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This study aims to develop a model of learning-by-hiring in which knowledge gains may occur at the time of recruitment but also after recruitment when other incumbent organizational members assimilate a recruit’s knowledge. The author’s model predicts that experienced recruits are more likely to catalyze change to their organization’s core technological capabilities.

The continuous-time parametric hazard rate regressions predict core technological change in a long panel (1970–2017) of US biotechnology industry patent data. The author uses over 140,000 patents to model the evolution of knowledge of over 52,000 scientists and over 4,450 firms. To address endogeneity concerns, the author uses the Heckman selection method and does robustness tests using a difference-in-difference analysis.

The author finds that a hire’s prior research and development (R&D) experience helps overcome inertia arising from her or his new-to-an-organization “distant” knowledge to increase the likelihood of core technological change. In addition, while the author finds that incumbent organizational members resist technological change, experienced hires may effectively induce them to adopt new ways of doing things. This is particularly the case when hires collaborate with incumbents in R&D projects. Understanding the effects of hiring on core technological change, therefore, benefits from an assessment of hire R&D experience and its effects on incumbent inertia in an organization.

First, the author does not recommend managers to hire scientists with considerable distant knowledge only as this may be detrimental to core technological change. Second, the author recommends organizations striving to effectuate technological change to hire people with considerable prior R&D experience as this confers them with the ability to influence other members and socialize incumbent members. Third, the author recommends that managers hire people with both significant levels of prior experience and distant knowledge as they are complements. Finally, the author recommends managers to encourage collaboration between highly experienced hired scientists and long-tenured incumbent organizational members to facilitate incumbent learning, socialization and adoption of new ways of doing things.

This study develops a model of learning-by-hiring, which, to the best of the authors’ knowledge, is the first to propose, test and advance KM literature by showing the effectiveness of experienced hires to stimulate knowledge diffusion and core technological change over time after being hired. This study contributes to innovation, organizational learning and strategy literatures.

The growing interest of the scientific community in film marketing research can be discovered by the large scale published writings on the subject. Although, in this field, comprehensive and systematic research is still lacking. The important aim of this article is to merge the latest research results of film marketing through bibliometric research on articles published from 1976 to 2020, and to examine and introduce the research outcome from the view of the growth trend of the field; the most productive and influential countries, authors, articles, institutions, and research journals; keywords, author patterns, and international linkages. Research outcomes show that film marketing research increased gradually from 1976 to 2020, especially in the last five to 10 years. During this period, all 500 scholarly articles were published in 331 journals and listed in Scopus. Comparison is drawn between institutions from developing countries (in terms of number of articles) and developed countries (in terms of citation rate). On the basis of key points and three-factor examination, a conclusion is drawn that in the last 10 years, ‘film marketing’ as a keyword has attracted widespread attention.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

This paper identified that knowledge from new recruits when used effectively can facilitate technological change within organisations.

The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.

Extant studies in entrepreneurship have explored factors that influence the birth and growth of start-up firms; however, there appears to be a dearth of studies examining the influence of founders' ambidextrous orientation on start-ups' success, especially their speed of attaining the coveted status of a “unicorn start-up” – which is considered a mega success in practice. This study examines whether and how founding teams’ collective ambidextrous orientation influences their respective start-ups’ pace of becoming a “unicorn”.

This study empirically analyses 220 interviews by the founders of 83 Indian unicorns in examining the influence of the founding teams’ collective exploration-exploitation capability on their firms' speed to achieve the “unicorn” status. The Cox Hazard model was used to test the hypothesized relationships, and linear ordinary least squares (OLS) regression was used to test the robustness of the results.

The authors find a strong positive relationship between founding teams’ ambidextrous skills and the speed of becoming a unicorn. The study results suggest that the founding teams’ collective exploratory skills may be more influential in their start-up’s speed to unicorn status vis-à-vis their exploitative skills.

This study finds that the founding teams’ ambidextrous orientation and exploratory skills accelerate their start-up’s speed to becoming a unicorn, contributing to the academic discourse on the “unicorn” phenomenon, which is widely acknowledged as a grand success status for start-ups—especially technology and venture capital funded start-ups—among the practitioners. This study contributes to the academic discourse on firm capabilities and founding-team-related antecedents of start-up success by raising a new dimension of the founding team’s ambidextrous orientation.

This study investigates organizations' non-adoption intention towards the enterprise metaverse. The innovation resistance theory (IRT) is used as an underpinning theory to examine the impact of various risks on non-adoption intention towards the enterprise metaverse.

A total of 294 responses were collected to examine the proposed hypotheses. A structural equation modelling technique was used to investigate the hypotheses using SPSS AMOS and PROCESS MACRO.

The results of this study reveal that performance, security and psychological risks are significantly associated with non-adoption intention towards enterprise metaverse. Further, distrust significantly meditates the association between performance risk, social risk, technological dependence risk, security risk and psychological risk and non-adoption intention towards enterprise metaverse. Moreover, the results of moderated-mediation hypotheses indicate that the mediating effect of distrust on the association among performance risk, social risk, psychological risk and non-adoption intention towards enterprise metaverse is higher for individuals having high technostress compared to individuals having low technostress.

