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Peer review has been with humans for a long time. Its effective inception dates back to World War II resulting information overload, which imposed a quantitative and qualitative screening of publications. Peer review was beset by a number of accusations and critics largely from the biases and subjective aspects of the process including the secrecy in which the processes became standard. Advent of the Internet in the early 1990s provided a manner to open peer review up to make it more transparent, less iniquitous, and more objective. This chapter investigates whether this openness led to a more objective manner of judging scientific publications. Three sites are examined: Electronic Transactions on Artificial Intelligence (ETAI), Atmospheric Chemistry and Physics (ACP), and Faculty of 1000 (F1000). These sites practice open peer review wherein reviewers and authors and their reviews and rebuttals are available for all to see. The chapter examines the different steps taken to allow reviewers and authors to interact and how this allows for the entire community to participate. This new prepublication reviewing of papers has to some extent, alleviated the biases that were previously preponderant and, furthermore, seems to give positive results and feedback. Although recent, experiences seem to have elicited scientists’ acceptance because openness allows for a more objective and fair judgment of research and scholarship. Yet, it will undoubtedly lead to new questions which are examined in this chapter.

The use of structured literature review methods like bibliometric analysis is growing in the management fields, but there is limited knowledge on how they can be facilitated by technology. Hence, we conducted a broad overview of software tools, their roles, and limitations in structured (bibliometric) literature reviewing activities. Subsequently, we show that several software tools are freely available to aid in searching the literature, identifying/ extracting relevant publications, screening/assessing quality of the extracted data, and performing analyses to generate insights from the literature. However, their applications may be confronted with several challenges such as limited analytical and functional capabilities, inadequate technological skills of researchers, and the fact that the researcher's insights are still needed to generate compelling conclusions from the results produced by software tools. Consequently, we contribute toward advancing the methodologies for performing structured reviews by providing a comprehensive and updated overview of the knowledge base of key technological software tools and the conduct of structured or bibliometric literature reviews.

The purpose of this paper is to review the literature on knowledge transfer in interorganizational partnerships. The aim is to assess the advances in this field by addressing the questions: What factors impact knowledge transfer in interorganizational partnerships? How do these factors interact with each other?

The study reports results of a literature review conducted in ten top journals between 2000 and 2017 in the fields of strategy and innovation studies.

The review identifies three overarching themes, which were organized according to 14 research questions. The first theme discusses knowledge in itself and elaborates on aspects of its attributes. The second theme presents the factors that influence interorganizational knowledge transfer at the macroeconomic, interorganizational, organizational and individual levels. The third theme focuses on the consequences, namely, effectiveness and organizational performance.

Partnership managers may improve and adjust contracts, structures, processes and routines, as well as build support mechanisms and incentives to guarantee effectiveness in knowledge transfer in partnerships.

The study proposes a novel theoretical framework that links antecedents, process and outcomes of knowledge transfer in interorganizational partnerships, while also identifying aspects that are either less well researched or contested and thereby suggesting directions for future research.

Online reviews in online health communities (OHCs) have been a vital information source for patients. The extant literature on the bias effects of helpful reviews mainly concentrates on traditional e-commerce, whereas research on OHCs is still rare. Thus, based on the heuristic-systematic model (HSM), this research explores how two unique reviewer characteristics in OHCs, which may induce attribution bias and confirmation bias, affect review helpfulness and how review length moderates these relationships.

This research analyzed 130,279 reviews collected from haodf.com (one of the representative OHCs in China) by adopting the negative binomial regression to test our research model.

The results indicate that reviewer cured status positively influences review helpfulness, whereas reviewer recommendation source negatively affects review helpfulness. Moreover, the effects of the two reviewer cues on review helpfulness will be weaker for longer reviews.

First, as one of the initial attempts, the current study investigates the effects of confirmation bias and attribution bias of online reviews in OHCs by exploring the effects of two unique reviewer characteristics on review helpfulness. Second, the weakening moderating effects of review length on the two bias effects provide empirical support for the theoretical arguments of the HSM in OHCs.

In recent years, practitioners have identified a number of problems with traditional performance management (PM) systems, arguing that PM is broken and needs to be fixed. In this chapter, we review criticisms of traditional PM practices that have been mentioned by journalists and practitioners and we consider the solutions that they have presented for addressing these concerns. We then consider these problems and solutions within the context of extant scholarly research and identify (a) what organizations should do going forward to improve PM practices (i.e., focus on feedback processes, ensure accountability throughout the PM system, and align the PM system with organizational strategy) and (b) what scholars should focus research attention on (i.e., technology, strategic alignment, and peer-to-peer accountability) in order to reduce the science-practice gap in this domain.

