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The purpose of this paper is to investigate the trend of life expectancy in Bangladesh and find the effect of eliminating the causes of diseases on life expectancy statistics.

Data consisted of 1,530 deaths in 2000, 1,582 deaths in 2004 and 1,514 deaths in 2008 that were collected from the Health and Demographic Surveillance System of International Centre for Diarrheal Disease Research, Bangladesh. Trends in life expectancy after eliminating the cause of diseases were examined by a Single Decrement Life table.

The expectation of life for both male and female presented differing patterns. Results showed that life expectancies were greatly reduced in the presence of all groups of non-communicable diseases (NCDs) in the community, whilst life expectancies were significantly improved if all NCDs within all disease groups were completely eliminated. The life expectancies in the presence of NCDs showed lowest expected years among all the present diseases groups and the life expectancies eliminating NCDs showed highest expected years among all the eliminating diseases groups. The results indicated that 10.99 years of life would be added to life expectancy at birth for the male population and 8.82 years for the female population in 2008 if NCDs were eliminated.

The findings of this study provide useful information which could contribute to a more effective allocation of targeted funding for developing public health programs. Lowering mortality by eliminating major groups of diseases results in higher life expectancy ratings. Specifically, the relative impacts of eliminating cardiovascular diseases and respiratory diseases, as compared with eliminating neoplasms.

The purpose of this paper is to test the discriminative validity of the Substance Use Beliefs Questionnaire (SUBQ) with alcohol dependent users, by assessing if the new tool can successfully differentiate between two extreme groups.

The criterion used to select the two extreme groups was participation or not in treatment for alcohol dependence. Score of the Severity of Alcohol Dependence Questionnaire (SADQ) was used as a secondary confirmation criterion of extreme difference.

In all, 98 staff and 94 people in treatment for alcohol dependence were recruited. The treatment group scored 30.83 higher than the control on SADQ, 10.76 on positive and 28.98 on negative expectancies. Negative expectancies score had correctly classified 88.5 per cent and positive expectancies score only 66 per cent of the original grouped cases. The area under the Receiver Operating Characteristics (ROC) curve for negative expectancies was 0.94 (very good) with a cut-off point of 43.5 with 89 per cent sensitivity and 92 per cent specificity. The area under the ROC curve for positive expectancies was 0.73 (fair). Due to the shape it was difficult to identify a cut-off point.

The results support the discriminative validity of the negative expectancies sub scale of the SUBQ between two extreme groups. With only the use of negative expectancies score participants could be classified correctly to those of the control and those of the treatment group.

SUBQ is the first tool to measure outcome expectancies across substances, facilitating relevant research with poly substance users. Future research needs to explore the discriminative validity of the tool with the other three substance groups (smokers, stimulant and opioids users), involved in the development and validation of the SUBQ.

The objective of this study is to reevaluate the correlation among pharmaceutical consumption, per capita income, and life expectancy across different age groups (at birth, middle age, and advanced age) within the OECD countries between 1998 and 2018.

We employ a two-step methodology, utilizing two independent approaches. Firstly, we con-duct the Dumitrescu-Hurlin pairwise panel causality test, followed by Machine Learning (ML) experiments employing the Causal Direction from Dependency (D2C) Prediction algorithm and a DeepNet process, thought to deliver robust inferences with respect to the nature, sign, direction, and significance of the causal relationships revealed in the econometric procedure.

Our findings reveal a two-way positive bidirectional causal relationship between GDP and total pharmaceutical sales per capita. This contradicts the conventional notion that health expenditures decrease with economic development due to general health improvements. Furthermore, we observe that GDP per capita positively correlates with life expectancy at birth, 40, and 60, consistently generating positive and statistically significant predictive values. Nonetheless, the value generated by the input life expectancy at 60 on the target income per capita is negative (−61.89%), shedding light on the asymmetric and nonlinear nature of this nexus. Finally, pharmaceutical sales per capita improve life expectancy at birth, 40, and 60, with higher magnitudes compared to those generated by the income input.

These results offer valuable insights into the intricate dynamics between economic development, pharmaceutical consumption, and life expectancy, providing important implications for health policy formulation.

