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The purpose of this paper is to investigate the impact of two dimensions of technological relatedness, namely technological similarity and complementarity, on collaborative performance, plus the mediating role of collaboration network stickiness and the moderating role of partner expertise and geographical distance in interfirm collaboration contexts.

This study takes Chinese Scientific and Technological Achievements (STA) of inter-firm collaboration in five high-tech fields in 2010–2020 as the sample and uses OLS regression to test the hypothesis.

Technological similarity and complementarity positively affect collaborative performance. Partner expertise negatively moderates the relationship between similarity, complementarity and collaborative performance. Geographical distance positively moderates the relationship between similarity and collaborative performance while negatively moderates that between complementarity and collaborative performance. Collaboration network stickiness partly mediates the relationship between similarity and collaborative performance.

This study expands literature on inter-firm collaboration, especially research on the antecedents of collaborative performance. Moreover, this study not only compensates for lack of empirical analysis in partner selection research, but also utilizes second-hand data to enhance the objectivity of analysis. Additionally, we enrich the research on the moderating role of partner expertise and geographical distance as well as the mediating role of collaboration network stickiness.

This study aims to examine the underlying mechanisms through which entrepreneurial marketing (EM) promotes the development of a favorable brand image and enhances sales growth and market share in new ventures (NVs).

The authors tested the theoretical model using a multi-informant design in which survey data were collected from NV entrepreneurs and marketing managers. Hypotheses were tested using linear regression and PROCESS analysis.

The authors demonstrate the significance of EM as comprising two NV capabilities – first, the level of complementarity between entrepreneurial orientation (EO) and market orientation (MO) as a dynamic capability, and second, brand management capabilities (BMCs) as an operational capability – in shaping a favorable NV brand image and promoting market performance.

The authors offer a novel perspective by demonstrating that EO and MO yield complementarities in driving NVs’ BMCs, which, in turn, drive brand image development and market performance for NVs. In doing so, the authors demonstrate novel theoretical implications for the relevance of EM to NV branding, which, to date, has received scant attention in the literature.

The authors identify a potential avenue for entrepreneurs and NV managers to mitigate the potential failure rates by simultaneously pursuing a higher level of EO and MO and investing in brand-building activities. Such efforts can help enhance brand image, drive sales growth and foster long-term success.

To the best of the authors’ knowledge, this study is the first to include brand capabilities as an element of EM, examine EM in NV brand image development and identify the role of EM capabilities relevant to NV brand building and market performance simultaneously.

In this study, a dynamic contracting model is developed between a venture capitalist (VC) and an entrepreneur (EN) to explore the influence of asymmetric beliefs regarding output-relevant parameters, agency conflicts and complementarity on the VC's posterior beliefs through the EN's unobservable effort choices to influence the optimal dynamic contract.

The authors construct the contracting model by incorporating the VC's effort, which is ignored in most studies. Using backward induction and a discrete-time approximation approach, the authors solve the continuous-time contract design problem, which evolves into a nonlinear ordinary differential equation (ODE).

The optimal equity share that the VC provides to the EN decreases over time. In accordance with the empirical evidence, the EN's optimistic beliefs regarding the project's profitability positively affect its equity share. However, the interactions between the optimal equity share, project risk and both partners' degrees of risk aversion are not monotonic. Moreover, the authors find that the optimal equity share increases with the degree of complementarity, which indicates that the EN is willing to cooperate with the VC. This study’s results also show that the optimal equity shares at each time are interdependent if the VC is risk-averse and independent if the VC is risk-neutral.

In conclusion, the authors highlight two potential directions for future research. First, the authors only considered a single VC, whereas in practice, a risk project may be carried out by multiple VCs, and it is interesting to discuss how the degree of complementarity affects the number of VCs that ENs contract. Second, the authors may introduce jumps and consider more general multivariate stochastic volatility models for output dynamics and analyze the characteristics of the optimal contracts. Third, further research can deal with other forms of discretionary output functions concerning complementarity, such as Cobb–Douglas and constant elasticity of substitution (See Varian, 1992).

The results of this study have several implications. First, it offers a novel approach to designing dynamic contracts that are specific and easy to operate. To improve the complicated venture investment situation and abate conflict between contractual parties, this study plays a good reference role. Second, the synergy effect proposed in this study provides a theoretical explanation for the executive compensation puzzle in economics, in which managers are often “rewarded for luck” (Bertrand and Mullainathan, 2001; Wu et al., 2018). This result indicates a realistic perspective on financing and establishing cooperative relationships, which enhances the efficiency of venture investment. Third, from an empirical standpoint, one can apply this framework to study research and development (R&D) problems.

First, the authors introduce asymmetric beliefs and Bayesian learning to study the dynamic contract design problem and discuss their effects on equity share. Second, the authors incorporate the VC's effort into the contracting problem, and analyze the synergistic effect of effort complementarity on the optimal dynamic contract.

