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This research paper aims to provide a comprehensive overview of the determinants influencing organisations decisions to adopt crowdsourcing. By synthesising existing literature, it seeks to identify critical factors that act as enablers or inhibitors in the adoption process and propose a framework for understanding crowdsourcing adoption within organisational contexts.

This study employed a systematic literature review methodology to examine the determinants influencing organisations' decisions to adopt crowdsourcing. The review encompassed research articles from the Web of Science and Scopus databases, spanning 2006 to 2021. Additionally, morphological analysis was conducted to categorise the identified determinants into three distinct contexts: technological, organisational and environmental. This methodological approach facilitated a comprehensive exploration of the factors shaping crowdsourcing adoption within organisational settings, allowing for a nuanced understanding of the phenomenon across different dimensions.

The study identifies 12 determinants influencing crowdsourcing adoption, categorised into technological, organisational and environmental dimensions. These determinants include technological compatibility, organisational readiness, top management support, crowd readiness and availability of third-party platforms. While some determinants primarily act as enablers, others exhibit dual roles or serve as inhibitors depending on contextual factors.

The findings offer valuable insights for scholars, practitioners, and organisational leaders seeking to leverage crowdsourcing as a strategic tool for innovation and competitiveness. The assessment scale of drivers and barriers developed in this research offers a systematic approach for evaluating the factors influencing crowdsourcing adoption, providing a nuanced understanding of innovation adoption dynamics. Theoretical implications include advancements in morphological analysis methodology and a nuanced understanding of innovation adoption dynamics. Managerial implications highlight strategies for enhancing organisational readiness, leveraging leadership support and mitigating adoption risks. Overall, the study provides a foundation for future empirical research and practical guidance for organisations planning to adopt crowdsourcing initiatives.

This research contributes significantly to crowdsourcing by presenting an integrated and theoretically grounded framework. By consolidating adoption determinants from diverse contexts, this study clarifies the understanding of crowdsourcing adoption. The framework offers practical value to managers and decision-makers, equipping them with a structured approach to assess and navigate the challenges associated with effectively adopting crowdsourcing. As such, this study contributes to advancing crowdsourcing practices and supports more informed managerial decision-making in innovation and knowledge sourcing.

In the context of rewards-based crowdfunding, this study aims to examine the role of project backers as providers of knowledge inputs beyond just financial capital.

This study uses binomial regression to study the relationship between project creators’ and backers’ knowledge sharing, and the relationship of these two knowledge-sharing elements with achieving above-goal funding levels.

This study finds that the project creator’s knowledge sharing is significantly and positively related to backers’ knowledge sharing and that this relationship is moderated by the type of project. Furthermore, backers’ knowledge sharing is positively related to above-goal funding outcomes for a project.

This study established the link between creators’ and backers’ knowledge sharing in rewards-based crowdfunding, which has been underexplored in the literature. This study’s direct attention to the role of knowledge as a key resource in rewards-based crowdfunding and crowdsourcing in general.

For entrepreneurs seeking crowdfunding, this study highlights the importance of knowledge sharing with their project backers to attain above-goal funding. Furthermore, eliciting backers’ knowledge input acts as a signaling mechanism that increases the crowd’s confidence in the project. It also endows entrepreneurs with knowledge resources that can improve project outcomes and achieve broader market success postcrowdfunding.

To the best of the authors’ knowledge, this study is one of the first to focus on knowledge content as a critical element in project backer-creator communication in rewards-based crowdfunding. This study also delineate the various knowledge types shared between the project creator and backers in both rewards-based crowdfunding projects.

This study investigates the relationship between the individual’s levels of innovativeness (ILI) and the individual’s intention to finance (IIF) an equity crowdfunding campaign to understand whether and to what extent individuals' personalities (IP) can foster crowdfunding success.

OLS models are applied based on survey data collected from 385 US and UK citizen respondents. Further, the baseline relationship between ILI and IIF is broken down on the basis of the interactions with two behavioral characteristics: proactive personality (PP) and openness to experience (OE).

Results show a positive relationship between individual’s levels of innovativeness and the individual’s intention to finance an equity crowdfunding campaign. Furthermore, this relationship continues to be positive when moderators are introduced in the models, demonstrating that PP and OE are personal traits that strengthen the main relationship.

Our findings contribute to enriching the stream of literature according to which equity crowdfunding is a helpful tool not only able to bridge the financial gap of companies during the first phase of their life cycle. The findings also contribute to the development of the innovation process, creating also a social identity within the crowdfunding community.

