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Academics and practitioners are increasingly discussing the role of crowdsourcing initiatives in dealing with the challenges imposed by the COVID-19 pandemic, along with their role in an emergency context in general. However, empirical evidence about the role of crowdsourcing initiatives in an emergency context is still scarce. This paper aims to address this gap in knowledge.

The authors adopted an inductive approach to investigate how companies leveraged crowdsourcing initiatives to address the issues posed by COVID-19. Data were collected through semi-structured interviews. The selection of the interviewees was based on a purposive sampling strategy. Data were analysed through thematic analysis.

The analysis led to the identification and examination of drivers (e.g. preserving relationships with customers), advantages (e.g. increased demand for services) and disadvantages (e.g. increased platform cost) of implementing crowdsourcing initiatives during COVID-19. By leveraging the findings, this paper suggests future research opportunities. Relevant future research opportunities include: (1) quantitatively evaluating the economic impact of crowdsourcing initiatives during a disruptive event and (2) examining how the sector in which the company operates influences the beneficial and detrimental effects of crowdsourcing initiatives on company performance.

The involvement of crowds as innovation partners has provided unique opportunities for companies to innovate and address the challenging scenario. The scientific literature about the role of crowdsourcing during COVID-19 is growing. However, there is still a scarcity of empirical evidence about the mechanisms underpinning the use of crowdsourcing during the pandemic. By leveraging semi-structured interviews, this paper addresses this relevant gap in knowledge.

The purpose of this Real Impact Research Article is to empirically explore one of the most controversial and elusive concepts in knowledge management research – practical wisdom. It develops a 10-dimensional practical wisdom construct and tests it within the nomological network of counterproductive and productive knowledge behavior.

A survey instrument was created based on the extant literature. A model was developed and tested by means of Partial Least Squares with data obtained from 200 experienced employees recruited from CloudResearch Connect crowdsourcing platform.

Practical wisdom is a multidimensional construct that may be operationalized and measured like other well-established knowledge management concepts. Practical wisdom guides employee counterproductive and productive knowledge behavior: it suppresses knowledge sabotage and knowledge hiding (whether general, evasive, playing dumb, rationalized or bullying) and promotes knowledge sharing. While all proposed dimensions contribute to employee practical wisdom, particularly salient are subject matter expertise, moral purpose in decision-making, self-reflection in the workplace and external reflection in the workplace. Unexpectedly, practical wisdom facilitates knowledge hoarding instead of reducing it.

Managers should realize that possessing practical wisdom is not limited to a group of select, high-level executives. Organizations may administer the practical wisdom questionnaire presented in this study to their workers to identify those who score the lowest, and invest in employee training programs that focus on the development of those attributes pertaining to the practical wisdom dimensions.

The concept of practical wisdom is a controversial topic that has both detractors and supporters. To the best of the author’s knowledge, this is the first large-scale empirical study of practical wisdom in the knowledge management domain.

For ranking aggregation in crowdsourcing task, the key issue is how to select the optimal working group with a given number of workers to optimize the performance of their aggregation. Performance prediction for ranking aggregation can solve this issue effectively. However, the performance prediction effect for ranking aggregation varies greatly due to the different influencing factors selected. Although questions on why and how data fusion methods perform well have been thoroughly discussed in the past, there is a lack of insight about how to select influencing factors to predict the performance and how much can be improved of.

In this paper, performance prediction of multivariable linear regression based on the optimal influencing factors for ranking aggregation in crowdsourcing task is studied. An influencing factor optimization selection method based on stepwise regression (IFOS-SR) is proposed to screen the optimal influencing factors. A working group selection model based on the optimal influencing factors is built to select the optimal working group with a given number of workers.

The proposed approach can identify the optimal influencing factors of ranking aggregation, predict the aggregation performance more accurately than the state-of-the-art methods and select the optimal working group with a given number of workers.

To find out under which condition data fusion method may lead to performance improvement for ranking aggregation in crowdsourcing task, the optimal influencing factors are identified by the IFOS-SR method. This paper presents an analysis of the behavior of the linear combination method and the CombSUM method based on the optimal influencing factors, and optimizes the task assignment with a given number of workers by the optimal working group selection method.

The crowdfunding concept and activities have recently been the focus of attention of many researchers and practitioners over different business contexts. However, there is a dearth of literature considering the main aspects of e-equity crowdfunding activities and their impact on the innovation performance for entrepreneurial business. Therefore, this study aims to explore how entrepreneurs' engagement in e-crowdfunding activities could enhance both knowledge acquisition and innovation performance.

The conceptual model will be proposed based on three main theoretical perspectives: relationship marketing orientation (RMO); Kirzner's alertness theory; and the DeLone and McLean model of information systems. The data of the current study were collected using an online questionnaire from a sample of 500 entrepreneurs who have actively engaged in e-crowdfunding in Saudi Arabia.

The statistical results of structural equation modelling (SEM) approved the impacting role of RMO, entrepreneurial alertness, system quality and service quality on the entrepreneurs' engagement in e-equity crowdfunding, which in turn, predicts both knowledge acquisition and innovation performance.

There are several limitations which could be addressed in future studies, for example, this study has only considered one form of crowdfunding (equity based crowdfunding) and due to its nature these findings would not be easily generalized to other kinds of crowdfunding (i.e. donation-based crowdfunding; rewards-based crowdfunding; and debt-based crowdfunding). Future studies could consider these kinds of crowdfunding activities.

