1- Introduction

2- Theoretical development and hypotheses

3- Mixed-methods approach

4- Discussion, conclusion, and future research

References

Abstract

Escalation of commitment, defined as the continuation in a failing course of action, is a persistent problem for decision makers in business markets, especially those involved in new product development (NPD). To address this issue, we use Anticipated Regret Theory to develop a model and then empirically test it to demonstrate how forward-looking emotions can lead decision makers to continue failing NPD projects in business-to-business (B2B) markets. We recognize that there are two countervailing types of anticipated regret (i.e., keep regret and drop regret) and test both in our model by adopting a mixed-methods empirical approach. In a quantitative study, a total of 280 subjects completed a NPD decision-making exercise in which various antecedents of persistence with a losing new, business-to-business NPD project were examined. The results suggest that anticipated drop regret plays a significant role in commitment to a failing course of action, whereas anticipated keep regret actually reduces commitment. In a second, qualitative study, twenty experienced NPD professionals operating in high-technology, B2B markets were interviewed either in-person or by telephone. The results suggest that anticipated drop regret is a more serious problem than anticipated keep regret, supporting the quantitative study.

Introduction

Business markets have been characterized by a rapid pace of technological and market change and widespread turbulence due to everchanging customer needs and intense competition (Langerak and Commandeur, 1997; Liang, Sudhir, and Cherian, 2014). A key source of competitive advantage in such markets is new product development (NPD) which has been found to be crucial to the long-term success of business-to-business (B2B) firms (Nijssen and Frambach, 2000). In fact, empirical evidence reveals that higher performing B2B firms derive almost half of their sales and profits from new products they introduced within the last five years (Hutt and Speh 2013). However, NPD is expensive and time-consuming and also suffers from a high failure rate which can have a debilitating impact on organizations (Schmidt et al. 2009). Research shows that about 40% of all new products fail in the market causing significant losses to firms, and ony about one-in-five new products meet annual profit objectives (Cooper 2012). Amidst several determinants of new product failure such as flawed products, incorrect market research, poor screening, and commercialization issues, a significant contributor to new product failure in business markets is escalation of commitment (Boulding et al. 1995; Liang, Sudhir, and Cherian, 2014; Schmidt and Calantone 1998). An escalation of commitment (hereafter escalation) situation has certain defining characteristics: (1) an initial investment of resources such as money, time, and effort into a course of action; (2) negative feedback about the future prospects of that course of action; (3) the possibility to withdraw or continue the chosen course with further investment of resources; (4) uncertainty about the consequences of these actions (Lehenkari 2012). Escalation is typically exhibited through the decision to ignore or distory negative information (Lee, Wong, and Ellick, 2015). Escalation can lead to substantial losses in resources, time, and opportunities and can be particularly disastrous for firms in B2B markets that require fast reactions and quick adaptability (Biyalogorsky et al. 2006).

Introduction

Literature review and hypotheses development

Methodology

Results

Discussion

References

Abstract

Purpose – The purpose of this paper is to examine the influences of social capital on knowledge heterogeneity in order to advance the understanding of the effects and to reconcile existing inconsistent findings.
Design/methodology/approach – Survey data collected from 105 new product development (NPD) projects were analyzed with regression-based methods.
Findings – The results indicated that trust, centralization and shared vision as the three social capital dimensions generally have negative impacts on the domain and presentation dimensions of knowledge heterogeneity. However, the three dimensions of social capital do not exhibit consistent influences on the tacitness heterogeneity (i.e. an epistemological dimension of knowledge heterogeneity).
Research limitations/implications – More research is needed to explore the role of social capital dimensions in developing a range of knowledge attributes of NPD teams, among which knowledge heterogeneity is one. The various dimensions of knowledge an NPD team possesses should have performance implications and deserve future investigation.
Originality/value – The study is one of the first documented attempts to demonstrate contingencies in the relationship between social capital and knowledge heterogeneity. The effect of social capital on knowledge heterogeneity should be understood at the level of dimensions of the two respective constructs.