Very few studies shed light on the nature and the direction of the causal relationships that operate among these indicators. Exiting from the standard procedures of cross-country regressions and panel estimations, the present manuscript strives to promote the relevance of using causality tests and Machine Learning (ML) methods on this topic. Therefore, this paper seeks to contribute to the literature in three important ways. First, this is the first study analyzing the long-run interactions among pharmaceutical consumption, per capita income, and life expectancy for the Organization for Economic Co-operation and Development (OECD) area. Second, this research contrasts with previous ones as it employs a complete causality testing framework able to depict causality flows among multiple variables (Dumitrescu-Hurlin causality tests). Third, this study displays a last competitive edge as the panel data procedures are complemented with an advanced data testing method derived from AI. Indeed, using an ML experiment (i.e. Causal Direction from Dependency, D2C and algorithm) it is believed to deliver robust inferences regarding the nature and the direction of the causality. All in all, the present paper is believed to represent a fruitful methodological research orientation. Coupled with accurate data, this seeks to complement the literature with novel evidence and inclusive knowledge on this topic. Finally, to bring accurate results, data cover the most recent and available period for 22 OECD countries: from 1998 to 2018.

This study aims to evaluate the users’ behavioral intention toward the acceptance and adoption of digital payment FinTech services in India. The study also compares the differences in Gen Y and Gen Z’s intention to adopt digital payment FinTech services.

The present study adopted both the unified theory of acceptance and use of technology (UTAUT) and the technology acceptance model (TAM) as its theoretical base and also added financial literacy and customer satisfaction. The data was analyzed by applying structural equation modeling using SmartPLS 4.

The outcomes of the study imply that customer satisfaction, effort expectancy and performance expectancy had a significant effect on behavioral intention. Moreover, effort expectancy, performance expectancy and perceived enjoyment had a significant influence on customer satisfaction, and effort expectancy and performance expectancy is significantly influenced by perceived enjoyment, while self-efficacy significantly influenced perceived enjoyment. Also, financial literacy does not moderate the relationship between effort expectancy, performance expectancy, facilitating condition and behavioral intention. Furthermore, the association of effort expectancy → customer satisfaction; perceived enjoyment → customer satisfaction; and perceived enjoyment → effort expectancy is moderated by age factor.

This study contributes by developing a more cohesive and unified model for assessing users’ behavioral intention toward acceptance and adoption of FinTech services by adopting constructs from the UTUAT and TAM and incorporating financial literacy and customer satisfaction to expand and enhance the theoretical prospect of the existing literature.

Few empirical studies examined the relationship between life expectancy and income in India. This study aims to examine the impact of life expectancy on economic growth in India by incorporating all the major states of India.

This study is based on secondary data and includes 16 major states of India covering the periods 2000–2014. The author used panel fixed effect model (FEM) to examine the impact of life expectancy on economic growth.

Empirical analysis revealed a positive trend in life expectancy in India. In association with life expectancy, the author found continuous growth in the elderly population. The result of the FEM shows that gains in life expectancy positively affect economic growth in India. The empirical findings do not support any negative impact of life expectancy gains on economic growth.

This study is the outcome of the independent and original research work of the authors and contributes significantly to the literature on the demography–economic relationship. The findings of this study help the author to understand that life expectancy gain is in no way a constraint, rather the skill and experience of the workforce are crucial to determining economic growth.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-06-2022-0422.

While science has researched the impact of air pollution on human health, the economic dimension of it has been less researched so far. Renewable energy consumption is an important factor in determining the level of life expectancy and reducing health expenditure. Thus, this study aims to investigate the impact of renewable energy, carbon emissions, health expenditure and urbanization on life expectancy in G-7 countries over the period of 2000–2019.

This study has adopted a novel Method of Moments Quantile Regression (MMQR). Furthermore, as a robustness check for MMQR, the fully modified ordinary least square, dynamic ordinary least squares and fixed effect ordinary least square estimators have been used.

The results indicated that renewable energy consumption, health expenditure and urbanization lead to an increase in life expectancy across all quantiles (5th to 95th), whereas higher carbon dioxide emissions reduce life expectancy at birth across all the quantiles (5th to 95th).

