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The importance of learning in development in increasingly dynamic contexts can help individuals and organizations adapt to disruption. Artificial intelligence (AI) is emerging as a disruptive technology, with increasing adoption by various human resource management (HRM) functions. However, learning and development (L&D) adoption of AI is lagging, and there is a need to understand of this low adoption based on the internal/external contexts and organization types. Building on open system theory and adopting a technology-in-practice lens, the authors examine the various L&D approaches and the roles of human and technology agencies, enabled by differing structures, different types of organizations and the use of AI in L&D.

Through a qualitative interview design, data were collected from 27 key stakeholders and L&D professionals of MSMEs, NGOs and MNEs organizations. The authors used Gioia's qualitative research approach for the thematic analysis of the collected data.

The authors argue that human and technology agencies develop organizational protocols and structures consistent with their internal/external contexts, resource availability and technology adoptions. While the reasons for lagging AI adoption in L&D were determined, the future potential of AI to support L&D also emerges. The authors theorize about the socialization of human and technology-mediated interactions to develop three emerging structures for L&D in organizations of various sizes, industries, sectors and internal/external contexts.

The study hinges on open system theory (OST) and technology-in-practice to demonstrate the interdependence and inseparability of human activity, technological advancement and capability, and structured contexts. The authors examine the reasons for lagging AI adoption in L&D and how agentic focus shifts contingent on the organization's internal/external contexts.

While AI-HRM scholarship has primarily relied on psychological theories to examine impact and outcomes, the authors adopt the OST and technology in practice lens to explain how organizational contexts, resources and technology adoption may influence L&D. This study investigates the use of AI-based technology and its enabling factors for L&D, which has been under-researched.

Despite the innovation and performance of venture capital (VC)-backed firms receiving extensive attention, how and under what conditions VC influences innovation and performance remains unclear. The present paper draws on organizational learning (OL) theory to examine the moderating effect of syndication on backed firms.

Drawing on a literature review that connects OL and innovation to the performance of VC-backed firms, this study examines the effects of OL on innovation and firm performance among these firms by questioning the moderating effect of VC syndication. A sample of 78 VC-backed firms was used to test the robustness of the proposed model and causal relationships through the use of partial least squares structural equation modeling (PLS-SEM).

The empirical evidence demonstrates that the intervention of venture capitalists can not only stimulate innovation, but also have a significantly positive effect on firm performance. Furthermore, the evidence reveals that syndication of VC investment supports backed firms in improving the firms' performance and generating innovation from acquired knowledge.

To the best of the authors' knowledge, this study is the first in North Africa that focuses on the moderating effects of venture capital syndication on the relationships between OL, innovation and firm performance.

Literature on entrepreneurial finance has long overcome the view of an investor as a sole provider of financial capital. Entrepreneurs need to consider more aspects when deciding on an investor. Especially the depiction of corporate venture capital (CVC) investors has long highlighted advantages and disadvantages compared to independent VC (IVC) investors. The authors investigate what drives entrepreneurs' preferences for CVC relative to IVC and thereby focus on two key issues in the entrepreneur's consideration – the role of resource requirements and exit strategies.

The data were collected in an online survey that gathered information on several characteristics of entrepreneurs and their ventures. The resulting data set of 105 German entrepreneurs was analyzed using logistic regression and revealed important drivers for entrepreneurs' investor preferences.

The study’s findings confirm that the venture's resource needs, specifically the need for marketing resources and access to the corporate network, which play a significant role in the decision on whether a CVC or IVC investor is preferred. Moreover, the analysis debunks the hypothesis that entrepreneurs view a CVC investment as the first step toward acquisition. However, those entrepreneurs striving for an IPO are less likely to prefer CVC.

The study expands the literature on CVC attractiveness and specifically considers the entrepreneurs' intentions and needs. The results confirm but also debunk some widespread perceptions about why entrepreneurs choose to pursue financing from a CVC investor.

A critical aspect in venture capital (VC) exiting is the choice of exit mode. This study aims to predict if venture capitalists (VCs) can take the venture capital undertaking public by identifying the impact of investment attributes, market timing and macroeconomic conditions on the choice of mode of exit for VCs.

The study uses logistic regression on a sample of 632 Indian VC-backed firms where VCs exited during the past two decades via initial public offers (IPOs) and other routes, including strategic sale, secondary sale and buyback.

Results suggest that growth stage investments, larger syndication size and a larger number of IPOs increase the probability of exiting through IPOs, whereas investments in the information technology and information technology-enabled services industry have a higher likelihood of being exited through other routes. Region and gross domestic product are found to be statistically insignificant in predicting the likelihood for a particular mode of exit.

The results have practical implications for VCs as knowledge regarding the influence of investment attributes, market timing and macroeconomic conditions can help them in deciding their exit strategy vis-à-vis mode of exit and can maximize their potential gains. The results also have implications for the potential investors, primarily the public at large and acquirers.

The determinants of VC exit options remain an unexplored area in the Indian context. To the best of the authors’ knowledge, the study is the first of its kind that has used investment attributes, market timing and macroeconomic conditions to predict VC exit options in India.

The study aims to shed light on the current state of “Dataset repositories” indexed in Directory of Open Access Repositories (OpenDOAR).

From each repository/record information, the Open-Access Policies, Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), year of creation and the number of data sets archived in the repositories were manually searched, documented and analyzed.

Developed countries like the United Kingdom and the USA are primarily involved in the development of institutional open-access repositories comprising significant components of OpenDOAR. The most extensively used software is DSpace. Most data set archives are OAI-PMH compliant but do not follow open-access rules. The study also highlights the sites’ embrace of Web 2.0 capabilities and discovers really simple syndication feeds and Atom integration. The use of social media has made its presence known. Furthermore, the study concludes that the number of data sets kept in repositories is insufficient, although the expansion of such repositories has been consistent over the years.

