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Crowdsourcing refers to a new business model in which enterprises or individuals publish tasks or problems, attracting freelancers or contributors to participate in solving tasks, submitting bids and allowing task seekers to choose the final solution. How to attract more quantity and quality of contributors to submit their solutions through a crowdsourcing platform has become a vital question.

In this study, the authors use web crawling to obtain 43,265 effective tasks in EPWK website (www.epwk.com) to probe how to elevate the quantity and quality of contributors via task reward design. This study uses the hierarchical linear model to probe the research questions.

Results show that, with the increase of task reward, the quantity of contributors goes up first and then goes down (inverted U shape), whereas the quality of contributors goes down first and then goes up (U sharp). Moreover, the authors investigate the moderating effects of another task design attribute, task duration. This study finds that task duration weakens the effect of task reward on the quantity of contributors while strengthening the effects of task reward on the quality of contributors.

First, this study theoretically probes two key aspects of task performance, namely, the quantity and quality of contributors, which expand the scope of task performance evaluation. Second, this study reconciles previous concern about the relationship of task reward and performance, which is different from previous studies that have paid more attention to the single perspective of their relationship. Finally, the authors investigate the moderating effects of task duration, which further uncover the mechanism behind task reward and performance, that is, the quantity and quality of task contributors.

Since 2015, China has made efforts to reform its intellectual property rights (IPR) system to better protect and stimulate innovation. These reforms are a result of the demand for more stringent intellectual property (IP) protection from China’s domestic, innovative industries and a measure to ease the pressure exerted by its foreign trading partners, particularly against the background of the US-China trade dispute that started at the beginning of 2018. This paper summarizes these reforms and their implications.

This paper combines a variety of sources, including academic articles, government websites, news reports, industry surveys and expert opinions, to offer insights in China’s IPR system and its recent reforms.

This paper summarizes and discusses (1) the state’s law amendments, including the 2015 amendment of the “Law on Promoting the Transformation of Scientific and Technological Achievements”, the second amendment of the “Anti-Unfair Competition Law” with regard to trade secret protection, the fourth amendment of the “Patent Law”, and the legislations and regulations addressing the criticisms of the US administration over China’s so-called “forced” technology transfer policies; (2) the establishment of the specialized IP courts and tribunals since 2014; (3) the restructuring of the State IP Office; and (4) the issuing of an “Outline for Building an IPR Powerhouse (2021–2035)”.

This paper highlights China’s efforts to make its IPR system stronger and more just. It also discusses international observers’ reactions and pinpoints specific areas for further improvement.

The paper develops a model for enhancing R&D productivity for Indian public funded laboratories. The paper utilizes the productivity data of five Council of Scientific and Industrial Research (CSIR) laboratories for analysis and to form the constructs of the model.

The weighted average method was employed for analyzing the rankings of survey respondents pertaining to the significant measures enhancing R&D involvement of researchers and significant non-R&D jobs. The authors have proposed a model of productivity. Various individual, organizational and environmental constructs related to the researchers working in the CSIR laboratories have been outlined that can enhance R&D productivity of researchers in Indian R&D laboratories. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was used to find the predictability of the productivity model.

The organizational factors have a crucial role in enhancing the R&D outputs of CSIR laboratories. The R&D productivity of researchers can be improved through implementing the constructs of the proposed model of productivity.

The R&D productivity model can be adapted by the R&D laboratories to enhance researchers’ R&D involvement, increased R&D outputs and achieving self-sustenance in long run.

The R&D laboratories can initiate exercises to explore the most relevant factors and measures to enhance R&D productivity of their researchers. The constructs of the model can function as a guideline to introduce the most preferable research policies in the laboratory for overall mutual growth of laboratory and the researchers.

Hardly any studies have been found that have focused on finding the measures of enhancing R&D involvement of researchers and the influence of significant time-intensive jobs on researchers’ productivity.

The article extends the distinction of semantic from syntactic labour to comprehend all forms of mental labour. It answers a critique from de Fremery and Buckland, which required envisaging mental labour as a differentiated spectrum.

The paper adopts a discursive approach. It first reviews the significance and extensive diffusion of the distinction of semantic from syntactic labour. Second, it integrates semantic and syntactic labour along a vertical dimension within mental labour, indicating analogies in principle with, and differences in application from, the inherited distinction of intellectual from clerical labour. Third, it develops semantic labour to the very highest level, on a consistent principle of differentiation from syntactic labour. Finally, it reintegrates the understanding developed of semantic labour with syntactic labour, confirming that they can fully and informatively occupy mental labour.

The article further validates the distinction of semantic from syntactic labour. It enables to address Norbert Wiener's classic challenge of appropriately distributing activity between human and computer.

The article transforms work in progress into knowledge for diffusion.

It has practical implications for determining what tasks to delegate to computational technology.

