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Are franking credit balances priced? This paper aims to investigate the valuation of franking credit balances via a determinant analysis and value relevance analysis.

The determinant analysis examines the factors that contribute to the increasing cumulative level of franking credit balances. Value relevance studies explore whether franking credit balances are priced in the market.

The results provide strong evidence of a size effect that the level of franking credit balances increases with firm size and weak evidence of an international focus effect that the level of franking credit balances increases with international ownership. They also find an individual dividend clientele effect that the level of franking credit balances decreases with individual ownership. They find significant evidence that franking credit balances are priced in the market. One dollar of franking credit is worth 1.4 dollars in firm value. That franking balances are capitalized at more than their face value suggests that franking credits signal firms' future dividend policy. They also find that the market valuation of franking balances increases with firm size but decreases with international focus.

This study provides direct evidence that franking credit balances are capitalized into equity prices. In the determinant analysis, this paper improves Heaney's (2009) model by using the percentage of international ownership as the proxy of international focus, thus addressing the limitation of his measure. In the value relevance tests, the study uses a modified model that includes log-transformation to reduce the skewness of variables based on Tanza's (2014) value relevance model. Moreover, the study suggests that the market valuation of franking credit balances increases with firm size, which contradicts Heaney's (2009) findings.

The purpose of this paper is to understand the distributional impact of house price increases on consumption in the context of the energy transition.

This study draws from two micro cross-sectional datasets, the English Housing Survey (EHS) and the Living Costs and Food Survey (LCFS) to study the Marginal Propensity to Consume (MPC) out of changes in house prices. By employing pseudo-panel regressions, the paper examines the impact of house price changes on consumption among diverse household types.

This paper finds varying consumption responses to house price changes across age and tenure groups. Older homeowners tend to increase consumption when house prices rise. In contrast, middle-aged individuals, often renters or mortgage holders, reduce consumption in response to price increases. The youngest age group also experiences increased consumption but to a lesser degree than the oldest group. Energy-efficient homes are related to lower consumption across all tenure levels. However, when interacted with house prices and age, the estimates are positive, pointing to an unequal accrual of property premiums depending on housing market positions.

The main limitations stem from data constraints. First, using a pseudo-panel approach hinders control for unobservable selection bias. Additionally, while robust under cross-validation and specifications tests, the energy efficiency variable imputation results in a low number of energy-efficient homes. Due to heterogeneous responses to rising house prices, this paper contends that an energy transition model that subsidises homeowners’ renovation is likely to produce a negative impact on consumption among younger and middle-aged households.

This paper contributes to the MPC literature by incorporating energy efficiency as a key variable. It draws from recent data to obtain new estimates. By highlighting shifts in consumption patterns the paper contributes to a well-established body of literature with renewed policy relevance regarding housing retrofit.

This study developed new measures of the routine and non-routine task contents of managerial, professional, technical, and clerical occupations from a workload perspective. Here, we present a comparative analysis of the workload structures of state and industrial sector employees.

Our method involves detailed descriptions of work processes and an element-wise time study. We collected and analysed data to obtain a workload structure that falls within three conceptual task categories: (i) non-routine analytic tasks, (ii) non-routine interactive tasks and (iii) routine cognitive tasks. A total of 2,312 state and industrial sector employees in Kazakhstan participated in the study. The data were collected using a proprietary web application that resembles a timesheet.

The study results are consistent with the general trend reported by previous studies: the higher the job level, the lower the occupation’s routine task content. In addition, the routine cognitive task contents of managerial, professional, technical, and clerical occupations in the industrial sector are higher than those in local governments. The work of women is also more routinary than that of men. Finally, vthe routine cognitive task contents of occupations in administrative units are higher than those of occupations in substantive units.

Our study sought to address the challenges of using the task-based approach associated with measuring tasks by introducing a new measurement framework. The main advantage of our task measures is a direct approach to assessing workloads consisting of routine tasks, which allows for an accurate estimation of potential staff reductions due to the automation of work processes.

We investigate the role of fiscal policy, through several measures of government revenues and expenditures and redistribution, on disposable and market income inequality and economic growth as well as the interaction between inequality and growth for 31 European countries from 1995 to 2019.

