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The purpose of this paper is to estimate the effects of human resource management (HRM) practices on firm outcomes at the firm level in Vietnam.

The paper employs a fixed-effects framework for the estimation using a panel sample of manufacturing firms from small- and medium-sized enterprise surveys between 2009 and 2013.

The paper finds that, on average, a firm that provides the training for new workers gains roughly 13.7, 10 and 14.9 percent higher in output value per worker, value added per worker and gross profit per worker, respectively, than the counterpart. Moreover, an additional ten-day training duration for new employees on average leads to a 4.1 percent increase in output value per worker, a 3.0 percent rise in value added per worker and a 3.0 percent growth in gross profit per worker. The paper also uncovers that a marginal 10 percent of HRM spending results in about 2 and 1.6 percent rises in output value per worker and value added per worker, respectively.

Using the case of Vietnam, this paper shows the important roles of HRM practices in explaining firm outcomes.

Failure plays a pivotal role in entrepreneurial learning. Knowledge of the learning process that enables an entrepreneur to re-emerge stronger after a failure, though considerable, is fragmented. This paper systematically collects relevant literature, assigns it to the stages of the experiential learning process (concrete experience, reflective observation, abstract conceptualization, active experimentation; Kolb, 1984), evaluates the research coverage of each stage and identifies promising avenues for future research.

This systematic literature review follows the guidelines articulated by Short (2009) and Tranfield et al. (2003), using Web of Science and EBSCO as primary data sources. Kolb’s (1984) experiential learning theory provides a basis for organizing the identified material into a framework of entrepreneurial learning from failure.

The literature provides insights on all stages of the process of entrepreneurial learning from failure. Particularly well elaborated are the nature of failure and its triggering effect for reflection, the factors influencing reflection, the contents of the resulting learning and their application in entrepreneurial re-emergence. Other topics remain under-researched, including alternative modes of recovery, the impact of personal attributes upon reflection, the cognitive processes underlying reflection, the transformation of failure-based observations into logically sound concepts and the application of this learning in non-entrepreneurial contexts.

This review provides the most complete overview of research into the process of entrepreneurial learning from failure. The systematic, theory-based mapping of this literature takes stock of current knowledge and proposes areas for future research.

This study clarifies the factors that foster individual innovative behaviour in the public sector by examining the effects and roles of empowering leadership, work group cohesiveness and individual learning orientation. This study also explores the direct effect of empowering leadership on work group cohesiveness and individual learning orientation, the influence of work group cohesiveness on individual learning orientation and the mediating roles of work group cohesiveness and individual learning orientation.

Data were collected from an online survey of respondents working in a public sector organization. Partial least squares structural equation modelling and mediation analysis by the bootstrap method were used for the data analysis.

Empowering leadership and individual learning orientation had significant direct effects on individual innovative behaviour. Both empowering leadership and work group cohesiveness have significant direct effects on individual learning orientation. Empowering leadership was positively related to work group cohesiveness. The mediation analysis revealed that individual learning orientation mediates the relationships between empowering leadership and individual innovative behaviour and between work group cohesiveness and individual innovative behaviour.

The study focuses on three factors that foster individual innovative behaviour in a public sector organization.

This study offers new insights into the factors that foster individual innovative behaviour in the public sector. The findings reveal the importance of using a balanced leadership style and encourage learning in the workplace for individual innovativeness by public leaders.

Artificial intelligence (AI) reasoning is fuelled by high-quality, detailed behavioural data. These can usually be obtained by the biometrical sensors embedded in smart devices. The currently used data collecting approach, where data ownership and property rights are taken by the data scientists, designers of a device or a related application, delivers multiple ethical, sociological and governance concerns. In this paper, the author is opening a systemic examination of a data sharing concept in which data producers execute their data property rights.

Since data sharing concept delivers a substantially different alternative, it needs to be thoroughly examined from multiple perspectives, among them: the ethical, social and feasibility. At this stage, theoretical examination modes in the form of literature analysis and mental model development are being performed.

Data sharing concepts, framework, mechanisms and swift viability are examined. The author determined that data sharing could lead to virtuous data science by augmenting data producers' capacity to govern their data and regulators' capacity to interact in the process. Truly interdisciplinary research is proposed to follow up on this research.

Since the research proposal is theoretical, the proposal may not provide direct applicative value but is largely focussed on fuelling the research directions.

For the researchers, data sharing concepts will provide an alternative approach and help resolve multiple ethical considerations related to the internet of things (IoT) data collecting approach. For the practitioners in data science, it will provide numerous new challenges, such as distributed data storing, distributed data analysis and intelligent data sharing protocols.

Data sharing may post significant implications in research and development. Since ethical, legislative moral and trust-related issues are managed in the negotiation process, data can be shared freely, which in a practical sense expands the data pool for virtuous research in social sciences.

The paper opens new research directions of data sharing concepts and space for a new field of research.

This study aims to evaluate the presence of abnormal returns due to stock splits or reverse stock splits in the Brazilian capital market context.

The event study technique was used on data from 518 events that occurred in a 30-year period (1987–2016), comprising 167 stock splits and 351 reverse stock splits.

The results revealed the occurrence of abnormal returns around the time the shares began trading stock splits or reverse stock splits at a statistical significance level of 5%. The main conclusion is that stock split and reverse stock split operations represent opportunities for extraordinary gains and may serve as a reference for investment strategies in the Brazilian stock market.

This study innovates by including reverse stock splits, as the existing literature focuses on stock splits, and by testing two distinct “zero” dates that of the ordinary general meeting that approved the share alteration and the “ex” date of the alteration, when the shares were effectively traded, reverse split or split.

People often face constraints such as a lack of time or information in taking decisions, which leads them to use heuristics. In these situations, fast and frugal rules may be useful for making adaptive decisions with fewer resources, even if it leads to suboptimal choices. When applied to financial markets, the recognition heuristic predicts that investors acquire the stocks that they are aware of, thereby inflating the price of the most recognized stocks. This paper aims to study the profitability against the market of the most recognized stocks in Europe.

In this paper, the authors perform a survey and use Google Trends to study the profitability against the market of the most recognized stocks in Europe.

The authors conclude that a recognition heuristic portfolio yields poorer returns than a market portfolio. In contrast, from the data collected on Google Trends, weak evidence was found that strong increases in companies monthly search volumes may lead to abnormal returns in the following month.

The applied investment strategy does not account for transaction costs, which may jeopardize its profitability given the fact that it is necessary to revise the portfolio on a monthly basis. Despite the results obtained, they are useful to understanding the performance of recognition heuristic strategies over a comprehensive time horizon, and it would be interesting to depict its viability during different market conditions. This analysis could provide additional information about a preferable scenario for employing our strategies and, ultimately, enhance the profitability of recognition heuristic strategies.

Through the exhaustive analysis performed here on the recognition heuristic in the European stock market, it is possible to conclude that no evidence was found for the viability of exploring this type of strategy. In fact, the investors would always gain better returns when adopting a passive investment strategy. Therefore, it would be wise to assume that the European market presents at least a degree of efficiency where no investment would yield abnormal returns following the recognition heuristic.

The main objective of this paper is to study the performance of the recognition heuristic in the financial markets and to contribute to the knowledge in this field. Although many authors have already studied this heuristic when applied to financial markets, there is a lack of consensus in the literature.