limited studies have investigated how the benefits of effectuation vary across conditions with different competitive intensities. Therefore, this study extends the proposition that environmental dynamism can enhance the effects of effectuation on the speed of the first product development of new ventures (Im, 2013) by showing that competitive intensity is another critical moderator for stimulating the effect of effectuation. Our results indicate that effectuation is more effective under conditions of competitive intensity and underscore the critical role of effectuation in NPD projects to defend the fierce competition. This new insight significantly enhances the predictive power of the effectuation theory for new product settings. Third, this study also extends the decision-making–position framework, which posits that a firm's positional advantage depends on its decision-making logic, while a firm's decision-making speed, decisionmaking flexibility, and resource construction options (Day & Wensley, 1988; Slotegraaf & Atuahene-Gima, 2011; Wu et al., 2017) are critical

5.3. Limitations and future research directions This study has several limitations. First, although we consider that China provides a suitable research context for investigating the impact 91

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of effectuation on new product advantages, our empirical results should be examined in other economies to retest and compare with the present results. Second, as effectuation itself is a multidimensional factor, further research is needed to study different dimensions and measurements of effectuation (Brettel et al., 2012; Chandler et al., 2011) which may influence new product advantage differently. Third, the crosssectional data may not be able to explain the causal relationship between effectuation and new product advantage (Blauth et al., 2014); thus, further research ought to explore how the impacts of effectuation on new product advantage change over time by using a longitudinal research design. Fourth, future research should study other kinds of environmental factors, such as market turbulence, technological turbulence, and hyper-competitiveness (Chen, Lin, & Michel, 2010; Jaworski & Kohli, 1993), which may have different contingent influences on the impacts of effectuation on new product advantages. In summary, our empirical results indicate that the impact of effectuation rests on the specific NPD goals that a firm wants to achieve and the competitive intensity the firm faces. Ideally, this research should facilitate further investigation of the pivotal role of other key forms of decision-making logic at various stages of NPD.

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Liang Wu is an assistant professor of Center for Cantonese Merchants Research, Guangdong University of Foreign Studies. His research areas include effectuation, bricolage, organizational learning, and new product development. He has published in Journal of Business Research, and others. Heng Liu is an associate professor in the Lingnan College, Sun Yat-sen University. His research area covers the interface issues between strategy and entrepreneurship. He has published over 10 academic articles in journal such as Journal of Operations Management, Journal of International Marketing, Industrial Marketing Management, Management and Organization Review, and others. Kun Su is an associate professor in School of Management, Northwestern Polytechnical University. His research is focusing on corporate governance and organizational change. He published in Investment Analyst Journal and others.

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