We use a simultaneous equations model to assess the linkage between economic growth, inequalities and fiscal policy variables.

(1) While disposable income inequality has a negative effect on all fiscal policy variables, market income inequality has a mixed effects; (2) for Eastern European countries, public consumption and direct taxation positively influence economic growth; conversely, for Western European countries, the effects are negative; (3) disposable and market income inequality have a positive effect on growth for Eastern European countries, and a negative influence on growth for Western European countries; (4) growth contributes to the increase of disposable and market income inequality for Eastern European countries; for Western European countries, the effects are opposite; and (5) fiscal policy allows for the attenuation of disposable income inequality.

The different results between the role of market and disposable income inequality levels lead us to suggest tax progressivity as an important feature to consider when analyse the trivariate relationship between inequalities, fiscal policy and growth. Furthermore, there are different dynamics between inequality and growth, and the role of fiscal policy, on both Eastern and Western European countries.

The current study sought to better understand the factors that contribute to whether officers value procedurally just interaction techniques and contribute to the limited research examining how the effects of warrior and guardian mentalities may vary based on individual officer characteristics.

Survey data collected from patrol officers in two geographically different and ethnically diverse United States police departments allowed for an examination of the generality of warrior and guardian orientations on perceptions of procedural justice across gender, race and/or ethnicity, military service, education, and experience.

There was a largely invariant effect of the mentalities on officer attitudes toward procedural justice, except for officers of color. In this sample, the guardian effect on prioritizing procedural justice was stronger for officers of color than for White officers.

This study sheds light on our theoretical understanding of the warrior/guardian framework and offers practical implications for police leaders and policymakers in their effort to improve police-community relations.

Learning organization (LO) concept has received much attention in the last decades. The purpose of an LO is to proactively shape its future by fostering a culture of continuous learning among its members. This approach empowers the organization to adapt, evolve and innovate, aligning with the needs and aspirations of both internal and external stakeholders. As a result, the assessment of an organization’s LO level, whether strong or weak, becomes a matter of significance. This study aims to use Senge’s LO Questionnaire (SLOQ) in conjunction with latent profile analysis (LPA) to identify latent classes within the organization. This approach seeks to enhance the organization‘s learning potential and facilitate its evolution.

This research was a cross-sectional study conducted at Hamadan University of Medical Sciences, Iran. The statistical population consisted of 451 managers and employees across seven departments based at the university headquarters. The sample included 295 individuals, and data was collected using the SLOQ.

The LPA has shown an excellent accuracy (97%) in identifying SLOQ cut-off points (three classes of good, moderate and low). Most of the participants are considered as a moderate LO (55.9%), which implies the possibility of improving learning, especially enhancing “common vision” and “systemic thinking.” Also, individuals belonging to the high-class category were more likely to be male and hold associate degree than individuals in other categories.

This study depends on self-report, and different perceptions of questions can cause percept bias. Another limitation is about the nature of this research, which is cross-sectional, which may bring back causality among variables. The other is only three demographic variables have been considered, and it is necessary to pay attention to other influential variables in future studies.

The originality of this study lies in its use of the SLOQ in combination with LPA to identify latent classes in an organization to improve learning potentials.

Prior studies mostly tested the association between carbon emissions and firm value in certain contexts. This study aims to advance the existing literature by concentrating on three indicators of greening in corporations namely resource use, emissions and eco-innovation, and examining their value relevance in the stock market at the global level. Furthermore, we deepen the investigation by exploring the moderating role of eco-innovation and the CSR committee between greening in corporations and market value.

The data for the study were retrieved from the Thomson Reuters Eikon database for the years between 2002 and 2019 and contain 17,961 firm-year observations which are analyzed through fixed-effects regression.

The results reveal that while resource usage is viewed as value-relevant by the market, the emissions and eco-innovation are not. However, despite eco-innovation per se not being value-relevant, its interaction with resource usage and emissions is value-relevant. Furthermore, CSR committees undertake a very critical role in translating greening practices into market value.

While the results for emissions support the cost-concerned school, the findings for resource usage confirm the value creation school. Furthermore, the interaction effect of eco-innovation and CSR committee confirms the resource-based theory and stakeholder theory, respectively.