Introduction

Organizations are knowledge-processing entities that operate in competitive business environments. In such contexts, high-quality knowledge management activities, including the creation, acquisition, sharing and integration (Grant, 1996; Nonaka and Takeuchi, 1995) of knowledge as critical firm-level intellectual capital (Ling, 2013; Su and Carney, 2013), have become crucial for organizational capability building and success (Teece, 1998; Teece et al., 1997). As business environments are rapidly changing and organizations are compelled to change to cope with environmental changes, knowledge is a cornerstone for enacting organizational changes but also a key barrier to changes if not being well managed. Laszlo and Laszlo (2002) argue that knowledge evolution is key for organizational members to align with societal changes with sustainable leaning so as to create competitiveness of organizations. Under such a premise, a detailed clarification of the relationships between specific enablers and specific types of knowledge (henceforth called knowledge dimensions) useful for the development of organizational competitiveness under rapidly changing environments is vital and crucial for organizations (Chalkiti, 2012; Jones and Mahon, 2012). By contrast, failing to continue the course of knowledge development may lead organizations to face great challenges in high-velocity contexts (Mahon and Jones, 2016; Scalzo, 2006).

The purpose of this paper is to investigate the processes underlying the relationship between creativity processes and new product performance. Drawing on the literature of new product development (NPD) and organizational creativity, we hypothesize that NPD speed mediates the relationship between creativity processes and new product performance and that encouragement by leadership moderates this mediating model. Using a sample of 245 companies in China, we found that (1) not all components of creativity processes related positively to new product performance. Specifically, information search and encoding (ISE) and idea and alternative generation (IG) are respectively and positively related to new product performance, but problem identification (PI) is not. (2) NPD speed fully mediates the influence of PI and ISE on new product performance, but it only partially mediates the relationship between IG and new product performance. (3) Encouragement by leadership positively moderates the relationship of PI and NPD speed, as well as the relationship of ISE and NPD speed; however, it does not significantly moderate the relationship between IG and NPD speed. The implications of these findings and directions for future research in NPD performance are discussed.

Introduction

To remain competitive, firms need to provide a constant stream of new products and quickly deliver innovative products to the market while controlling costs (Allocca & Kessler, 2006; Carbonell & Rodriguez, 2006; Farid, Hakimian, Ismail, & Nair, 2017; Goktan & Miles, 2012). New product development (NPD) literature has focused on the NPD process, which starts from generating new ideas to commercializing new products, and proclaimed that new product innovativeness and NPD speed (the pace of progress that a firm displays in this process) are unequivocally important to new product performance (Chen, Reilly, & Lynn, 2012; Kessler & Bierly, 2002; Knockaert & Spithoven, 2014). However, the influences on NPD performance are quite complex. The relationship between new product innovativeness and NPD speed still has room for development (Kessler & Bierly, 2002; Wu, Liu, & Zhang, 2016). For example, are different tasks in the process of engaging creativity, which is crucial for new product innovativeness, equally associated with NPD speed? If not, how exactly do firms manage the NPD process by balancing creativity and speed to increase new product performance?

This study proposes a moderated mediating framework to describe the relationships among international diversification, technological capability, market orientation and emerging market multinational enterprises’ new product performance. Within this framework, emerging market multinational enterprises’ technological capabilities mediate the impact of internationalization on new product performance. This mediating effect is more salient for firms entering more developed markets than those entering less developed markets. Analysis of a sample of Chinese multinationals’ internationalization process supports these relationships.

Introduction

Studies of international diversification and organizational learning have suggested that international diversification enhances product innovation by exposing a firm to diverse customer requirements (Matarazzo & Resciniti, 2013; Zahra, Ireland, & Hitt, 2000). Notably absent in the literature is a basic but puzzling question: Does international diversification relate to new product performance through technological capability? This question has received little attention, perhaps because scholars investigating the topic have mainly focused on multinational enterprises (MNEs) from developed markets and assumed that all MNEs possess strong technological capabilities and can develop new solutions to satisfy different customer requirements (Petison, Thongthou, & Lekmoung, 2012). But recent studies have questioned this assumption because they have found many MNEs from emerging markets (EMNEs) have technological capability which is actually weak compared with their counterparts from developed markets (Piperopoulos, Wu, & Wang, 2017; Wu & Park, 2017). In order to satisfy diverse requirements of international customers and succeed in global markets, EMNEs need an ability to develop new products that solve those customers problems. So academic and practical considerations call for an explanation of how EMNEs international diversification relates to their technological capabilities and innovation performance.

