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This research investigates the differential impact of information technology (IT) investments based on their features, such as investment in data management capability, security improvement, IT outsourcing or new IT infrastructure. The Long-Horizon Event Study (LHES) is essential for providing a more appropriate measure of the value of IT investments because firms' strategic decisions often set long-horizon and large-scale organizational goals, and there is inherent uncertainty regarding future cash flows resulting from these investments. Therefore, the authors aim to analyze how announcements of IT investments affect the firm's abnormal stock returns over the long term and to compare the differential impact of different features of IT investment.

The authors gathered IT investment announcements and stock data of listed firms in Korea between 2000 and 2018, and the monthly stock market returns over the 5 years after the announcements. To measure the differential impact of IT investments based on the investment features, the authors separate announcements data into five groups. A LHES is used to estimate the long-term effects of IT investment announcements.

The results indicate that announcements of IT investments had a long-term positive effect on firm performance. Additionally, the findings reveal differential effects of IT investments across industries and investment features. Notably, news of self-developed IT investments and IT investments in the manufacturing industry had significantly positive effects. However, contrary to common belief, announcements of investments in so-called essential IT areas such as data, security, or new IT infrastructure did not yield significant effects.

Although the need for LHES has been emphasized in information systems research, few follow-up studies have been conducted since Barua and Mani (2018). This is primarily due to the challenges associated with collecting large-scale abnormal stock returns data over a long horizon. This research represents the first LHES to investigate the differential impact of IT investments based on their features. By doing so, this study can provide valuable insights for decision-makers within firms, helping them understand the time horizon of market outcomes of IT investments based on their features. Furthermore, this work extends the scope of LHES to comprehend the differential impacts of investment features. For instance, managers need to grasp that so-called essential IT investments, such as data management, security enhancements or new IT infrastructure, may not necessarily generate long-term market value.

The article investigates the wealth generation from takeover bids through an event study, analyzing the impact of announcements on stock returns of target and bidder firms across the industries and related and unrelated industry acquisitions. It aims to provide insights into financial implications for shareholders and market participants, contributing to understanding merger dynamics and informing investment decisions.

The methodology involves data collection of announcement dates, defining event and estimation windows. Market models and four-factor models are applied to compute abnormal returns. Linear regression estimates return, which is aggregated and tested for significance, providing insights into the wealth effects of takeover announcements.

Analysis reveals positive returns for both firms' shareholders on the announcement day, particularly significant for target firms. Pre-announcement, positive abnormal returns suggest potential information leakage, but reverse post announcement. Comparative model analysis emphasizes the role of systematic risk. Notably, a prolonged bidding process benefits the target firm. Trading in target company stocks under unrelated industry acquisitions appears to be more beneficial during the bidding.

This article introduces a novel approach by utilizing a four-factor model for computing abnormal returns, unlike previous research relying solely on the market model. It also focuses separately on related and unrelated industry acquisitions. This methodology captures comprehensive systematic risk, resulting in more conservative and robust abnormal returns. This methodological advancement addresses existing gaps in the literature and provides actionable insights for stakeholders in mergers and acquisitions.

This paper evaluates the risk-adjusted returns, selectivity, market timing skills and persistence of the performance of Nigerian pension funds.

Annual return data of 23 pension funds that operated in Nigeria between 2018 and 2022 were obtained from the National Pension Commission (PenCom). Risk-adjusted return was appraised using the Treynor ratio, Sharpe ratio and Jensen alpha, while the Treynor–Mazuy and Henriksson–Merton multiple regression models were applied to decompose selective and timing skills. Performance persistence was assessed using the contingency table and rank correlation models.

Evidence shows that pension funds deliver excess risk-adjusted returns and exhibit selective skills. However, the evidence does not support the presence of timing skills, and there is overwhelming evidence that good (bad) performance does not repeat.

