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Daniel and Titman (1997) contend that the Fama‐French three‐factor model’s ability to explain cross‐sectional variation in expected returns is a result of characteristics that firms have in common rather than any risk‐based explanation. The primary aim of the current paper is to provide out‐of‐sample tests of the characteristics versus risk factor argument. The main focus of our tests is to examine the intercept terms in Fama‐French regressions, wherein test portfolios are formed by a three‐way sorting procedure on book‐to‐market, size and factor loadings. Our main test focuses on ‘characteristic‐balanced’ portfolio returns of high minus low factor loading portfolios, for different size and book‐to‐market groups. The Fama‐French model predicts that these regression intercepts should be zero while the characteristics model predicts that they should be negative. Generally, despite the short sample period employed, our findings support a risk‐factor interpretation as opposed to a characteristics interpretation. This is particularly so for the HML loading‐based test portfolios. More specifically, we find that: the majority of test portfolios tend to reveal higher returns for higher loadings (while controlling for book‐to‐market and size characteristics); the majority of the Fama‐French regression intercepts are statistically insignificant; for the characteristic‐balanced portfolios, very few of the Fama‐French regression intercepts are significant.

This paper investigates the economic impact of corporate name changes around the time of their announcement. We analyse a sample of 107 listed Australian companies that changed their name over the period January 1995 to December 1999. We conduct separate analysis of firms having ‘major’ versus ‘minor’ name changes; of firms with coincident financial restructuring versus firms without restructuring; of small firms versus large firms and of dotcom firms versus non‐dotcom firms. Generally, we find some evidence of a negative association between the corporate name change event and abnormal returns. This seems particularly the case for those companies whose name change is deemed to be ‘major’.

This study aims to test the effects of forecast specificity on the asymmetric short‐window share market response to management earnings forecasts (MEF).

The paper examines a large sample of hand‐checked Australian data over the period 1994 to 2001. Using an analyst news benchmark, it estimates a series of regressions to investigate whether the short‐term impact from bad news announcements is greater in magnitude than from good news announcements and whether this differs between routine and non‐routine MEFs. Additionally, it examines whether (after controlling for news content of MEF) there is a differential market impact conditional on specificity: minimum versus maximum versus range versus point.

The results indicate that an asymmetric response is evident for the overall sample and a sub‐set of non‐routine forecasts. Contrary to predictions, the results show that forecast specificity, minimum, maximum, range and point MEFs make no additional contribution to the differences in the market reaction to bad or good news.

The study extends the research investigating the short‐run market impact of MEFs. The main element of innovation derives from the interaction between specificity and news content, as well as distinguishing between routine versus non‐routine cases. Notably, it found little support for the view that more specific forecasts elicit greater market responses. What the results do suggest is that managers appear to choose the form of the forecast to suit the news being delivered. In particular, bad news delivered in a minimum forecast seems to be ignored by the market.

This study systematically reviewed existing literature on the impact of economic uncertainty on property performance to highlight focus areas and spur future research amid unprecedented global uncertainty levels. Conceptually, uncertainty levels and environmental dynamism are related to investors' risk judgement and decision-making.

Peer-reviewed journal articles published from 2007 to 2022 were assembled and arranged through the Scientific Procedures and Rationales for Systematic Literature Reviews (SPAR-4-SLR) protocol. The initial search produced 2,028 results from the Web of Science and Scopus databases, which were rigorously purified for a final dataset of 70 articles. These records were subsequently assessed through content analysis, bibliographic modelling, topic modelling and thematic analysis. Recurring themes were visualised using the VOSviewer software.

The existing literature suggests that economic uncertainty negatively impacts investment volumes, returns and performance. Research has also increased since 2018, with a strong emphasis on the housing sector and developed property markets. Commercial property and emerging markets account for only 10 and 8% of previous research, respectively.

These findings highlight the negative impact of economic uncertainties on property performance and investment volumes, which necessitate careful risk assessment. Given the high susceptibility of emerging and commercial property markets to uncertainty, these markets warrant further research amid ongoing uncertainty concerns across the globe.

Given current unprecedented levels of global uncertainty, the effects of economic uncertainty have received renewed interest. This study synthesised the current understanding of how different property markets respond to increased uncertainty and outlined future research directions to enhance understanding. Themes and relationships were also integrated into a conceptual map summarising the reported effects of economic uncertainty on housing, commercial property, investment and behaviour in the property market.

Mobile devices transcend the educational affordances provided by conventional tethered electronic and traditional learning. However, empirical findings show that educators are not integrating technology effectively into the curriculum. This paper aims to discuss these issues.

In this study, a thematic synthesis methodology was used to develop and present a framework for thinking about the integration of mobile devices in teaching and learning.

The mobile learning (mlearning) integration framework comprises four main parts: beliefs, resources, methods and purpose. These four areas are elucidated to reveal the many sub-components that determine how technology is integrated.

An ecological framework is then presented to demonstrate how the individual parts of the initial framework operate through a complex, interconnected network of systems involving personal and environmental factors.

The population of internationally forcibly displaced people, which includes refugees and asylum seekers, is large and heterogeneous. To determine the varying reasons for and experiences during the migration journey, including exposure to violence and health- and integration-related needs, there is an urgent need to involve children with refugee backgrounds in research and development activities. This chapter describes a model for the child participatory approach developed at Barnafrid, a national competence centre on violence against children at Linköping University in Sweden. The model has been tested in the Long Journey to Shelter study, which investigated exposure to violence and its consequences on mental health and functional ability among forcibly displaced children and young adults. As part of this project, we conducted workshops with children (n = 36, aged 13–18 years) to design a questionnaire on exposure to community violence in the country of resettlement. Experiences recounted during the child participatory workshops indicated no problems involving newly arrived children with refugee backgrounds and Swedish-born adolescents in research activities. However, attention should be paid to proper preparatory work and the need for adjustments. We discuss the results in light of other studies on refugee child participation, the United Nations Convention on the Rights of a Child and diversity considerations.