Investigates the differences in protocols between arbitral tribunals and courts, with particular emphasis on US, Greek and English law. Gives examples of each country and its way of using the law in specific circumstances, and shows the variations therein. Sums up that arbitration is much the better way to gok as it avoids delays and expenses, plus the vexation/frustration of normal litigation. Concludes that the US and Greek constitutions and common law tradition in England appear to allow involved parties to choose their own judge, who can thus be an arbitrator. Discusses e‐commerce and speculates on this for the future.

Online review bias research has predominantly focused on self-selection biases on the user’s side. By collecting online reviews from multiple platforms and examining their biases in the unique digital environment of “Chinanet,” this paper aims to shed new light on the multiple sources of biases embedded in online reviews and potential interactions among users, technical platforms and the broader social–cultural norms.

In the first study, online restaurant reviews were collected from Dianping.com, one of China's largest review platforms. Their distribution and underlying biases were examined via comparisons with offline reviews collected from on-site surveys. In the second study, user and platform ratings were collected from three additional major online review platforms – Koubei, Meituan and Ele.me – and compared for possible indications of biases in platform's review aggregation.

The results revealed a distinct exponential-curved distribution of Chinese users’ online reviews, suggesting a deviation from previous findings based on Western user data. The lack of online “moaning” on Chinese review platforms points to the social–cultural complexity of Chinese consumer behavior and online environment that goes beyond self-selection at the individual user level. The results also documented a prevalent usage of customized aggregation methods by review service providers in China, implicating an additional layer of biases introduced by technical platforms.

Using an online–offline design and multi-platform data sets, this paper elucidates online review biases among Chinese users, the world's largest and understudied (in terms of review biases) online user group. The results provide insights into the unique social–cultural cyber norm in China's digital environment and bring to light the multilayered nature of online review biases at the intersection of users, platforms and culture.

This article aims to systematically review the literature published in recognized journals focused on cognitive heuristic-driven biases and their effect on investment management activities and market efficiency. It also includes some of the research work on the origins and foundations of behavioral finance, and how this has grown substantially to become an established and particular subject of study in its own right. The study also aims to provide future direction to the researchers working in this field.

For doing research synthesis, a systematic literature review (SLR) approach was applied considering research studies published within the time period, i.e. 1970–2021. This study attempted to accomplish a critical review of 176 studies out of 256 studies identified, which were published in reputable journals to synthesize the existing literature in the behavioral finance domain-related explicitly to cognitive heuristic-driven biases and their effect on investment management activities and market efficiency as well as on the origins and foundations of behavioral finance.

This review reveals that investors often use cognitive heuristics to reduce the risk of losses in uncertain situations, but that leads to errors in judgment; as a result, investors make irrational decisions, which may cause the market to overreact or underreact – in both situations, the market becomes inefficient. Overall, the literature demonstrates that there is currently no consensus on the usefulness of cognitive heuristics in the context of investment management activities and market efficiency. Therefore, a lack of consensus about this topic suggests that further studies may bring relevant contributions to the literature. Based on the gaps analysis, three major categories of gaps, namely theoretical and methodological gaps, and contextual gaps, are found, where research is needed.

The skillful understanding and knowledge of the cognitive heuristic-driven biases will help the investors, financial institutions and policymakers to overcome the adverse effect of these behavioral biases in the stock market. This article provides a detailed explanation of cognitive heuristic-driven biases and their influence on investment management activities and market efficiency, which could be very useful for finance practitioners, such as an investor who plays at the stock exchange, a portfolio manager, a financial strategist/advisor in an investment firm, a financial planner, an investment banker, a trader/broker at the stock exchange or a financial analyst. But most importantly, the term also includes all those persons who manage corporate entities and are responsible for making their financial management strategies.

Currently, no recent study exists, which reviews and evaluates the empirical research on cognitive heuristic-driven biases displayed by investors. The current study is original in discussing the role of cognitive heuristic-driven biases in investment management activities and market efficiency as well as the history and foundations of behavioral finance by means of research synthesis. This paper is useful to researchers, academicians, policymakers and those working in the area of behavioral finance in understanding the role that cognitive heuristic plays in investment management activities and market efficiency.

The purpose of this study was to investigate consumer preference formation in presence of reviews coming from traditional and electronic word of mouth (eWOM) under different conditions of review valence.

Scenario-based experimental design was used for this study. A total of 172 respondents were randomly assigned across four experimental scenarios and a control group. Across-groups comparisons were conducted using post hoc Dunnett’s test for checking the hypothesis.

The study revealed that negativity bias impacted consumer judgement formation to such an extent that positive valence from higher credibility source (WOM) is over-powered by negative reviews from lesser trusted source (eWOM). Further, a negative valence review from higher credibility source (WOM) may not be countered even in presence of consensus of positive valence review coming from a higher number of positive eWOM.

This study contributes to existing literature by examining consumer preference behaviour in presence of reviews coming from traditional and eWOM under different conditions of review valence. While earlier studies have examined individual role of WOM and eWOM under differing valence conditions, this paper extends literature by studying combined effect of WOM and eWOM under different review valence.