An evaluation of the investment performance of pension funds is crucial for ensuring the financial stability of retirees, maintaining economic stability and making informed investment decisions. It serves the interests of pensioners, pension fund managers, regulators and the broader economy. Our evidence that pension funds generate positive excess returns is a departure from most of the literature on managed funds. We recommend that more Nigerians should leverage the pension fund industry to grow their wealth and prepare for retirement.

This study, to our knowledge, is the first to appraise all the key facets of the investment performance of pension funds in the Nigerian context.

This paper aims to examine the impact of announcements related to 77 interventions by 46 listed Indian pharmaceutical firms during COVID-19 on the abnormal returns of the firms. The study also finds the variables which explain cumulative abnormal returns (CARs).

This study uses standard event methodology to compute the abnormal returns of firms announcing pharmaceutical interventions in 2020 and 2021. Besides this, the multilayer perceptron technique is applied to identify the variables that influence the CARs of the sample firms.

The results show the presence of abnormal returns of 0.64% one day before the announcement, indicating information leakage. The multilayer perceptron approach identifies five variables that explain the CARs of the sample companies, which are licensing_age, licensing_size, size, commercialization_age and approval_age.

The study contributes to the efficient market literature by revealing how firm-specific nonfinancial disclosures affect stock prices, especially in times of crisis like pandemics. Prior research focused on determining the effect of COVID-19 variables on abnormal returns. This is the first research to use artificial neural networks to determine which firm-specific variables and pharmaceutical interventions can influence CARs.

This study introduces a new type of platform recommendation about mutual funds and draws on the signaling theory to conduct a quasi-experimental design to investigate how the platform recommendation influences investors’ investment decisions. Moreover, the authors examine the combined effect of star ratings and the platform recommendation on fund flow and test the investment value of recommended funds.

This study implements a quasi-experimental design based on 1,295 mutual funds traded on Alipay’s online platform to test the hypotheses.

The empirical results show that the recommended funds received higher fund flows from investors when the platform recommendation was established. Moreover, a substitution effect between tag recommendation and star ratings on fund flow was identified. We also uncovered that investing in platform-recommended funds can yield significant and higher fund returns for investors than those without platform recommendations.

Our findings shed new insights into the role of platform recommendations in helping fund investors make investment decisions and contribute to the business of online mutual fund transactions by investigating the effect of platform recommendations on fund flow and performance.

This systematic review aimed to identify and categorize applications using Fast Healthcare Interoperability Resources (FHIR) to support activities outside of direct healthcare provision.

A systematic electronic search was performed, and 53 studies were included after the selection process.

The results show that FHIR is being used to support (1) clinical research (i.e. clinical research based on interventional trials, data interoperability to support clinical research and advanced communication services to support clinical research), (2) public health and (3) medical education. Despite the FHIR potential to support activities outside of direct healthcare provision, some barriers were identified, namely difficulties translating the proposed applications to clinical environments or FHIR technical issues that require further developments.

This study provided a broad review of how FHIR is being applied in clinical activities outside of direct clinical care and identified three major domains, that is, clinical research, public health and medical education, being the first and most representative in terms of number of publications.

Previous research found that when exercise partners provided social support to each other, both participants engaged in higher levels of activity (Gellert et al., 2011). These results suggest that there may be benefits to providing inclusive physical activity (PA) programming to individuals with autism; however, little research has explored specific strategies. The purpose of this study is to explore the use of a behavioral intervention consisting of visual social stories and additional intervention components (i.e. prompting, checklists) to teach adolescents/young adults with autism and their workout partners without disabilities to provide social support to one another during partnered PA.

A multiple probe across dyads was used to explore the effects of the intervention on social support (i.e. verbal encouragement and feedback).

The results indicate the treatment was effective during the intervention phase. When partners and settings changed during generalization, results were maintained in all but one participant.

Areas for future research and implications for practice to support inclusive PA for autistics are discussed.