Deciding which artist or song to sign and promote has always been a challenge for recording companies, especially when it comes to innovative newcomer singers without any chart history. However, the specifics of a creative industry such as the hedonic nature of music, socio-network effects, and ever fastening fashion cycles in combination with digitalization have made the recording industry even more competitive and these initial decisions even more crucial. With respect to the ongoing digital transformation and shift in power from organizations to consumers, we leverage digitally mediated wisdom of the crowd to build a forecasting model for better understanding chart success. Therefore, we draw on the literature of hedonic and experiential goods to investigate the relationship between crowd evaluations based on listening experience and popular music chart success. We track 150 song positions in reported music charts and also evaluate these songs via the crowd. Our model indicates that the wisdom of the crowd can improve forecasting chart success by almost 30% relatively to factors that have been earlier identified in the literature. However, this forecasting relevance is bound to certain conditions, namely the composition of the crowd, the underlying chart and market mechanisms, and the novelty of the musical material. In sum we find that crowd-based mechanisms are especially suited to forecast the performance of novel songs from unknown artists, which makes them a powerful yet very affordable decision support instrument for very uncertain contexts with limited historical data available. These findings can support recording companies to address the challenge of signing newcomers and thereby further enable the innovation system of the industry.

Introduction

It has always been crucial for music companies and labels to sign the right newcomer artists and select the best songs for a release in order to ensure a constant revenue stream from music product sales (Cameron, 2016; Dewan and Ramaprasad, 2014; Ordanini, 2006). These decisions are by no way perfect even today, which is reflected in revenues that are distributed according to the power law: revenues of few commercial successes compensate for the losses of many failures (Cameron, 2016; Strobl and Tucker, 2000). The reasons for this high number of failures and their challenges for firm survival can be found in the complex and uncertain market characteristics of the music publishing industry as well as in ever changing technologies. First, the consequences of the digital transformation, such as music piracy, lower per unit revenues of digital content, and new business models have diminished the total profitability of the industry (Aguiar and Waldfogel, 2015; Benner and Waldfogel, 2016; Dobusch and Schüßler, 2014; Hiller, 2016; Lam and Tan, 2001). Second, the selection criteria of the recording companies are based on informal heuristics since the hedonic nature of music prevents an objective measurement of its quality. Third, direct and large-scale consumer engagement has been expensive. In order to increase the chances of reaching the consumer, the products are therefore developed and bundled according to the expected consumer taste formulated by the media gatekeepers (Hirsch, 1972; Ordanini, 2006). Fourth, consumer taste is susceptible to fashion cycles, socio-network effects and decision difficulties due to increasing choice of consumption, which result in diversity and uncertainty about expected demand.

The lack of quantitative mechanisms aimed at evaluating the potential business benefits of ecodesign prior to implementation is a major barrier to wider adoption in manufacturing companies. Ecodesign is defined as the consistent integration of environmental aspects into product development processes. Within this frame, there is a need to understand how the development of ecodesign capabilities affect overall business performance over time. Drawing upon the Ecodesign Maturity Model (EcoM2) as the theoretical foundation, this paper systematically reviews the literature on (i) relevant applications of dynamic modelling and (ii) relationships between ecodesign management practices and key business performance outcomes, in order to develop a simulation-based approach aimed at deriving a business case framework for ecodesign implementation. The resulting framework originates the “business case simulator”, which was subjected to the judgement and evaluation of six industry experts regarding its applicability and usefulness to manufacturing settings. The results are discussed and future research streams e coupled with improvement opportunities to the business case simulator e are pointed.