Circular economy firms, unlike those in a linear economy, encounter significant challenges due to the nature of nonstandardized products, such as asymmetric information on product specifications and highly dispersed supply and demand. Consequently, achieving large-scale operations becomes inherently difficult for them. However, when digital platform-based circular firms adopt diversified business models, their value creation has the potential to achieve significant scale. This study aims to answer “How do digital platform-based circular firms scale up value creation through business model diversification?”.

The authors use a qualitative case study design with a longitudinal (2011–2023) analysis of the various business models developed by ATRenew, a leading firm in the Chinese preowned electronics industry.

The authors find that the recycling business model can act as a “cornerstone” business model for a circular firm to integrate the business model portfolio by leveraging the most synergies (or complementarities) with other business models. Besides, digital platform-based circular firms can effectively scale up value creation by leveraging capability complementary on the supply side, through establishing industrial infrastructure shared with the platform ecosystem; and by leveraging customer complementary on the demand side, through activating the dual role played by platform users as suppliers and consumers.

The authors contribute to the circular business models literature with three mechanisms to scale up circular value creation through business model diversification and advance a set of propositions to be tested in future research. This also has important practical implications for circular economy platform businesses.

Supply chain (SC) professionals are increasingly working alongside business partners of diverse backgrounds, which has been argued to engender both innovation and creativity but also found as potentially detrimental to SC relationships and performance. To reconcile these views, this study explores two mechanisms – supplementary (similarity) and complementary fits – at the surface (observable traits) and deep (unobservable characteristics) levels and their impact on a focal firm representative’s perception of a SC partner’s trustworthiness.

Model was tested using survey data from 285 managers involved in interorganizational SC relationships.

Results indicate that a focal firm representative’s perception of supplementary and complementary fits with a SC partner positively impacts their perception of the partner’s trustworthiness. However, the effects of similarity at both surface and deep levels and complementarity weaken each other.

Understanding the mechanisms of diversity in SC relationships is crucial for fostering trustworthiness and achieving organizational objectives. Firms should evaluate both supplementary and complementary fits when hiring or assigning roles. Embracing a complementary fit not only promotes diversity but also mitigates the negative impact of similarity bias, ultimately strengthening trustworthiness within the organization's SC ecosystem.

By simultaneously examining individual and combined effects of two unique mechanisms of supplementarity and complementarity at the surface and deep levels, this study sheds light on inconsistent findings of the effects of diversity in the SCM literature.

This study empirically aims to examine the collaboration between social enterprises (SEs) and impact investors (IIs), which are organisations with similar interests but with distinct logics, and in high demand in emerging economies with complex problems. Despite the significant economic contributions of these organisations, there have been limited studies examining how they collaborate in different contexts, including theoretical insights explaining how they gain partner fit from resource synergy.

Mainstream businesses use the compatibility and complementarity concepts to examine buyer–supplier strategic alliances. Using similar concepts in the context of hybrid organisations, the authors interviewed six pairs of SEs and IIs with dyadic relations in Nigeria, aiming to deeply understand how they align dissimilar logics in pursuing common goals in emerging economies.

The authors’ findings revealed how compatibility criteria from the institutional logics perspective and complementarity from social exchange theory guide collaboration between SEs and IIs in an emerging economy. Using these theories provides new insights that distinguish SEs and IIs collaboration from conventional theories on the internationalisation of businesses, which remained insufficient for understanding the cross-border operations of SEs.

The study holds practical implications for organisations, regardless of their size, international investors, governments, organisations and individuals desiring to pursue sustainable business agendas in emerging economies with huge impact opportunities and the process involved.

The outcomes of this study extend knowledge of the theoretical lens examining collaborative entrepreneurship from the perspective of hybrid organisations. It also challenged existing knowledge on collaboration between SEs and IIs, often characterised by potential tensions due to the dissimilarity of institutional logics of actors.

This study aims to specify whether heterogeneous reference-point-based aspirations are related to the cooperation levels of R&D alliance portfolios in a positive or negative (or nonlinear) way, and to unveil how cooperative behaviors evolve in recurrent project cooperation.

This study establishes a network containing a cooperation subnetwork and a project subnetwork based on patent data in the “deep learning” field to investigate how cooperative behaviors evolve in R&D alliance portfolios. A model of evolutionary games on complex networks is constructed to gain insight into the dynamic evolution of DMs’ strategies.

First, the heterogeneous aspirations of DMs can improve the cooperation level in R&D alliance portfolios. Second, compared to prudent DMs, aggressive DMs are more likely to choose the cooperation strategy, implying that an appropriate aspiration level nurtures cooperative R&D endeavors with partners. Third, the effects of effort complementarity, knowledge reorganization capabilities and cooperation supervision on cooperation are contingent on the distribution of DMs’ aspiration types.

Policymakers should identify aspiration types of DMs when screening partners. They can encourage partners to focus more on historical payoffs and establish relatively higher aspiration levels to improve the cooperation level. Developing highly detailed contracts becomes crucial when cooperating with firms that possess extensive knowledge reorganization capabilities.

This work contributes a theoretical framework for investigating cooperation in R&D alliance portfolios through the lens of evolutionary games on complex networks, thus revealing the effects of heterogeneous reference-point-based aspirations of DMs on R&D cooperation.