Red Riding Hood is said to have been assembled from folktales that pre-date the collector Charles Perrault's 1697 re-telling and initial publishing (Dundes, 1989; Zipes, 1993). Since then, it is a story that has been re-told and re-imagined many times in various media contexts, with Beckett suggesting that it is one of the most familiar icons of Western culture, and a ‘highly effective intertextual referent’ (Beckett, 2002, p. XVI). Even though this story has been generally regarded as a children's tale, adult themes of sexuality and transgression have been explored in modern re-conceptions. In this chapter, we examine the representation of gender and masculinity in commercial media output: the 2011 American film Red Riding Hood (Hardwicke, 2011) and the pilot episode of the NBC series Grimm (2011). In Red Riding Hood, a romantic horror film, the male characters may be regarded as satellites that cluster around the female protagonist, whereas in Grimm, through its generic fusion of police procedural and horror genres, the text plays upon strong established examples of traditional male roles alongside more nuanced and contemporary representations of masculinity. Our analysis explores themes of transformation and heteronormativity and the extent to which the texts challenge or conform to traditional tellings.

Some fairy tale characters have transcended their original fairy tale genre and their influence can be perceived in other fictional genres. One illustration of this is the character of Blue Beard. This story, written by Charles Perrault, was first published in 1697 (Hermansson, 2010, p. 2). It moved through several themes or topics from anxiety to money to the right of the husband and wives (Warner, 2014, p. 82). In Perrault's story, Blue Beard is conceived as a serial killer and a jealous husband. ‘Whatever the medium, whatever the date: in opera, cartoon, X-rated film or graphic novel, he is an archetypal serial murderer, terrifying and yet alluring’ (2015, p. 76).

The influence of this character and examples that carry, at the very least, remnants of Blue Beard can be clearly identified in several contemporary narratives. In the BBC television series The Fall (Cubitt, 2013), Jamie Dornan portrays a serial killer named Paul Spector. Alternatively known as the Belfast Strangler, Spector, like Blue Beard, has a wife and children who are unaware of his murderous spree. Another example of the attempted enforcement of extreme patriarchy can be seen in Cult, the seventh season of Ryan Murphy's American Horror Story anthology series. The season is dedicated to the 2016 US election between Donald Trump and Hillary Clinton (Raddish, 2017). In this fictional narrative Kai Anderson (Evan Peters) symbolises toxic masculinity and models patriarchal archetypes to create a cult and become a senator.

Through a contextual post-structuralist analysis of the aforementioned characters, this chapter intends to examine the representation and evolution of male character under the umbrella of the fairy tale ‘man’.

The purpose of this paper is to investigate the vulnerability of smallholder farmers to climate change in northwest Ethiopia.

To achieve this aim, data was collected from a survey of 352 households, which were stratified into three groups: Lay Gayint (138 or 39%), Tach Gayint (117 or 33%) and Simada district (97 or 28%). To gain a deeper understanding of the vulnerability of these households, two approaches were used: the livelihood vulnerability index (LVI), consisting of 32 indicators, and the socioeconomic vulnerability index (SeVI), containing 31 indicators. Furthermore, qualitative data was obtained through focus group discussions conducted in six randomly chosen groups from the three districts, which were used to supplement the findings.

Both methods indicate that Simada is the most vulnerable district, followed by Tach Gayint and Lay Gayint. According to the SeVI approach, Simada district showed the highest level of sensitivity and exposure to climate-related hazards, as well as the lowest score for adaptive capacity. However, using the LVI approach, Simada district was found to have the highest sensitivity to climate effects and exposure to climate-related hazards, along with a higher adaptive capacity than both Lay Gayint and Tach Gayint districts.

Although there are numerous studies available on the vulnerability of farmers to climate change, this particular study stands out by using and contrasting two approaches – the LVI and the SeVI – to assess the vulnerability of households in the study area. Previous research has indicated that no single approach is sufficient to evaluate climate change vulnerability, as each approach has its own strengths and limitations. The findings of this study have significant implications for policymakers and development practitioners, as they can use the results to identify the households that are most vulnerable to climate change. This will enable them to design adaptation options that are tailored to the specific needs of each community and that will effectively address the risks of current and future climate change.