Introduction

Despite the significant growth in the number of researchers and corporations reporting the benefits of ecodesign-related efforts (Haned et al., 2015; IRRC Institute, 2015; Plouffe et al., 2011), a number of challenges still hinder a broader and consistent implementation of ecodesign in manufacturing firms. There is a particular lack of proper mechanisms to evaluate the potential business benefits originated by ecodesign (Boks, 2006; Dekoninck et al., 2016; McAloone, 1998; Rodrigues et al., 2017a, 2016b), which can be defined as a proactive approach for the integration of environmental aspects and considerations into the product development processes (Pigosso et al., 2013, 2015). With that, the concept of “business case” emerges as the set of arguments that support and elicits the key reasons why an organization should implement or advance a specific cause (Carroll and Shabana, 2010; Rodrigues et al., 2018a,b; Schaltegger and Lüdeke-Freund, 2012; Weber, 2008; Whelan and Fink, 2016). In general, most business cases typically account for a posteriori analysis of the influences of ecodesign-related practices, as opposed to a priori, predictive business cases, which focuses on how to collect information and measure the overall and strategic performance of a company in financial (e.g. profitability, revenues, costs, return on investment etc.) and non-financial terms (e.g. water usage, material usage, energy usage, CO2 emissions, water footprint etc.).

Individuals and organizations cannot learn perpetually. Managers should be aware that unlearning becomes necessary to discard obsolete and outdated mental models, assumptions, behaviors, or routines. We investigated new product development teams and interviewed change consultants to explore the phenomenon of unlearning in real-life contexts. We particularly focus on examples of cases in which these steps have been used and implemented successfully. In order to make room for new knowledge and foster innovations more efficiently, we identify and suggest four critical steps managers can introduce to help their employees unlearn: creating situational awareness, providing islands of temporal and spatial freedom, encouraging an error-forgiving and stop-doing culture, and reducing the influence of old knowledge over time. Fostering unlearning can prevent rigidity and stagnation while opening up room for innovation. This article adds an important practical dimension to the mainly theoretical ideas in the field of unlearning and provides strong implications for managers and other practitioners.

Why letting go is important

When looking at rituals, traditions, behaviors, or daily routines, we often observe the same phenomenon: Humans are creatures of habit. Leaving our comfort zones is challenging (Govindarajan, 2016) and we usually avoid re-examining what is going well–—especially during times of great success. Although sticking to a proven strategy can be beneficial in many ways, our established mental models and habits might obstruct us from keeping up with the environment. Think about your own business: When was the last time you have critically scrutinized well-proven aspects of your business? Have you asked yourself why companies go bankrupt despite possessing abundant resources to innovate? Are you prepared to tackle future challenges, particularly when they do not unfold the way you expected? Such questions become increasingly relevant. Organizations are situated in dynamic and unpredictable environments; what managers have learned as best practices may suddenly become obsolete. Hence, organizations are required to discard existing mental models, assumptions, beliefs, or routines. The process of intentionally getting rid of established knowledge structures is referred to as unlearning (Tsang & Zahra, 2008). Unlearning becomes essential when we want to adapt to a new situation but some of our knowledge contradicts new ideas.

1- Introduction

2- Literature review

3- Methodology

4- The new product balanced scorecard

5- Discussion and conclusions

References

Abstract

New product development (NPD) is a counter-seasonal strategy able to reduce demand fluctuations, especially during the seasonal tails. No previous study has analysed this field through the lens of balanced scorecard (BSC). This explorative paper contributes to this gap and considers two research questions: i) How is a destination NPD process operationalized using the four BSC perspectives? ii) What is the relevance and content of each perspective in this particular field? The study deploys a longitudinal analysis of the Skipassfree product, launched by Livigno (Italy) in 2007. Over a decade, this product generated a significant uplift both in terms of hotel guests (+108%) and ski company clients (+248%). The proposed framework is built around 22 codes and incorporates a fifth BSC perspective (the destination context). “Learning and growth” is the most significant perspective with “alignment” being a key attribute, which suggests the relevance of innovation and stakeholders’ involvement.