In the modern digital realm, while artificial intelligence (AI) technologies pave the way for unprecedented opportunities, they also give rise to intricate cybersecurity issues, including threats like deepfakes and unanticipated AI-induced risks. This study aims to address the insufficient exploration of AI cybersecurity awareness in the current literature.

Using in-depth surveys across varied sectors (N = 150), the authors analyzed the correlation between the absence of AI risk content in organizational cybersecurity awareness programs and its impact on employee awareness.

A significant AI-risk knowledge void was observed among users: despite frequent interaction with AI tools, a majority remain unaware of specialized AI threats. A pronounced knowledge difference existed between those that are trained in AI risks and those who are not, more apparent among non-technical personnel and sectors managing sensitive information.

This study paves the way for thorough research, allowing for refinement of awareness initiatives tailored to distinct industries.

It is imperative for organizations to emphasize AI risk training, especially among non-technical staff. Industries handling sensitive data should be at the forefront.

Ensuring employees are aware of AI-related threats can lead to a safer digital environment for both organizations and society at large, given the pervasive nature of AI in everyday life.

Unlike most of the papers about AI risks, the authors do not trust subjective data from second hand papers, but use objective authentic data from the authors’ own up-to-date anonymous survey.

The present study reviews the literature on the history and evolution of investment strategies in the stock market for the period from 1900 to 2022. Conflicts and relationships arising from such diverse seminal studies have been identified to address the research gaps.

The studies for this review were identified and screened from electronic databases to compile a comprehensive list of 200 relevant studies for inclusion in this review and summarized for the cognizance of researchers.

The study finds a coherence to complex theoretical documentation of more than a century of evolution on investment strategy in stock markets, capturing the characteristics of time with a chronological study of events.

There were complications in locating unpublished studies leading to biases like publication bias, the reluctance of editors to publish studies, which do not reveal statistically significant differences, and English language bias.

Practitioners can refine investment strategies by incorporating behavioral finance insights and recognizing the influence of psychological biases. Strategies span value, growth, contrarian, or momentum indicators. Mitigating overconfidence bias supports effective risk management. Social media sentiment analysis facilitates real-time decision-making. Adapting to evolving market liquidity curbs volatility risks. Identifying biases guides investor education initiatives.

This paper is an original attempt to pictorially depict the seminal works in stock market investment strategies of more than a hundred years.

The metaverse, which is now revolutionizing how brands strategize their business needs, necessitates understanding individual opinions. Sentiment analysis deciphers emotions and uncovers a deeper understanding of user opinions and trends within this digital realm. Further, sentiments signify the underlying factor that triggers one’s intent to use technology like the metaverse. Positive sentiments often correlate with positive user experiences, while negative sentiments may signify issues or frustrations. Brands may consider these sentiments and implement them on their metaverse platforms for a seamless user experience.

The current study adopts machine learning sentiment analysis techniques using Support Vector Machine, Doc2Vec, RNN, and CNN to explore the sentiment of individuals toward metaverse in a user-generated context. The topics were discovered using the topic modeling method, and sentiment analysis was performed subsequently.

The results revealed that the users had a positive notion about the experience and orientation of the metaverse while having a negative attitude towards the economy, data, and cyber security. The accuracy of each model has been analyzed, and it has been concluded that CNN provides better accuracy on an average of 89% compared to the other models.

Analyzing sentiment can reveal how the general public perceives the metaverse. Positive sentiment may suggest enthusiasm and readiness for adoption, while negative sentiment might indicate skepticism or concerns. Given the positive user notions about the metaverse’s experience and orientation, developers should continue to focus on creating innovative and immersive virtual environments. At the same time, users' concerns about data, cybersecurity and the economy are critical. The negative attitude toward the metaverse’s economy suggests a need for innovation in economic models within the metaverse. Also, developers and platform operators should prioritize robust data security measures. Implementing strong encryption and two-factor authentication and educating users about cybersecurity best practices can address these concerns and enhance user trust.

In terms of societal dynamics, the metaverse could revolutionize communication and relationships by altering traditional notions of proximity and the presence of its users. Further, virtual economies might emerge, with virtual assets having real-world value, presenting both opportunities and challenges for industries and regulators.

The current study contributes to research as it is the first of its kind to explore the sentiments of individuals toward the metaverse using deep learning techniques and evaluate the accuracy of these models.

This study aims to analyze the importance and performance of customer-facing technologies in luxury hotels. The study also assessed differences between and within the four generations in the importance-performance analysis (IPA).