Being acquainted with both lean and action learning in theory and in practice, this study finds that the theoretical complementarity of these two research streams has traditionally been underexploited. In this conceptual paper, this study aims to advance the theoretical understanding of lean by exploring the complementarity of lean thinking and action learning leading to a proposed integrated theory of these two research streams. Target audience is the operations management research community.

By deliberately adopting a process of theorising, this paper explores, reflects upon and combines individual experiences of researching, teaching and engaging in lean and action learning as operations management scholars.

Having taken a gemba walk through the literature and practices of lean and action learning, this study views and notices a systematic and complementary relationship between the two domains. The overlapping theoretical and practical complementarities of lean and action learning suggest that these two research streams are ripe for synthesis into an integrated theory. This finding provides an opportunity to (1) progress towards an integrative design of interventions leading to more sustainable lean system adoptions and (2) add new depth to our theoretical explanation of the success and failures of lean system adoptions.

This paper contributes an original integrated theory perspective on lean and action learning.

This article aims to answer two research questions that remain controversial in the accounting and corporate governance literature: (1) how corporate disclosure is related to board monitoring and (2) how this link is affected by the institutional environment and firm-level governance.

The study is based on S&P data on corporate disclosure by Russian companies collected over 2002–2010 and supplemented by information from the SKRIN database. The dataset covers 125 non-financial companies, with 559 observations in total. We use three indicators of board monitoring: the percentage of non-executive directors, a dummy for two-tier boards, and a dummy for an audit committee. The firm’s governance is proxied by a dummy for single class stock, while the institutional environment is proxied by a dummy for ADRs/GDRs. We apply conventional methods of panel data analysis with several robustness checks, including the random- and fixed-effects models, 2SLS that addresses the potential endogeneity of board composition, alternative definitions of the dependent variable, and an extended list of controls.

We find a positive (complementary) relationship between the amount of disclosure and the proxies for board monitoring employed. This complementary relationship turns out to be the strongest among companies that have better internal governance but face a weaker institutional environment. There is little evidence of such complementarity under strong institutions.

The findings may be of interest to investors and policymakers. As to the former, the results warn of firms that provide limited disclosure in the presence of strong corporate governance arrangements, such as independent boards, as these factors are not substitutes for each other. As to the latter, the results support comprehensive policies aimed at simultaneous improvements in both board governance and corporate disclosure in weak institutional settings.

This paper uses a unique setting and rich, partly proprietary data to extend the existing literature on the relationship between corporate disclosure and board monitoring, with an emphasis on the moderating role of the institutional environment and firm-level governance. It is also one of the very few studies of corporate disclosure in Russia, an important emerging economy of the early 2000s.

Most previous studies predicting review helpfulness ignored the significance of deep features embedded in review text and instead relied on hand-crafted features. Hand-crafted and deep features have the advantages of high interpretability and predictive accuracy. This study aims to propose a novel review helpfulness prediction model that uses deep learning (DL) techniques to consider the complementarity between hand-crafted and deep features.

First, an advanced convolutional neural network was applied to extract deep features from unstructured review text. Second, this study used previous studies to extract hand-crafted features that impact the helpfulness of reviews and enhance their interpretability. Third, this study incorporated deep and hand-crafted features into a review helpfulness prediction model and evaluated its performance using the Yelp.com data set. To measure the performance of the proposed model, this study used 2,417,796 restaurant reviews.

Extensive experiments confirmed that the proposed methodology performs better than traditional machine learning methods. Moreover, this study confirms through an empirical analysis that combining hand-crafted and deep features demonstrates better prediction performance.

To the best of the authors’ knowledge, this is one of the first studies to apply DL techniques and use structured and unstructured data to predict review helpfulness in the restaurant context. In addition, an advanced feature-fusion method was adopted to better use the extracted feature information and identify the complementarity between features.

大多数先前预测评论有用性的研究忽视了嵌入在评论文本中的深层特征的重要性, 而主要依赖手工制作的特征。手工制作和深层特征具有高解释性和预测准确性的优势。本研究提出了一种新颖的评论有用性预测模型, 利用深度学习技术来考虑手工制作特征和深层特征之间的互补性。

首先, 采用先进的卷积神经网络从非结构化的评论文本中提取深层特征。其次, 本研究利用先前研究中提取的手工制作特征, 这些特征影响了评论的有用性并增强了其解释性。第三, 本研究将深层特征和手工制作特征结合到一个评论有用性预测模型中, 并使用Yelp.com数据集对其性能进行评估。为了衡量所提出模型的性能, 本研究使用了2,417,796条餐厅评论。

广泛的实验验证了所提出的方法优于传统的机器学习方法。此外, 通过实证分析, 本研究证实了结合手工制作和深层特征可以展现出更好的预测性能。

据我们所知, 这是首个在餐厅评论预测中应用深度学习技术, 并结合了结构化和非结构化数据来预测评论有用性的研究之一。此外, 本研究采用了先进的特征融合方法, 更好地利用了提取的特征信息, 并识别了特征之间的互补性。