Introduction

The increasing competition in the tourism and hospitality industry (Go and Govers, 2000; Ritchie and Crouch, 2000) pushes destinations to carefully manage seasonality (BarOn, 1975), especially in alpine contexts (Pegg et al., 2012; Sainaghi, 2008). Possible strategies are usually centred upon a few drivers, including: price (Turrión-Prats and Duro, 2017), events (Connell et al., 2015; Getz and Page, 2016; Sainaghi and Mauri, 2018), NPD (Sun and Carter, 2009), and market segmentation (Sainaghi and Canali, 2011). While event management (Getz, 2008, 2012) and marketing strategies (price, market segmentation) are well studied, particularly in the field of hospitality, NPD has not evolved in a similar accretive manner (Chen et al., 2013). Based on a recent literature review, NPD is defined as the process from a conceptualized idea to market launch of novel or updated goods (Müller-Stewens and Möller, 2017). Sandvik et al. (2011) suggest that tourism organizations may not always have a formalised process for NPD, especially in the case of small-medium sized businesses. Many studies assert that NPD is strongly linked with firm competitive advantage, both in the general field of management (Barczak et al., 2009) and also for lodging companies (Skalpe and Sandvik, 2002). For hospitality firms and in the context of this study, NPD is usually deeply linked with the overall destination management (Sainaghi, 2006). Prior hospitality and tourism studies as reported in Table 1 were focused on: hospitality businesses (60%), followed by tourism destination (35%), plus some marginal articles that follow a combined approach (5%). Researchers have used the BSC mainly to measure performance, to develop strategy or to evaluate (in a broad sense) hospitality and especially tourism websites. Given the array of local firms, the implementation of a new product is a difficult task for a tourism destination (Haugland et al., 2011). A destination, can be described as a network (Baggio and Sainaghi, 2016) or complex system (Baggio and Sainaghi, 2011), where nodes are organizations and relationships among them are links (Sainaghi and Baggio, 2014, 2017). Furthermore, while each company produces and delivers its service or good, the customer perceives the destination as a single element (Buhalis, 2000). Therefore, a destination new product requires evidence of governance (d’Angella and Go, 2009; d’Angella et al., 2010). These observations illustrate the high level of NPD complexity and the need to treat this topic with a multi-stakeholder approach (Beritelli, 2011) and inside a “processual” – rather than a “content” – perspective (Sainaghi, 2006).

Studies on R&D internationalization have identified the decentralization-recentralization pattern of many multinational corporations (MNCs). Studies have also indicated that MNCs tend to pursue a transnational emphasis during R&D recentralization. This research focuses on recentralization of new product development (NPD) in MNCs and aims to reveal different processes of it towards a transnational emphasis. Through a qualitative case study of four MNCs, the NPD recentralization process was explored in two important dimensions – power centralization and pluralistic input. Four process variants were discovered, showing different approaches to power centralization and pluralistic input. The processes of recentralization are novel to the area of R&D internationalization. This study also advances our understanding of R&D recentralization with a focus on NPD fulfilling market demand, which has not been well addressed in prior studies. In addition, this study generates some insights into transnational management.

Introduction

R&D internationalization has received considerable attention over the past decades (Asakawa and Som, 2008; Awate et al., 2015; Hsu et al., 2015; Patel and Vega, 1999; Von Zedtwitz et al., 2004; Wortmann, 1990). With internationalized R&D (owning overseas R &D centers), multinational corporations (MNCs) can gain competitive advantage through accessing local knowledge and leveraging locational innovation advantage (Un and Rodríguez, 2018; von Zedtwitz and Gassmann, 2002). Research has revealed the decentralization-recentralization pattern (or embeddedness before integration) of R&D internationalization of many MNCs (Chen et al., 2012; Christensen, 2002; Gassmann and von Zedwitz, 1999; Granstrand, 1999; Håkanson, 1990; Niosi, 1999). For companies adopting this path, they tend to adopt a decentralized R&D structure at an early stage of R&D internationalization. Local R&D units are granted local autonomy to achieve local embeddedness which helps to access local technological and market knowledge and cultivate new knowledge (Lehrer and Asakawa, 2003). However, with the increasing number of autonomous R&D units, coordination becomes more difficult and duplication of efforts is more likely to happen (Gassmann and von Zedwitz, 1999; Granstrand et al., 1993). With “growing coordination problems” (Chen et al., 2012, p. 1545), MNCs will then recentralize R&D for better control and coordination. Such recentralization is necessary for MNCs to efficiently leverage knowledge at the global scale and avoid duplication. While recentralization can lead to a higher innovation performance in the long run, it has great challenges as well, such as the conversion loss (Chen et al., 2012). The conversion loss is caused by the autonomy-control tension and the disruptive effect on projects during the recentralization process. However, we still have very limited knowledge of the processes of R&D recentralization – how R&D recentralization can be done in MNCs. It is also unclear whether there are different processes.