The study's findings will enrich the metaverse literature. Further, it provides a deeper understanding of enterprise metaverse adoption from a B2B perspective using the underpinnings of IRT. The study helps organizations understand the risks associated with the adoption of the enterprise metaverse.

Candlestick charts are a key tool for the technical analysis of cryptocurrency price fluctuations. It is essential to examine trends in the time series of a financial asset when completing an analysis. To accurately examine its potential future performance, it must also consider how it has changed and been active during the period. The researchers created cryptocurrency trading algorithms in this study based on the traditional candlestick pattern.

The data includes information on Bitcoin prices from early 2012 until 2021. Only the engulfing Candlestick model was able to anticipate changes in the price movements of Bitcoin. The traditional Harami model does not work with Bitcoin trading platforms because it has yet to generate profitable business results. An inverted Harami is a successful cryptocurrency trading method.

The inverted Harami approach accounts for 6.98 profit factor (PrF) and 74–50% of profitable (Pr) transactions, which favors a particularly long position. Additionally, the study discovered that almost all analyzed candlestick patterns forecast longer trends greater than shorter trends.

To statistically study its future potential return, examining how it has changed and been active over the years is necessary. Such valuations are the basis for trading strategies that could help traders and investors in the cryptocurrency market. Without sacrificing clarity or ease of application, the proposed approach has increased performance by up to 32.5% of mean absolute error (MAE).

This study is novel in that it used multilayer autoregressive neural network (MARN) models with crypto-net (CNM) in machine learning to analyze a time series of financial cryptocurrencies. Here, the primary study deals with time trends extracted through a neural network model. Then, the developed model was tested using Bitcoin and Ethereum. Finally, CNM validity was tested through linear regression.

As digital procurement continues to transform heavily as a value center and create new business models by linking businesses with a web of external partners, the full path to achieving such an all-encompassing thing is unknown. Thus, the study aims to explore the research gap through an exhaustive bibliometric and systematic literature review on the Digital procurement theme in the supply chain domain.

This study is a qualitative and quantitative analysis of this field, using performance analysis and science mapping to examine 583 articles published from 2002 to 2021.

A systematic literature review indicated core topics on “sustainable or green procurement” and “emerging landscape of technology” in the field of study.

Though the Scopus database used for the analysis is the largest, it may not have complete coverage of all published articles in the field of study; thus, this study is a representation of only a sample rather than its entire population.

Outcome is based on the review of the past 20 years’ contribution on the topic starting from 2002 to 2021.

This study aims to examine the impact of consumer risk appetite, biases (specifically negative recency bias), and the importance of reviews in enhancing information quality. By analyzing these variables, the authors gain insights into their role in enriching the overall information spectrum available to consumers. The findings contribute to a better understanding of how risk appetite, biases and consumer reviews shape the quality of information.

The questionnaire assessed the relationship between dependent and independent variables by asking participants to rate their experiences in relevant scenarios. Variance-based structural equation modeling with the ADANCO program was used to examine the data. ADANCO software is used explicitly for variance-based structural equation modeling. To evaluate research models and test hypotheses, partial least square path modeling is used.

The efficiency of reviews and ratings is greatly influenced by consumer risk appetite. Businesses should focus on clients who are willing to take risks and balance positive and negative feedback. It is essential to comprehend how customers understand reviews. Credibility is increased by taking biases into account and encouraging unbiased criticism. Promoting thorough reviews strengthens influence. Monitoring and making use of these elements improve online reputation and commercial success.

The research has limitations due to the simplicity of the attributes taken into account and the requirement for a larger sample size. Overcoming barriers to promote consistent client feedback is essential, and tailored emails can help with assessment generation. Increased customer participation in writing evaluations can be achieved by removing obstacles and highlighting the advantages of participation.

Businesses and buyers rely on this “organically” generated content as the basis of their promotional strategy and buying decisions. Most of the research is related to consumer reviews, their behavior and the importance of social validation. However, some critical aspects related to this need further investigation.

This paper aims to explain how Web 2.0, social connectedness online, has created incredible new business options. This research’s primary goal is to help businesses use these resources more effectively and perform better.

Variance-based structural equation modeling with the ADANCO program was used to examine the data. ADANCO software is used explicitly for variance-based structural equation modeling. To evaluate research models and test hypotheses, partial least square path modeling is used.

Theories encompassing social support and related approaches to “word of mouth” online, electronic purchasing and virtual communities mediated by technological platforms are the foundational frameworks for this research piece. It then produces a statistical model that enables users to predict how social commerce (s-commerce) building blocks, including forums, communities, ratings and reviews and recommendations, assist businesses in introducing innovative strategies to win in the digital markets. The results necessarily focus on trust, an essential component of e-commerce. Reciprocally, the study reverses engineer’s trust through the constructs of this moment mentioned.

The present study describes the scope of empirical testing and validation of this framework and assists practitioners in further strengthening s-commerce strategy, an emerging and essential platform in the e-commerce industry.

Research highlights the dearth of current analysis in such conceptual domains while generating novel research insights aimed at e-commerce and digital business. From the viewpoint of potential and recurring customers who interact with online communities and product offerings, the study captures the essence of human interactions, often known as trade relationships, online.