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Parents or primary caregivers of children with Autism Spectrum Disorder (ASD) have important role in early recognition of the disorder as well as in the management of the disorder. Knowledge, attitude and practice of primary caregivers towards children with ASD are important factors in promoting health and wellness of such children. The purpose of this paper is to evaluate the effectiveness of video-assisted teaching regarding care of children with ASD on knowledge, attitude and practice among primary caregivers.

Quasi-experimental research design with one group pre-test post-test was adopted. A total of 60 primary caregivers were selected through stratified random sampling technique. Video-assisted teaching was given to 60 primary caregivers. Data collection was done before and after the video-assisted teaching programme using structured questionnaire which consists of 57 questions.

The result of the study showed that the post-test level of knowledge attitude and practice among primary caregivers of children with ASD was significantly high (p<0.0001) when compared to pre-test level by using Wilcoxon Signed Rank Test. The study finding revealed that video-assisted teaching was effective in enhancing the knowledge, developing a positive attitude and good practice among primary caregivers regarding care of children with ASD.

Findings of this study will help mental health nurses, psychologists, intellectual disability nurses, teachers, public health, social workers, etc. to know the importance of video-assisted teaching programme regarding care of children with ASD and to motivate the primary caregivers to participate in such teaching programme. The community mental health nurse can plan video-assisted teaching programme in a community regarding care of children with ASD.

Results of this study indicate that the video-assisted teaching is effective and helps the primary caregivers to enhance the knowledge, attitude and practice regarding care of children with ASD. So continuous awareness in primary health centre and community area is necessary to improve the knowledge, attitude and practice of primary caregivers.

This chapter develops a framework for classifying approaches to conceptualizing and measuring implementation of innovations. It first develops a typology that distinguishes rollout, modification, programmatic and transformation conceptualizations of implementation. The implications of each conceptualization for measurement of implementation are discussed. Following this a classification scheme for implementation measures is presented that distinguishes measures on the basis of their: (a) criterion for success of the implementation; (b) innovation unit; (c) source of data; (d) measurement scale; and (e) level of analysis. Issues related to various measurement choices are discussed along with recommendations for future research and development in the measurement of implementation.

The purpose of this paper is to describe the further development of the previously introduced switch of brand (SB) model (presented in 2012) which helps to approximate variety-seeking behaviour (VSB) out of household panel data.

Based on existing theoretical variety-seeking models analysing household panel data, the further expansion of the variety-seeking model “Switch of Brands” (SB) is presented. In the last contribution in the British Food Journal the authors presented this simple but powerful tool to approximate VSB. The further developed model “Switch of Brands – Promotions” (SB _PR ) integrates relevant variables into one theoretical variety-seeking model (number of purchased brands, number of purchases, price promotions, etc.). In particular, price promotions were integrated into the SB model in order to deliver even more realistic approximations of households’ VSB.

The explanatory power of the model in view of brand loyalty is tested. The empirical analysis is conducted with scanner household panel data from Austria in three different product categories.

The data analysis shows that the model has an excellent explanatory power concerning brand loyalty, however, not better than the original SB model.

The SB _PR model allows interpretations for marketing purposes and brand management including marketing variables (here: price promotions). The model may be applied in any business field where panel data are available.

The model delivers a consistent theoretical framework for approximating VSB by means of purchase histories.

The purpose of this paper is twofold: first, to clarify what specific behaviors are involved in consumers' partial switching in mobile application (app) usage, and, second, to explore the common and differential motivations of these behaviors.

This paper specified two behaviors in consumers' partial switching in mobile app usage, trialing and combining behaviors, and conceptualized them as different types of variety seeking behaviors. A theoretical model contrasting intrinsic motivations and extrinsic motivations on the two behaviors was developed and tested with a sample of 561 mobile app users in China.

The findings showed that both trialing and combining behaviors could be motivated by intrinsic individual-related and extrinsic technology-related factors. Besides, intrinsic individual-related factors were more effective in motivating trialing behavior, whereas extrinsic technology-related factors were more effective in motivating combining behavior. All these findings are applicable and consistent in both hedonic and utilitarian apps.

This study extends and advances the literature on information technology switching by investigating consumer use behaviors from a new perspective of partial switching and multiple competing apps usage. This study also contributes to variety seeking literature by extending the understanding of variety seeking to the context of mobile app usage. Finally, by investigating the associations and distinctions of trialing and combining behavior, this study not only helps to fully understand the partial switching but also enriches the understanding of different types of variety seeking behaviors.