1- Introduction

2- Theoretical background

3- Method

4- Results

5- Discussions and conclusions

References

Abstract

Research shows that effective marketing and R&D interface is pivotal in a company’s new product development performance and future competitiveness. The increased popularity of social media promised to enhance interaction, collaboration, and networking between the two functions. However, there is limited knowledge regarding the key activities, infrastructure requirements, and potential benefits of social media in the marketing and R&D interface. This study aims to advance the current understanding of social media engagement strategies, which facilitates improved marketing and R&D interfaces and ultimately NPD performance for manufacturing companies. Based on a multiple-case study in two manufacturing companies, this study first presents the role of social media in facilitating improved marketing and R&D interface within a B2B context. Second, it presents the adoption process of the social media engagement strategy for an evolving marketing and R&D interface. The adoption process is divided into three phases, namely coordination, cooperation, and coproduction, to provide detailed insights regarding full-scale social media engagement. Taken together, the study provides novel insights into industrial marketing management literature by exemplifying the role of social media and proposing a systematic social engagement strategy for improved marketing and R&D interface in the manufacturing industry.

Introduction

New product development (NPD) is pivotal to companies’ longterm survival and growth (Ahmad, Mallick, & Schroeder, 2013; Geroski & Machin, 1992). A prominent key success factor for NPD is the ability to closely integrate and build upon competences from many internal functional departments, such as sales, marketing, research and development (R & D), engineering, finance, production, and aftermarket (Gomes, de Weerd-Nederhof, Pearson, & Cunha, 2003). Specifically, there is a wide consensus that enhanced marketing and R & D interface are vital to NPD success (Griffin & Hauser, 1996; Gupta, Raj, & Wilemon, 1985; Lam & Chin, 2005; Li & Chen, 2016; Song & Song, 2010; Xie, Song, & Stringfellow, 2003). This is because the marketing function possesses deeper insights into the customer and the market, and R & D function represents the knowledge, skills, and capabilities to develop innovative product and service offers. Through improved interface between marketing and R & D, technical and market expertise are effectively combined to achieve the common business goals such as develop novel product design with higher marketability (Fain, Kline, & Duhovnik, 2011). A harmonious marketing and R & D interface helps companies to integrate resources, mitigate innovation risks, access new technologies, enter new markets, improve product quality, and reduce uncertainties (Hempelmann & Engelen, 2015; Yao, Xu, Song, Jiang, & Zhang, 2014). Although positive benefits from interface integration between marketing and R & D towards innovativeness have been widely recognized (Fain et al., 2011; Olson, Walker, Ruekert, & Bonner, 2001), studies also acknowledge the higher likelihood of conflicts and challenges arising between marketing and R & D interface (Hernandez & Lee, 2007) due to the differences in their personality, backgrounds, language, responsibilities, perspectives, and interests (Griffin & Hauser, 1996; Wang, 1996).