Investors regard eco-innovation-induced pro-environmental behaviors as value-relevant. These results propose firms replace eco-innovation at the focal point in developing environmental strategies and connecting other greening efforts to it. Moreover, CSR committees are critical to corporations in translating greening practices into firm value by developing and implementing disclosure and communication strategies.

The study’s originality stems from investigating the synergetic effect that eco-innovation and CSR committees generate in translating greening practices to greater market value at a global scale.

This paper aims to explore the risk and benefit perceptions influencing beer patrons’ purchase and consumption decisions in India. It delves into the drivers behind consumers’ consumption patterns and attitudes towards beer, which subsequently impact their behavioural intentions, including word-of-mouth recommendations and repurchase intentions.

The study uses a structured questionnaire to collect data from 306 beer patrons in Delhi and the National Capital Region. Through exploratory factor analysis and structural equation modelling, the research examines the risk (5) and benefit (3) factors associated with beer consumption using a factor model consisting of 29 constructs. A conceptual framework illustrates the interrelationships between variables, which are subsequently validated empirically.

The findings reveal that patrons’ attitudes towards beer are influenced by their perceptions of risk and benefit. Specifically, health and socio-psychological risks are identified as the most significant and negatively impacting factors on patrons’ attitudes. Factorial analysis demonstrates that patrons’ attitudes towards beer are positively influenced by their perception of benefits such as value for money, sensory appeal and convenience. Furthermore, the study highlights that an increase in benefit perception or a decrease in risk perception leads to a favourable shift in patrons’ attitudes towards beer.

To the best of the authors’ knowledge, this study will be the first to investigate how beer consumers’ consumption patterns and purchase decisions are impacted by assessing consumer risk and benefit perceptions. This study will also aid stakeholders in tailoring their beer offerings better to meet the desires and requirements of their customers.

Our objective was to evaluate the cost-utility of a health and social care integration programme for frail older adults in Buenos Aires, Argentina.

Based on a study of the programme’s effectiveness, a Markov model was conducted to assess its cost-utility. The active intervention was the health and social care integration programme, and the control was the best standard of care so far. The setting was the patients' home of residence. A third-party payer perspective and a lifelong time horizon were adopted. All transition probabilities, quality-adjusted life years (QALYs) and costs were estimated from the effectiveness study. A discount rate of 3.5% was applied to costs and benefits. Costs are expressed in international dollars (Int$), calculated according to the International Monetary Fund’s purchasing power parity rate. Different sensitivity analyses were performed. The model was built in Excel 365. Construct validity, verification during model construction and internal consistency of the results were assessed.

The programme had an average cost of Int$18,768.22/QALY, and the control Int$42,609.68/QALY. In the incremental analysis, the programme saved Int$26,436.10 and gained 0.81 QALYs over the control. In the sensitivity analyses, in 99.96% of cases, the programme was less costly and more effective.

The cost savings can facilitate the scalability.

The health and social care integration programme for frail older adults was more effective and less costly than the best standard of care to date. This study contributes to the scarce evidence on the efficiency of integrated care strategies for frail older persons.

This study aims to describe and empirically explore a new method for bank anti-money laundering (AML) systems using machine learning models. Current automated money laundering detection systems are notorious for flagging many false positives, causing bank employees to spend unnecessary time manually checking transactions that do not constitute money laundering. Decreasing the number of false positives can free up resources for investigating money laundering.

This study uses unique bank data on small- and medium-sized enterprises (SMEs) to examine how various client risk classification models can predict future suspicious transactions. This study explores various sources of client risk data and machine-learning approaches.

Client risk classification models can accurately predict suspicious future transactions. Adding accounting data and credit score information to client risk classification dramatically improves accuracy. This makes it easier to balance the risk of missing suspicious transactions with the need to reduce the number of false positives.

The suggested approach with readily available data sources and a focus on classifying client risk in a dynamic model can help banks significantly improve their efficiency by targeting their AML efforts toward the riskiest clients.

To the best of the authors’ knowledge, this study is the first to empirically explore machine learning in client risk classification, document how machine learning in client risk classification can significantly reduce false positives by incorporating novel, but readily available sources, such as credit risk and accounting data.