This study has contributed to the understanding of e-equity crowdfunding in several aspects. For example, this study presents results that assist both researchers and practitioners in the Middle East and Saudi Arabia to develop an in-depth knowledge of e-equity crowdfunding by considering new dimensions such as RMO and information system success factors.

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.

This paper aims to draw on historical conceptions of true and false prudence within the broader context of virtue ethics ideas, to create a prudence framework for developing risk-and-ethics cultures in organisations.

The authors use a theoretical analytical approach as a means of examining plausible representations of risk as ethical practice.

While the ethical ideal of true prudence is explained primarily with reference to psychological theories of generativity, false prudence is explained as undesirable, primarily with reference to psychological problems of narcissism and the broader dark triad. True and false prudence are represented as centring upon very different motivations for foresight, each of which might set the cultural tone for organisational risk management.

This paper’s main contribution is therefore to call attention to the benefits for organisations of reflecting upon differences between true and false prudence when planning the risk management they want.

This study employed self-determination theory (SDT) and the “Motivational affordance–Psychological outcomes–Behavioral outcomes” framework to investigate the relationship between gamification features and knowledge-sharing behavior in online communities.

A theoretical model was tested with 281 Chinese users from an online social question and answer (Q&A) community. Partial least square structural equation modeling was applied to analyze the data.

The empirical results revealed that competence mediated the effects of immersion and achievement-related gamification features on knowledge sharing. Moreover, relatedness mediated the effects of immersion, achievement and social-related gamification features on knowledge sharing.

This study was conducted on a Chinese Q&A platform, and the results may not be generalizable to other cultures or service providers with different goals.

The study's findings indicate that gamification could serve as an effective toolkit for incentivizing and promoting knowledge sharing in online communities. The findings thus provide strategic insights for administrators of online communities seeking to leverage gamification designs to encourage user participation in knowledge-sharing activities.

Research on the role of gamification in promoting knowledge sharing has been limited in scope and has focused on tourism comment communities. Little evidence exists on the effect of gamification within social Q&A communities. Further, the finding of gamification's positive role in motivating knowledge sharing indicates the need for the knowledge-sharing field to focus on contextual factors.

This study aims to empirically investigate the linkages between digital trails of social signals (content and profile features of bitcoin-related tweets) and bitcoin price return using a VAR-BEKK-GARCH model.

Bitcoin-related tweets were collected every hour for six months from September 1, 2020, to February 29, 2021. The analysis involved two steps: first, examining tweet content, profiles, sentiment and emotions; and second, investigating the relationship between social signal volatility and hourly bitcoin price return.

Results indicate that bitcoin price changes can impact the sentiment expressed in tweets about bitcoin, and vice versa. While sadness exhibits a bidirectional volatility spillover with bitcoin, fear and anger display a one-period lag. Quartile analyses reveal that only fear in the second quartile shows a bidirectional spillover effect with bitcoin, while all other emotions except sadness demonstrate a unidirectional spillover effect in all remaining quartiles.

The study uses a novel two-step approach to analyze volatility spillovers between social signals and bitcoin price returns. Findings can guide investors and portfolio managers in making better allocation decisions and assist policymakers and regulators in reducing the adverse effects of bitcoin’s volatility on financial system stability.

This paper studies the interactive valuation discourses of an online user community (transfermarkt.de) that seeks to determine market values for soccer players. Despite their seemingly casual nature, these values have featured in newspapers, transfer negotiations, academic research, and capital market communication – and have thus become reified.

The paper employs netnographic research methodology to collect and thematically analyze a wide range of user entries on the platform. These entries are studied using theoretical insights from the sociology of quantification and valuation.

The analysis reveals how values are constructed in constant interaction between value-proposing users and value-justifying “experts.” This dynamic form of relational valuation positions players relative to one another as well as to actual transactions on the transfer market. In the absence of authoritative guidelines, it is this possibility and affordance for interaction that enacts a coherent valuation regime. The paper further reveals the platform's response to a disruptive event, which risked bringing the user-expert dynamics to a halt, requiring intervention from the platform to repair its valuation frame.

The paper responds to increased scholarly interests in the valuation of professional athletes. It contributes to the extant literature on valuation, first, by analyzing the dynamic valuation work that feeds into the social construction of values and, second, by studying platform participation and user interaction in a socially engineered online space.

Consumer-perceived review attitude determines consumer overall information adoption and is a core part of consumer’s online-shopping. This study aims to focus on factors that could influence consumer review attitude and can be used by marketers to shape individual information perception.

The study used the questionnaire method to collect data from online shoppers and the modelling of structural equations as an empirical approach to analyse the data.

The findings demonstrate that both systematic and heuristic cues impact the reviewer’s credibility and perceived website attitude differently, which, in turn, influence review attitude. Review characteristics, such as factuality, consistency and relevancy, have a positive relationship with reviewer credibility, while only review consistency and relevancy appears to have a relationship with review attitude. Website characteristics such as reputation, familiarity and social interactivity positively influence the website attitude, which positively influences review attitude. Apart from this, review skepticism has a significant negative relationship with review attitude.

This study could help to foster a positive attitude towards online reviews. Digital marketers need to motivate trusted reviewers to post consistent, fact-based reviews. Further improving the overall website reputation and interactivity could bring a positive attitude towards the reviews. Also, digital marketers must filter and avoid contradictory reviews or reviews that have a bipolar message and reviews expressing numerous emotions to enhance review relevance and consistency.

The current study addresses the need to understand the formation of consumer review attitude through both review and website characteristics using heuristic – systematic model. The paper captures the complex process undergone by the consumer to decipher review attitude and thereby extend the understanding of consumer information processing.