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Innovation ecosystems consist of independent and yet interdependent enterprises and actors. This constitutes a challenge for innovation leadership to set the direction to pursue UN SDGs. Enhanced insights and understandings of how collaborative innovation leadership can be conducted in practice are revealed in two cases conducted using a participatory action research approach. Hereby theory and practice are linked by the ecosystem participant actions to reach competitiveness of the offshore wind energy ecosystem compared to traditional/other renewable energy sources pursuing UN SDG number 7 of ‘affordable and clean energy’. The two cases are situated in ports in offshore wind energy innovation ecosystems, respectively in the Baltic Sea and in the North Sea. The first case contains complementary ecosystem participants with both leadership and expert positions. The second case contains both complementary and competitive ecosystem participants with leadership positions. The findings note that both cases can use collaborative innovation leadership through the point of origin in the guiding star of the value proposition at ecosystem level to go beyond their own enterprise self-interest. Next, collaborative innovation leadership is based in heterogeneities of both knowledge domains and individual and organisational behaviours for the innovative imagination of future needed initiatives for collaborative design-based sensemaking. A short-term challenge is present from reduced revenue in own enterprise from future needed initiatives. This short-term impact needs to be addressed for enhanced long-term robustness. The findings are summarised in a model for dissemination of collaborative innovation leadership in ecosystems to pursue UN SDGs in practice.

The purpose of the study is to examine the interaction between the structure of the top management team, takeover defense mechanisms and firms rate of collective actions.

The study uses elements of agency theory, prospect theory and competitive dynamics research to develop a model for examining heterogeneity in the rate of collective actions among firms in the technology sector. A sample of 299 firm-year observations arrayed into panel regression analyses is used.

The findings from this study show a positive relationship between the size of the top management team and the count of collective actions when takeover defense mechanisms are present. Further this study finds a negative relationship between top management team ownership and collective actions when these same takeover defense mechanisms are present. Additionally, the female ratio of the top management team is negatively related to collective actions.

This study was conducted using a sample of technological firms. These relationships may not be generalizable to firms in other contexts. Further, other elements of the firm’s governance structure (i.e. board of directors or shareholders) may play an important role in the strategic decision-making process.

This study expands on existing research by linking several blocks of literature, top management team literature, competitive dynamics literature and corporate governance literature, into a model to examine firm structural characteristics on the heterogeneity in the propensity to formulate collective actions among firms.

This paper aims to examine the effect of new product launch actions and firm reputation on firm performance in the Chinese auto industry.

This analysis adopts empirical data from 66 auto firms in China’s auto market from 2007 to 2012 to explore how new product launch actions undertaken by a firm can contribute to achieving superior performance and to investigate the relationships between new product launch actions and firm performance. Moreover, how firm reputation interacts with new product launch actions to affect firm performance is also investigated. Fixed effects regression model following the Hausman specification test was used to quantitatively examine the relationship.

It was concluded that the focal firm’s new product launch actions, including new product launch breadth, complexity and heterogeneity of its new repertoire of product launch actions, and firm reputation can impact its performance. Firm reputation can impact the signaling process and the capability of firms to enhance their performance via new product launch movements.

This research contributes to new product launch research by providing a more comprehensive view of competitive dynamic actions by which a firm’s performance is strengthened by examining the effects of two factors that affect performance. These factors are as follows: the characteristics in terms of breadth, complexity, and heterogeneity of new product launch actions undertaken by a firm and the characteristic of firm reputation.

This paper aims to examine why retail firms seldom achieve full integration of online and offline channels as prescribed in omni-channel literature. It examines the intermediate process of channel integration from an internal, operational perspective.

This study is composed of two parts. In the first part, the authors interviewed informants from nine firms that were engaged in channel integration. In the second part, the authors conducted case studies with three firms from the cosmetics and skincare industry against the backdrop of the COVID-19 pandemic to find evidence to support or negate the propositions made in the first part.

The first part identified six operational challenges to channel integration. The authors categorized these challenges into two groups: inter-channel communication and inter-channel competition. Inter-channel competition carries more weight at the latter stage of integration. The authors also identified two antecedents that affect the seriousness of these challenges: heterogeneity among channels in business operation and external competitive pressure. In the second part, the authors found that both inter-channel communication and inter-channel competition were improved because of the external competitive pressure exerted by the COVID-19 pandemic. However, the heterogeneity of offline channels against online channels in business operation is a double-edged sword.

The study identifies the changing effects of the challenges of channel integration and their antecedents in the midst of integration. The positive influence of a specific dimension of channel heterogeneity against other channels increases and then decreases along channel integration. The identification of the changing effects lays the foundation for a finer stage model of channel integration.

The study here responds to the view that the crucial problem in strategic management (research) is firm heterogeneity – why firms adopt different strategies and structures, why heterogeneity persists, and why competitors perform differently. The present study applies complexity theory tenets and a “neo-configurational perspective” of Misangyi et al. (2016) in proposing complex antecedent conditions affecting complex outcome conditions. Rather than examining variable directional relationships using null hypotheses statistical tests, the study examines case-based conditions using somewhat precise outcome tests (SPOT). The complex outcome conditions include firms with high financial performances in declining markets and firms with low financial performances in growing markets – the study focuses on seemingly paradoxical outcomes. The study here examines firm strategies and outcomes for separate samples of cross-sectional data of manufacturing firms with headquarters in one of two nations: Finland (n = 820) and Hungary (n = 300). The study includes examining the predictive validities of the models. The study contributes conceptual advances of complex firm orientation configurations and complex firm performance capabilities configurations as mediating conditions between firmographics, firm resources, and the two final complex outcome conditions (high performance in declining markets and low performance in growing markets). The study contributes by showing how fuzzy-logic computing with words (Zadeh, 1966) advances strategic management research toward achieving requisite variety to overcome the theory-analytic mismatch pervasive currently in the discipline (Fiss, 2007, 2011) – thus, this study is a useful step toward solving the crucial problem of how to explain firm heterogeneity.

The rapid development of digital technologies drives digital entrepreneurs to pivot, a behavior that allows entrepreneurs to adjust original opportunities and explore new opportunities. This study aims to investigate the effect of the structural characteristics of digital entrepreneurial teams, the functional heterogeneity, on pivoting from the perspective of digital agility. Moreover, this study also examines the moderating effect of knowledge sharing.

Two-phase survey data were sourced from Chinese digital entrepreneurial teams through the entrepreneurial networks of MBA programs of a Chinese business school and entrepreneurial support organizations in China. The sample of 272 teams with 708 entrepreneurs was collected to test the hypotheses.

The functional heterogeneity of digital entrepreneurial teams, including industry background heterogeneity and occupational experience heterogeneity, positively impacts pivoting by providing heterogeneous knowledge and resources. Moreover, this relationship is mediated by the digital agility of the digital team, and knowledge sharing moderates the relationship between functional heterogeneity and digital agility.

While existing studies have mainly focused on the external factors, this study empirically investigates the team-level internal factors of digital pivoting in digital entrepreneurial teams, enriching the research perspective of pivoting. Moreover, the current study bridges the literature on digital agility with pivoting, broadening the theoretical mechanism of pivoting and expanding the theoretical boundaries of digital agility.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.