Data were collected using a Qualtrics panel of recent luxury hotel customers in the USA belonging to all four generations. The cross-generational IPA was conducted using t-tests and (ANAOVA).

The IPA matrix concentrated most technology items in either low importance – low performance or high importance – high performance quadrants. One-way ANOVA revealed significant differences between generations on the importance ratings of all technology items except wireless charging power solutions and on the performance ratings of all technology items. Furthermore, post hoc tests indicated that millennials rated luxury technology most favorably among the four cohorts, followed by generations Z, X and Baby Boomers. In addition, significant differences between the importance and performance of many technology items within each generational cohort were observed. Overall, Wi-Fi was unanimously ranked across generations as the most important technology among luxury guests, but it was the only one that scored lower in performance than importance.

The findings of this study contribute to hospitality scholarship in two primary ways: the importance and performance of technology and generational differences. The results advance the understanding of the impact of generational factors on customer-facing technological adoptions in the luxury hotel sector.

Technologies that are pervasive in the home also become vital offerings for hotels. The more pervasive technology, the more a luxury hotel must work to ensure that it performs at optimal levels. Additionally, which technologies are most important to targeted generations are provided so practitioners may budget for their implementation.

This research is a pivotal step forward in unraveling the intricate interplay between generational factors and technological evaluations, providing a foundation for future research and practical applications in a rapidly evolving technological landscape in the hospitality industry.

In social marketplaces, follower ego networks are integral social capital assets for online sellers. While previous research has underscored the positive impact of the follower number on seller performance, little attention has been given to the structure of follower networks and their value implications. This research investigates two structural properties of follower networks—network centralization and density—and examines their main and contingent effects on sellers’ sales performance.

A 13-month panel dataset of 1,150 sellers in Etsy, a social marketplace for handmade and vintage products, was collected and analyzed. A fixed effects model was adopted to validate the hypotheses on the main effect of centralization and density, as well as the moderating effects of two store attributes: store age and product diversification.

We find that both network centralization and density negatively impact sellers’ sales performance, and these effects vary across store age and product diversification levels. Specifically, the negative effect of network centralization is less pronounced for older stores than young ones, whereas the negative effect of density is more severe for stores with high product diversification.

This research contributes to social commerce research by highlighting the significance of network structure, alongside network size, in assessing the value of followers and offers practical guidance for sellers in social marketplaces seeking to optimize their follower networks.

Tourists with different cultural backgrounds think and behave differently. Accurately capturing and correctly understanding cultural differences will help tourist destinations in product/service planning, marketing communication and attracting and retaining tourists. This research employs Hofstede's cultural dimensions theory to analyse the variations in destination image perceptions of Chinese-speaking and English-speaking tourists to Xiamen, a prominent tourist attraction in China.

The evaluation utilizes a two-stage approach, incorporating LDA and BERT-BILSTM models. By leveraging text mining, sentiment analysis and t-tests, this research investigates the variations in tourists' perceptions of Xiamen across different cultures.

The results reveal that cultural disparities significantly impact tourists' perceived image of Xiamen, particularly regarding their preferences for renowned tourist destinations and the factors influencing their travel experience.

This research pioneers applying natural language processing methods and machine learning techniques to affirm the substantial differences in the perceptions of tourist destinations among Chinese-speaking and English-speaking tourists based on Hofstede's cultural theory. The findings furnish theoretical insights for destination marketing organizations to target diverse cultural tourists through precise marketing strategies and illuminate the practical application of Hofstede's cultural theory in tourism and hospitality.

Indirect knowledge leakage to rivals located near alliance partners represents a significant risk that has received limited scholarly attention. Hence, the question of how to manage this risk – which the authors term “partner-rival co-location risk” – in nonequity alliances remains unanswered, and this study aims to suggest establishing a steering committee to oversee the partnership.

Drawing on the agglomeration economies and alliance governance literatures, the authors develop a set of hypotheses and perform a series of empirical tests on 470 nonequity alliances in the US biopharmaceutical industry.

The authors propose that there is a positive linkage between partner-rival co-location risk and the formation of a steering committee in a nonequity alliance, which receives strong empirical support. Further, this relationship is significantly moderated by the breadth (alliance scope) but not the depth (reciprocal interdependence) of interaction between the partnering firms.

This paper is a pioneer to shed light on “partner-rival co-location risk” and how partner-rival co-location risk affects the governance decision of whether to establish a steering committee in a nonequity alliance, thus offering important theoretical and practical insights into competition and cooperation in alliance management.