1- Introduction

2- Indicators of innovativeness

3- Indicators of innovation input

4- Indicators of innovation output

5- Conclusions

References

Abstract

Innovation is vital for each country’s economic growth. This paper comprises an analysis of the main indicators that characterize innovation activity in Romanian firms. Improving our knowledge of firms’ activities in aspects associated with their behavior related to innovation is important for monitoring the strengths and weaknesses of the national innovation systems. Our study on Romanian firms is based entirely on the published data by Eurostat (the Commission Innovation Survey – CIS), which centralizes data every two years, the last have been published at the end of 2016 (CIS 9) covering the period 2012-2014. From building and analyzing three main types of general innovation indicators related to firms’ innovativeness, including innovation inputs and outputs, our results show that the main weaknesses in business innovation persist over time and they consist of the extremely low share of innovative firms, a low level of business innovation expenditures and a high volatility of innovation performance based on creative effort resulted from R&D activities still concentrated in a few industries.

Introduction

Innovation is of crucial importance for the modern economy competitiveness, as well as for a high standard of living and welfare. According to OECD (2005, p. 46), innovation can be broadly defined as ”the implementation of a new or significantly improved product (good or service), or process, a new marketing method, or a new organizational method in business practices, workplace organization or external relations”. Improving our knowledge of firms’ activities in aspects associated with their behavior related to innovation is important for monitoring the strengths and weaknesses of the national innovation systems. The use of innovation indicators developed on the basis of firms’ survey has intensified in the last decade, as they broaden the business analysis framework by taking into account innovation expenditures performed by firms and innovation outputs (with reference, most often, to product innovations expressed by using turnover), comprising also the sources of information used by firms, including their collaboration with various partners, as well as business perceptions on obstacles encountered in their activities. Data such as those provided by the Community Innovation Survey (CIS – Eurostat, EBRD etc.) are of considerable utility to identify innovation determinants and innovative firms’ characteristics related to the size and structure of innovation expenditures, the collaboration activities and information used in innovation process, innovation output, and allow comparisons over time by firm’s size, by industries and cross-countries comparisons. In this framework, simple (general) innovation indicators can be built using the answers given by firms to a single question, or complex indicators can be elaborated by using answers to several questions (OECD, 2011; Huang et al., 2010; Bloch et al., 2008: Arundel, 2007 etc.) being of importance for government policy focused on stimulating innovation. This paper comprises an analysis of the main innovation indicators that characterize the firms’ activity in Romania. Our study is based entirely on the published data by Eurostat (the Commission Innovation Survey – CIS), which centralizes data every two years, the last ones have been published at the end of 2016 (CIS 9) covering the period 2012-2014. Our concern is related to the fact that innovation activities in Romania still lags behind those within most of the EU countries, and the pace of catching up the more advanced economies is very low. These can be reflected in the size of composite innovation indicators such as the Global Competitiveness Index (GCI, reported by the World Economic Forum), the Knowledge Economic Index (measured by the World Bank), the Summary Innovation Index (SII, published by the European Commission) etc. In this respect, we build and analyze three main general groups of innovativeness indicators. Section 2 comprises a first set of indicators referring to shares of innovative firms broadly defined, performing technological (product and/or process) innovation and non-technological (organizational and/pr marketing) innovation, as well as firms that introduced new product innovations. Section 3 contains a descriptive analysis of business innovation inputs, by building a second set of innovation indicators such as business innovation expenditure, share of innovative firms that performed research and development (R&D) activities and collaborative activities of innovative firms. The next group of indicators measure innovation outputs analyzed in section 4, the most common comprising the share of turnover from new-to-firm and new-to market product innovations (as % of turnover) and section 5 concludes.