The study aimed to explore the differential impact of various types of sales promotion on consumers' variety-seeking behaviour and the roles of utilitarian and hedonic shopping motivations in promotion-induced variety-seeking. The study further assessed the moderation impact of risk-taking tendencies and deal proneness in the promotion-induced variety-seeking buying episodes.

Based on the temporality of gratification of promotional rewards (immediate/delayed) and the type of promotional rewards (monetary/non-monetary), we classified consumer sales promotions into four types (MI: Monetary/Immediate; NMI: Non-monetary/Immediate; MD: Monetary/Delayed and NMD: Non-monetary/Delayed). We conducted survey research across four major metro cities in India. We collected data from the buyers of two supermarket chains in four major metro cities of India and analyzed the data using SEM techniques.

The study’s findings revealed that only MI and NMI sales promotions lead to variety-seeking buying, whereas MD and NMD do not influence variety-seeking. The study further revealed that MI, NMI and NMD influence hedonic shopping motivations and play a role in variety-seeking buying episodes. NMD does not influence utilitarian shopping motivation or play a role in inducing variety-seeking buying behaviour.

The study is one of the very few studies that explored the differential impact of various types of sales promotions on variety-seeking buying behaviour. The study’s findings enable the retailer to devise promotional strategies to induce variety-seeking among the shoppers. Further, the findings of the instrumentality of CSP in inducing HSM may help the retailer create a promotional environment and induce the shopper (in a good mood) to buy more, thus improving store performance.

Post-Sarbanes Oxley Act (SOX), the audit committee has been empowered greatly to play a central role in the corporate governance of firms. Embedded in agency theory, this study aims to examine the effect of the audit committee on the likelihood by firms to pay dividends.

The study population is US firms in the Institutional Shareholder Services (ISS) database from 2007 to 2018. The authors apply the multivariate logit fixed-effect regression for the analyses after conducting the appropriate statistical tests.

From the results of the research model, the authors find that there is a positive relationship between the size and gender diversity of the audit committee and the propensity to pay dividends suggesting that a larger audit committee with substantial women representation improve the information environment in firms leading to higher dividend distribution. The extent of busyness of the audit committee impacts negatively on the propensity to pay dividends. The results are driven by high-performing firms and not driven by specific levels of firm size.

The findings of the study give impetus to the audit committee as an important component of the corporate governance mechanism that advances the interest of stakeholders. Thus, efforts that seeks to promote the audit committee’s resourcefulness must be embraced by all stakeholders.

To the best of the authors’ knowledge, this study is the first to focus on audit committee and dividend payout policy of US firms post-SOX. The study demonstrates how the audit committee characteristics including its size, gender diversity and busyness affect dividend policy by mitigating information asymmetry problems.

The present study is aimed at examining the antecedents of online shopping user behavior and customer satisfaction. More specifically, (1) the effect of social influence, variety seeking behavior, advertising and convenience on user behavior, and (2) the effect of user behavior on customer satisfaction are examined.

A conceptual model is developed and tested after verifying the psychometric properties of the survey instrument. Data were collected from 556 respondents from three major cities (Hyderabad, Chennai and Bangalore) in the southern part of India using structured instrument. Hierarchical regression is performed. Measurement model was checked using structural equation modeling (Lisrel package).

The results reveal that (1) social influence, (2) variety seeking, (3) advertising, (4) convenience, (5) trust and (6) product factors were positively related to online user behavior. Results also show that user behavior is significantly and positively related to customer satisfaction. The hierarchical regression results also showed moderating effects of (1) trust in the relationship between social influence, variety seeking and user behavior, and (2) product factors in the relationship between advertising, convenience and user behavior. Finally, results suggest that user behavior is partially mediating the relationship between trust and customer satisfaction, i.e. trust has both direct and indirect effect on customer satisfaction.

As with any survey-based research, the present study suffers from the problems associated with self-report measures viz., common method bias and social desirability bias. However, the authors attempted to minimize these limitations by following appropriate statistical techniques.

This study contributes to both practicing managers and the literature on advertising. The study suggests that trust and product play a major role in strengthening the relationship between antecedents and user behavior.

This study provides new insights about the importance of gaining trust in influencing consumer behavior. The conceptual model the authors developed is novel in the sense not many studies are available in India to empirically examine the moderating relationships of trust and product in consumer behavior.