The work has the potential to benefit both researchers in general and policymakers in particular. Scholars interested in research data, data sharing and data reuse can learn about the present state of repositories that preserve data sets in OpenDOAR. At the same time, policymakers can develop recommendations and policies to assist in the construction and maintenance of repositories for data sets.

According to the literature, there have been numerous studies on open-access repositories and OpenDOAR internationally, but no research has focused on repositories preserving content-type data sets. As a result, the study attempts to uncover various characteristics of OpenDOAR Data set repositories.

This study analyzes the role of regional cooperative banks in identifying and financing small and medium-sized enterprises (SMEs) from a proximity perspective. Access to finance is a major challenge for SMEs. Regional cooperative banks can remove this barrier based on cooperative bank's characteristics and geographic proximity to SMEs. Understanding the interplay between these financial actors and firms can contribute to a better support of SMEs development.

The results are based on a case study of eight SMEs located in southwestern France. Interviews were conducted with two regional cooperative funds and eight SMEs. The interview guide included questions related to the company, the projects financed and how financing was accessed.

Results reveal that a combination of three forms of proximity allows regional cooperative banks and SMEs to establish effective financing operations. They show that regional cooperative banks are key players in the existing financing mechanisms for SMEs. Such financing is often used to gain access to larger players at a later stage. The findings suggest the need for public policies that promote the integration of financing actors in regional ecosystems to advance SMEs' development.

This article examines how SMEs access financing, with a focus on regional cooperative banks, which have received little attention in the literature. Moreover, the relationships between these actors are studied through the lens of proximity. Regional cooperative banks are able to finance projects that may have been overlooked by traditional banks due to trust-building local dynamics.

Institutional, economic, social and technological advancements enable openness to cope with wicked public management issues. Although open innovation (OI) is becoming a new normality for public sector entities, scholarly knowledge on this topic is not fully systematized. The article fills this gap, providing a thick and integrative account of OI to inspire public management decisions.

Following the SPAR-4-SLR protocol, a domain-based literature review has been accomplished. Consistently with the study purpose, a hybrid methodology has been designed. Bibliographic coupling permitted us to discover the research streams populating the scientific debate. The core arguments addressed within and across the streams were reported through an interpretive approach.

Starting from an intellectual core of 94 contributions, 5 research streams were spotted. OI in the public sector unfolds through an evolutionary path. Public sector entities conventionally acted as “senior partners” of privately-owned companies, providing funding (yellow cluster) and data (purple cluster) to nurture OI. An advanced perspective envisages OI as a public management model purposefully enacted by public sector entities to co-create value with relevant stakeholders (red cluster). Fitting architectures (green cluster) and mechanisms (blue cluster) should be arranged to release the potential of OI in the public sector.

The role of public sector entities in enacting OI should be revised embracing a value co-creation perspective. Tailored organizational interventions and management decisions are required to make OI a reliable and dependable public value generation model.

The article originally systematizes the scholarly knowledge about OI, presenting it as a new normality for public value generation.

Building on the venture capital (VC) literature, this paper aims to study the impact of the value added by corporate venture capitalists (CVCs) on their funded companies by comparing its IPO valuation with its independent venture capitalists (IVCs) peers.

This study uses a sample of 3,719 VC-backed ventures, between the years 1998 and 2020. The empirical analysis focuses on the propensity score matching approach, pairing ventures based on their probability of being funded by CVCs, and consequently, interpret the results derived from the valuation multiple ratios between the “nearest neighbors.”

This study finds that companies funded by CVCs can achieve higher valuations at their IPO compared to IVC-backed companies. Moreover, CVC-backed companies outperformance is mainly driven by startups which hold a technological fit with their CVC investor, with higher technological overlaps being translated into more significant valuations.

This study presents systematic evidence to the subject concerning ventures’ type of investors and its effect on the startups’ IPO valuations.

This paper contributes to the enrichment of the industry’s literacy while also easing entrepreneurs’ decisions when choosing a funding partner. CVCs offer a variety of services and support that fits the specific needs of their funded companies.

To the best of the authors’ knowledge, this study is among the first to examine the role of CVCs as a tool to help venture growth.

This paper aims to review, systematize and map the extant literature on private equity (PE) and study the underlying research agenda for investment selection and value creation in portfolio firms of PE investors. The PE investment process entails the preinvestment stage, where PE investors screen the target firms, and the postinvestment stage, where PE investors monitor the funded firms. With the motive to understand both stages, this review consolidates the findings of existing literature.

This research adopts a systematic literature review approach to study the underlying themes in PE investment literature. To adequately profile the key research areas, the authors have adopted citation classics in addition to keyword search and drawn the most significant papers in this field of research based on citation metrics.

The review presents a heterogeneous set of themes by encapsulating the relevant PE literature and identifies significant and emergent themes within the broad research area of investment and performance. The foundational themes found are selection determinants for PE investments, value creation in PE investments and selection vs value-adding effect of PE investors. While the emergent themes are the relative performance of PE investments; sources of value creation; skill, luck and social capital in PE; and resource dependency vis-à-vis PE. Each theme or subtheme chalks out the underlying research agendas for future researchers.

To build an understanding of the selection determinants and value creation, this review addresses the need to synthesize and align the PE literature concerning pre and post investment stages. PE is a fertile research area that is systematically captured in this review by identifying themes, subthemes and avenues for future research.