Despite there being various styles of leadership, transformational leadership has received much attention (Senior and Fleming, 2006) and has often been studied by researchers to predict innovation performance (Chen et al., 2012; Gumusluoglu and Ilsev, 2009a, b). According to Di Benedetto (2013), in recent years, transformational leadership has become an emerging research topic in innovation and has increasingly received attention from scholars. Many studies support the positive effect of transformational leadership on organizational innovation (Aragón-Correa et al., 2007; García-Morales et al., 2008; Gumusluoglu and Ilsev, 2009a, b; Jung et al., 2008; Matzler et al., 2008; Noruzy et al., 2013). However, this positive relationship requires a wider analysis of the intermediate steps between transformational leadership and product innovation performance; how transformational leadership effectively influences product innovation is a complex process (Mumford and Licuanan, 2004). The mechanisms for its connection with product innovation processes and outcomes have not been explicitly studied (Crossan and Apaydin, 2010). Despite some advances, few studies (Chen et al., 2014; Gumusluoglu and Ilsev, 2009a, b) have gone beyond a simple, direct relationship between transformational leadership and product innovation performance. However, an explanation of the process through which transformational leadership affects product innovation performance is missing. In addition, most previous studies examined transformational leadership of lower management level rather than at the CEO level (Chen et al., 2014). In essence, knowledge is limited as to why CEO transformational leadership is related to product innovation performance. We aim to address this limitation. Which theoretical mechanism explains why transformational leadership may relate to product innovation performance? We advance one theoretical explanation and propose that the link between CEO transformational leadership and product innovation performance is mediated by some organizational factors. To build our theoretical framework, we argue that transformational leading by the CEO increases a firm’s ability to recognize and exploit opportunities for product innovation. Transformational CEOs can influence social interaction and social change within the organization, which promotes a culture of innovation. CEO transformational leadership also influences the innovation strategy by focusing on tactics and pushing the strategic direction toward innovation. Davila et al. (2006) conclude that CEOs have important roles in putting the innovation strategy in place, as well as making innovation a part of the company’s culture. The theoretical framework of this study was developed from a multidimensional framework of organizational innovation (Crossan and Apaydin, 2010) and was grounded in transformational leadership theory. A theoretical framework that links CEO transformational leadership, organizational factors (i.e. innovation strategy, organizational learning, innovation culture, and the new product development (NPD) process), and product innovation performance is proposed and depicted in Figure 1.

Controlling new product development (NPD) projects is a fundamental concern for companies, executives and research and development managers (Chiesa and Masella, 1996; Bonner et al., 2002; Chiesa et al., 2009; Zhu and Chen, 2016), and the selection of the “right” formal controls is considered a crucial managerial task (Henttonen et al., 2016). Facing turbulent dynamics in competitive areas, shortened life cycles, globalisation, reduced time to market, and increased NPD costs and risks (Chiesa et al., 2009), firms increasingly have to balance flexibility and control related to their performance measure use (Henri, 2006). Thus, one goal in controlling NPD projects is to avoid unpleasant surprises in order to assure that the NPD project is on track and delivers new products that meet customer needs (Bonner et al., 2002). Another goal is to ensure that formal controls do not interfere with the necessity that NPD teams are innovative and creative, and adapt quickly to new developments in the NPD project (Henri, 2006). Furthermore, previous literature has argued that adaptability affects firm performance (e.g. Thongpapanl et al., 2012) and that team adaptability is key for project success (e.g. Klarner et al., 2013), yet we have little empirical evidence on how formal controls affect team adaptability in NPD projects. Despite the emerging notion that formal controls will affect NPD success (e.g. Rijsdijk and Van den Ende, 2011; Chong and Mahama, 2014; Henttonen et al., 2016), it is insufficiently clear to which extent formal controls and team adaptability jointly affect NPD project success. To address this gap, we investigate the relationship between formal controls, team adaptability and project success for two key performance measures in NPD projects: cost and resource allocation (Henttonen et al., 2016). Cost and resource allocation decisions are amongst the most important decisions in every NPD project, and firms usually provide specific targets in these areas (Davila, 2000; Thamhain, 2003). We focus on the use of two specific formal controls that are argued to help achieve a balance between predictable goal achievement and creative innovation: target rigidity (an output control) that provides direction for project execution and can be used to monitor progress, and process autonomy (a behaviour control) that provides autonomy to adapt project activities to new developments in the NPD project. We argue that these controls provide the motivation (output controls) and discretion (behaviour controls) for the team to adapt to new developments in the NPD project, which subsequently affects NPD project success. We empirically test our research model based upon data collected from a survey of 113 NPD project managers. We analyse the effectiveness of target rigidity and process autonomy for resource and cost measures in separate models as project managers may use these measures differently.