The purpose of the paper is to investigate the extent to which a model based on financial and non-financial variables predicts auditors' decisions to issue qualified audit reports in the case of companies listed on the Tehran Stock Exchange (TSE).

The authors utilized data from the financial statements of 96 Iranian firms as the sample over a period of five years (2012–2016). A total of 480 observations were analysed using a probit model through 11 primary financial ratios accompanying non-financial variables, including the type of audit firm, auditor turnover and corporate performance, which affect the issuance of audit reports.

The results demonstrated high explanatory power of financial ratios and type of audit firm (the national audit organization vs other local audit firms) in explaining qualifications through audit reports. The predictive accuracy of the estimated model is evaluated using a regression model for the probabilities of qualified and clean opinions. The model is reliable, with 72.9% accuracy in classifying the total sample correctly to explain changes in the auditor's opinion.

This study contains some limitations. First, it is likely that similar researches in developed countries set a large sample (e.g. over 1,000 firms) including more years, but the authors cannot follow such a trend due to data access restrictions. Second, banks and financial institutions, investment and holding firms are removed from the sample, because their financial structure is diverse. The third limitation of the study represents the different economic and cultural conditions of Iran compared to other countries. Future studies could focus on internal control material weaknesses or earnings management to predict audit opinion in emerging economies including Iran.

The paper has practical implications and can assist auditors in identifying factors motivating audit report qualifications, mainly in emerging economies.

The paper contributes to auditing research, since very little is known about the determinants of audit opinion in emerging markets including Iran; it also constitutes an addition to previous knowledge about audit opinion in the context of TSE. The paper is one of the rare studies predicting auditor opinions using both financial variables and non-financial metrics.

The increase in autism prevalence and presentation in the media suggests a rise in public awareness. This paper aims to explore what factors (contact, knowledge and ethnicity) may be associated to positive attitudes towards individuals with autism spectrum disorder (ASD).

An online survey examined contact with and knowledge of ASD among Black, Asian and White ethnic groups to predict public attitudes to people with ASD.

In multiple regression models, the results suggested that the level of contact predicts positive attitudes towards autism when demographic factors were accounted. The level of knowledge about autism were significantly associated to attitudes, but not consistently when demographic factors were accounted. However, differences in knowledge and attitudes to people with ASD were identified amongst Black, Asian and White ethnic groups.

These findings have implications for policy and public health and education campaigns, including ensuring contact and knowledge of autism among the public.

These findings have implications for policy and public health and education campaigns, including ensuring contact and knowledge of autism among the public. Additionally, further effort is required to target public knowledge and attitudes to autism, particularly among ethnic groups. Institutional support tailored to encourage structured and unstructured contact across public domains such as education, health, social and care practices could effectively reduce prejudice between the public and people with ASD over time.

Coronary artery disease is one of the most common cardiovascular disorders in the world, and it can be deadly. Traditional diagnostic approaches are based on angiography, which is an interventional procedure having side effects such as contrast nephropathy or radio exposure as well as significant expenses. The purpose of this paper is to propose a novel artificial intelligence (AI) approach for the diagnosis of coronary artery disease as an effective alternative to traditional diagnostic methods.

In this study, a novel ensemble AI approach based on optimization and classification is proposed. The proposed ensemble structure consists of three stages: feature selection, classification and combining. In the first stage, important features for each classification method are identified using the binary particle swarm optimization algorithm (BPSO). In the second stage, individual classification methods are used. In the final stage, the prediction results obtained from the individual methods are combined in an optimized way using the particle swarm optimization (PSO) algorithm to achieve better predictions.

The proposed method has been tested using an up-to-date real dataset collected at Basaksehir Çam and Sakura City Hospital. The data of disease prediction are unbalanced. Hence, the proposed ensemble approach improves majorly the F-measure and ROC area which are more prominent measures in case of unbalanced classification. The comparison shows that the proposed approach improves the F-measure and ROC area results of the individual classification methods around 14.5% in average and diagnoses with an accuracy rate of 96%.

This study presents a low-cost and low-risk AI-based approach for diagnosing heart disease compared to traditional diagnostic methods. Most of the existing research studies focus on base classification methods. In this study, we mainly investigate an effective ensemble method that uses optimization approaches for feature selection and combining stages for the medical diagnostic domain. Furthermore, the approaches in the literature are commonly tested on open-access dataset in heart disease diagnoses, whereas we apply our approach on a real and up-to-date dataset.