The empirical findings conclude that governments should recognize their potential in renewable energy sources and devise policies such as tax-related regulations, or relevant incentives to encourage further investments in this field.

This paper in comparison to the other research studies used MMQR to investigate the impact of factors affecting life expectancy. Also, to the best of the authors’ knowledge, so far no study has investigated the impact of renewable energy on life expectancy in G-7 countries.

The purpose of this paper is to investigate the effect of flood occurrence on mortality rate and life expectancy amongst 53 African countries.

The study utilizes panel data from the period 2000–2018 on 53 African countries and system generalized method of moments (system GMM) for the analysis.

The result indicates that flood occurrence causes the destruction of health facilities and the spread of diseases which reduces life expectancy. In addition, flood occurrence increases mortality rate amongst 53 African countries.

The study recommends that governments amongst African countries should implement strategies being enshrined in Conference of Parties (COP, 2021) on climate change. This will help to reduce the level of climate change and flood occurrence.

Previous studies focussed on the adverse effect of flood occurrence without considering the issue of life expectancy amongst African countries. This study contributes to existing empirical studies by examining the effect of flood occurrence on mortality rate and life expectancy amongst African countries.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-07-2022-0508.

With the trend of adopting and studying artificial intelligence (AI) service robots at restaurants, the authors’ understanding of how customers perceive robots differently across restaurant segments remains limited. Therefore, building upon expectancy theory, this study aims to propose a trust-based mechanism to explain customers’ support for AI-based service robots.

For cross-segment validation, data were collected from online survey participants under the scenarios of experiencing AI service robots in luxury (n = 428), fine-dining (n = 420), casual (n = 409) and quick-service (n = 410) restaurant scenarios.

In all four segments, trust in technology increased willingness to accept AI service robots, which was then positively related to customers’ support for AI-based service robots. Meanwhile, customers’ AI performance expectancy mediated the relationship between trust in technology and willingness to accept AI service robots. On the other hand, at luxury, fine-dining and casual restaurants, males perceived a stronger positive relationship between trust in technology and AI performance expectancy. No generational differences were found in the four restaurant segments between trust in technology and AI performance expectancy.

To the best of the authors’ knowledge, this study is one of the first attempts in hospitality research to examine cross-segment validation of customers’ responses to AI-based service robots in the luxury, fine-dining, casual and quick-service restaurant segments.

What is the relationship between longevity and health? Health expectancies were developed more than 30 years ago specifically to answer this question. It may therefore be the time to try to answer this question, though it is worth noting that the question implies a unidirectional relationship. Almost no one questions the positive association between health and longevity. It is expected that healthy, robust people will live, on average, longer than frail people. This heterogeneity in terms of robustness/frailty may explain the shape of the mortality trajectory with age, ie. the oldest old seem to follow a lower mortality schedule (Vaupel et al, 1979). On the other hand, many people wonder about the relationship between longevity and health. Are we living longer because we are in better health? Are we living longer in good health? Or are we merely surviving longer whatever our health status? In other words, can we live in good health as long as we can survive? And this is exactly the purpose of health expectancies: monitoring how long people live in various health statuses (Sanders, 1964; Sullivan, 1971; Robine et al, 2003a).

The purpose of this study is to test two common assumptions underlying the success of total quality management (TQM): that change occurs as training imparts new knowledge needed by employees to effectively participate in the intervention, and that employees then receive the organizational support needed to translate this knowledge into new job behaviors and organizational practices that define the intervention.

A survey was completed by 216 employees of a large US company 15 months after the implementation of a Fsix sigma quality improvement program. Respondents were asked to indicate the extent to which they had the skills and resources needed for successful participation in six sigma (success expectancies).

Results showed that employees as a group had low expectancies for both skills and resources, but that expectancies were significantly higher for program participants than for non‐participants.

The primary implication of this finding for change agents is the need to examine and understand employee expectancies for success and how they are affected by training and ongoing participation. Monitoring employee expectancy beliefs regarding the confidence, knowledge, and resources needed for successful participation allows training, facilitation, and oversight to be targeted in terms of who needs what and when.

This study tests two common assumptions underlying the success of TQM.