The paper has social implications for the understanding of appropriate human and machine computational tasks and our own distinctive humanness.

The paper is highly original. Although based on preceding research, from the late 20th century, it is the first separately published full account of semantic and syntactic labour.

The proliferation of non-fungible token (NFT)-based crypto-art platforms has transformed how creators manage, own and earn money through the creation, assets and identity of their digital works. Despite this, no studies have examined the drivers of continuous content contribution behavior (CCCB) toward NFTs. Hence, this study draws on the theory of relational bonds to examine how various relational bonds affect feelings of psychological ownership, which, in turn, affects CCCB on metaverse platforms.

Using structural equation modeling and importance-performance matrix analysis, an online survey of 434 content creators from prominent NFT platforms empirically validated the research hypotheses.

Financial, structural, and social bonds positively affect psychological ownership, which in turn encourages CCCBs. The results of the importance-performance matrix analysis reveal that male content creators prioritized virtual reputation and social enhancement, whereas female content creators prioritized personalization and monetary gains.

We examine Web 3.0 and the NFT creators’ network that characterizes the governance practices of the metaverse. Consequently, the findings facilitate a better understanding of creator economy and meta-verse commerce.

Literature on product innovation (PI) has witnessed tremendous growth over the past 60 years. Yet, there has been a dearth of a comprehensive review of the extant PI research maintaining the breadth and depth of the topic. To seal this gap, this paper aims to explore the intellectual foundations and emergent trends in 2,588 research papers on PI from 1962 to 2022.

The paper adopts a bibliometric analysis approach via visualization maps of prolific contributions, co-citation, co-occurrence and thematic networks.

Emerging streams, frontiers and hotspots in PI research are revealed and a knowledge graph is developed. The review provides a broad spectrum of opportunities that enrich imminent research to better inform business decisions.

The review addresses 20 research questions associated with barely examined areas in PI research that require further analysis in imminent research.

To the best of the authors' knowledge, this is the first review that comprehensively synthesizes the intersected knowledge base of PI research using bibliometric analysis of a vast number of documents within a long time span.

This chapter argues that the Americanisation of online policing has questionable impacts in Australian prosecutions involving drugs obtained and distributed through dark web cryptomarkets. The authors describe several Australian prosecutions of mid- and low-level dealers who have accessed drugs through the dark web and contrast these with the United States (US) case against the cryptomarket, AlphaBay. The discussion in this study emphasises how Australian police and courts view the relative weight of dark web activity associated with the domestic and transnational supply of illicit drugs that result in formal prosecutions. The authors suggest that large-scale forms of online and dark web police surveillance undertaken by US enforcement agencies reflect Ethan Nadelmann’s (Cops across borders: the internationalization of US criminal law enforcement, University Park: Pennsylvania State University Press, 1993) thesis on the Americanisation of global policing through transnational communications networks. The authors then explain how key elements of transnational dark web drug supply appear to have a marginal bearing on criminal investigations into low- and mid-level traffickers in Australia, which rely on conventional surveillance tactics to identify clandestine mail pickups, physical distribution methods, and irregular money trails. However, the authors then illustrate how the Americanisation of online policing that targets high-level entrepreneurs and seeks to dismantle or eliminate dark web cryptomarkets has important implications on Australian reforms aimed at enhancing online surveillance powers to target a range of crimes that are often wrongly associated with illicit drug cryptomarkets. The authors conclude by demonstrating how intensive dark web surveillance has limited direct impact on routine drug policing in Australia, with dark web communications simply another medium for facilitating the physical detection of illicit transnational drug transactions.

The purpose of this study is to gain insight into the dynamics and considerations of professionals regarding the sharing of tacit, personal knowledge in their practice.

Adopting a social-constructivist ontology, the qualitative design deploys semi-structured interviews and focus groups. Data were coded, and analysed through interrelating and reasoning.

Personal knowledge is difficult to share precisely, but can be shared to some extent using reflection and stories. Knowledge also provides a position and professional agency, emphasising boundaries and impacting the decisions on interaction and sharing. As such, professional commitment is vulnerable and contextual and, by extension, material becomes part of this interplay of professional practice and collaborative development.

Findings imply that exchange and use of knowledge and material present in organisations are impacted by individual professionals’ autonomy and decisions, which consequently impact on employees’ practice. This calls for research that focuses on individual factors such as autonomy, professionalism and attitudes in addition to organisational and facilitative matters.

Stimulating professional commitment and interpersonal learning is a matter of valuing personal knowledge and practice to avoid protectionism, boundaries and segregated agency. Management and professionals should consider how and why individuals exchange their personal knowledge, paying attention to social structures and individuals’ voices and objectives in forming communities.

This study combines the concept of tacit knowledge with the younger field of practice theory. By connecting personal knowledge to practice, it extends agency to the material world and offers a more individual perspective to knowledge sharing in and between entities.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.

Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.

The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.

The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.

This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.