Advances in information technology now permit the recording of massive and diverse process data, thereby making data-driven evaluations possible. This study discusses whether teachers’ information literacy can be evaluated based on their online information behaviors on online learning and teaching platforms (OLTPs).

First, to evaluate teachers’ information literacy, the process data were combined from teachers on OLTP to describe nine third-level indicators from the richness, diversity, usefulness and timeliness analysis dimensions. Second, propensity score matching (PSM) and difference tests were used to analyze the differences between the performance groups with reduced selection bias. Third, to effectively predict the information literacy score of each teacher, four sets of input variables were used for prediction using supervised learning models.

The results show that the high-performance group performs better than the low-performance group in 6 indicators. In addition, information-based teaching and behavioral research data can best reflect the level of information literacy. In the future, greater in-depth explorations are needed with richer online information behavioral data and a more effective evaluation model to increase evaluation accuracy.

The evaluation based on online information behaviors has concrete application scenarios, positively correlated results and prediction interpretability. Therefore, information literacy evaluations based on behaviors have great potential and favorable prospects.

Prior research on additions to the S & P 500 and the smaller MidCap 400 and SmallCap 600 indexes reach different conclusions regarding the key variables that explain the cross-section of announcement period abnormal returns. Most notable in this regard is that liquidity measures, long thought to be of importance, do not appear to explain abnormal returns of the S & P 500 when other factors are controlled for. By contrast, they do appear to matter for additions to the smaller stock indexes. To explore this difference, the purpose of this paper is to analyze the abnormal returns upon announcement that a stock will be added to the Nasdaq-100 Index in a cross-sectional manner, controlling for several possible alternative factors.

This paper analyzes abnormal returns upon announcement that a stock will be added to the Nasdaq-100 Index. The authors consider several possible sources of the positive price effects in a multivariate setting that controls simultaneously for measures of liquidity, arbitrage risk, operating performance and investor interest and awareness. The authors then analyze both trading volume and the bid-ask spreads. The authors finally examine analyst and investor interest, focussing on changes in analyst coverage.

The authors find that only liquidity variables are significant, but that factors representing feedback effects on the firm’s operations and level of managerial effort are not. The authors find that the average bid/ask spreads of stocks added to the Nasdaq-100 index are lower after the addition. The authors also find that the number of analysts following a stock increases significantly after addition, verifying increased analyst interest. Both forms of evidence are consistent with the hypothesis that the additions are associated with enhanced liquidity for the stocks.

The authors conclude that what does happen to a Nasdaq stock when it is announced that it will be added to the Nasdaq-100 Index is that more analysts are drawn to it, and its market liquidity is enhanced. The authors conclude that what does not happen is that there is no evidence of significant effects of enhanced managerial effort or operating performance associated with the inclusion. This difference is noteworthy because it suggests that a certification effect of additions to the S & P indexes associated with S & P’s selection process are unique to it and do not apply to the Nasdaq-100 Index additions based on market cap alone. The results provide indirect evidence on the existence and significance of the certification effect associated with additions to the S & P indexes.

This paper aims to make a systematic review of the manpower prediction model of the construction industry. It aims to determine the forecasting model's development trend, analyse the use limitations and applicable conditions of each forecasting model and then identify the impact indicators of the human resource forecasting model from an economic point of view. It is hoped that this study will provide insights into the selection of forecasting models for governments and groups that are dealing with human resource forecasts.

The common search engine, Scopus, was used to retrieve construction manpower forecast-related articles for this review. Keywords such as “construction”, “building”, “labour”, “manpower” were searched. Papers that not related to the manpower prediction model of the construction industry were excluded. A total of 27 articles were obtained and rated according to the publication time, author and organisation of the article. The prediction model used in the selected paper was analysed.

The number of papers focussing on the prediction of manpower in the construction industry is on the rise. Hong Kong is the region with the largest number of published papers. Different methods have different requirements for the quality of historical data. Most forecasting methods are not suitable for sudden changes in the labour market. This paper also finds that the construction output is the economic indicator with the most significant influence on the forecasting model.

The research results discuss the problem that the prediction results are not accurate due to the sudden change of data in the current prediction model. Besides, the study results take stock of the published literature and can provide an overall understanding of the forecasting methods of human resources in the construction industry.

Through this study, decision-makers can choose a reasonable prediction model according to their situation. Decision-makers can make clear plans for future construction projects specifically when there are changes in the labour market caused by emergencies. Also, this study can help decision-makers understand the current research trend of human resources forecasting models.

Although the human resource prediction model's effectiveness in the construction industry is affected by the dynamic change of data, the research results show that it is expected to solve the problem using artificial intelligence. No one has researched this area, and it is expected to become the focus of research in the future.

This paper aims to identify and study the effect of identified eight barriers to sustainable consumption on consumers’ intention to purchase sustainable products.

Data were collected from a self-administered field survey in India, and 315 valid responses were obtained from the survey process. Partial least square structural equation modeling analysis was carried out to establish the validity of the measures used and to examine the impact of the identified barriers on sustainable purchase intentions.

The results of this study indicate that barriers such as low willingness to pay, low functional performance, low availability of sustainable products and difficulty of integration in the normal route have a statistically significant negative impact on consumers’ sustainable purchase intentions.

The findings of this study are useful for marketers and policymakers who want to increase the consumer adoption of sustainable products in emerging markets.

This study develops measures to capture the consumers’ perception of barriers to the adoption of sustainable products.