# Quality & Quantity

Online ISSN: 1573-7845
Print ISSN: 0033-5177
Recent publications
• Mehak Goyal
• Pankaj Deshwal
Design/methodology/approach This study applies qualitative data analysis approach. 40 In-depth interviews were taken using non-probability sampling technique. then the recorded audios were transcript into MS Word file. the collected data was analysed using NVivo software by QSR International. Purpose By presenting new insights into the field of marketing research using content analysis. To identify the factors affecting the post purchase behaviour of the customer. The same will also be helpful for the mangers to formulate strategies to better understand online customer experience and predict their future actions. Findings Text search query and hierarchal chart identified the various factors influencing online post purchase customer experience and also compared these factors in terms of frequency in the data set provided. Conceptual model (OPPCE) provided eight factors such as delivery, return and refund policy, customer support etc., these factors could be used by scholars and academicians for further research in this domain. Originality To the knowledge of the authors, it is the first study of its kind to attempt to perform content analysis of online post purchase customer experience concept The study aims to identify various unexplored factors influencing online post purchase customer experience. qualitative approach was applied in only countable studies in extant literature, this study fills this research gap.

The search for happiness, understood as an inner and personal attitude that goes beyond mere satisfaction, is one of the aims of tourists' co-creation of value. To date, few studies have analysed the importance of people's moral principles in the co-creation of tourist value. Moral emotions play an essential role in this process. In this study, 12 tourism managers within administration, 28 hotel managers and 24 travel agencies actively participated in defining the indicators selected to measure how the co-creation of value from five Span-ish towns affected customers' happiness. Moreover, 444 tourists participated in the study. The PLS-SEM technique was used to examine the data obtained. Results show that the co-creation of value contributes to the happiness of the tourist. Of particular significance is the influence of customers' co-creation of value on customer happiness. Additionally, the predictive capacity of the model is replicable to other tourist destinations.

In recent time, Turkey could be said to have experienced different levels of Economic Risk, Financial Risk, and Political Risk from low- to high-level. This study investigates the linkage between country risks, namely Financial Risk, Economic Risk, and Political Risk (FEP risk) in Turkey for the period 1984Q1 to 2019Q1 by using threshold cointegration, Markow-switching regression (given the nonlinearity and structural breaks observed in the time series variables), and frequency domain causality approaches. The empirical findings of this study reveal that (i) nonlinear cointegration between Economic Risk, Financial Risk, and Political Risk in Turkey is statistically significant given the evidence of threshold cointegration test, which determines the structural breaks endogenously; (ii) there is positive linkage among the component of country risk at different volatility periods; (iii) there is a significant Granger causal linkage between Economic Risk, Financial Risk and Political Risk at the different frequency levels. The study is likely to open debate about the literature since the study concludes with a discussion on short-run and long-run implications for economic, political, and financial stabilises, thus offering policy suggestions for the policymakers in Turkey.

Civil aviation sector is of great importance for all countries. Besides, demand estimation of air passengers is crucial for every part of air industry. It is used for marketing, determining policies, pricing, making decisions on investments and more. This paper presents an econometric model to estimate air travel demand for 10 major European region countries which have high number of airline passengers by using panel regression model. Countries were determined based on data availability of the variables. Authors tried to determine the effects of independent variables as “purchasing power parities”, “net direct investment”, “population”, “GDP per capita”, “consumer prices (transport)”, “exchange rates”, “commercial aircraft fleet”, “number of tourism enterprises” and “urban population rate” on demand. The model is estimated using panel data for 10 countries over a period from 2009 to 2018. The results also suggest that the countries’ GDP values effect the air passenger demand. Besides countries should make investments in more aircraft fleet size to promote their civil aviation sector which is of great importance for the European economy.

Differences in the response-scale formats constitute a major challenge for ex-post harmonisation of survey data. Linear stretching of original response options onto a common range of values remains a popular response to format differences. Unlike its more sophisticated alternative, simple stretching proves readily applicable without requiring assumptions regarding scale length or access to auxiliary information. The transformation only accounts for response scale length, ignoring all other aspects of measurement quality, which makes the equivalence of harmonised survey variables questionable. This paper focuses on the inherent limitations of linear stretching based on a case study focusing on the measurements of trust in the European Parliament by the Eurobarometer and the European Social Survey—8 timewise corresponding survey waves in 14 European countries (2004–2018). Our analysis demonstrates that the linear stretch approach to harmonising question items with different underlying response scale formats does not make the results of the two surveys equivalent. Despite harmonisation, response scale effects are retained in the distributions of output variables.

Highly cited papers are influenced by external factors that are not directly related to the document's intrinsic quality. In this study, 50 characteristics for measuring the performance of 68 highly cited papers, from the Journal of The American Medical Informatics Association indexed in Web of Science (WOS), from 2009 to 2019 were investigated. In the first step, a Pearson correlation analysis is performed to eliminate variables with zero or weak correlation with the target (“dependent”) variable (number of citations in WOS). Consequently, 32 variables are selected for the next step. By applying the Ridge technique, 13 features show a positive effect on the number of citations. Using three different algorithms, i.e., Ridge, Lasso, and Boruta, 6 factors appear to be the most relevant ones. The "Number of citations by international researchers", "Journal self-citations in citing documents”, and "Authors' self-citations in citing documents”, are recognized as the most important features by all three methods here used. The "First author's scientific age”, "Open-access paper”, and "Number of first author's citations in WOS" are identified as the important features of highly cited papers by only two methods, Ridge and Lasso. Notice that we use specific machine learning algorithms as feature selection methods (Ridge, Lasso, and Boruta) to identify the most important features of highly cited papers, tools that had not previously been used for this purpose. In conclusion, we re-emphasize the performance resulting from such algorithms. Moreover, we do not advise authors to seek to increase the citations of their articles by manipulating the identified performance features. Indeed, ethical rules regarding these characteristics must be strictly obeyed.

A variety of data is of geographic interest but is not available at a small area level from large-scale national sample surveys. Small area estimation can be used to estimate parameters of target variables to detailed geographical scales based on relationships between the target variables and relevant auxiliary information. Small area estimation of proportions is a topic of great interest in many fields of study, where binary variables are diffused, such as in labour force, business, and social exclusion surveys. The univariate generalised mixed model with logit link function is widely adopted in this context. The small area estimation literature has shown that multivariate small area estimators, where correlations among response variables are taken into account, provide more efficient estimates than the traditional univariate approaches. However, the estimation problem of multivariate proportions has not been studied yet. In this article, we propose a bivariate small area estimator of proportions based on a bivariate generalised mixed model with logit link function. A simulation study and an application are presented to evaluate the good properties of the bivari-ate estimator compared to its univariate setting. We found that the extent of the improved efficiency of the bivariate over the univariate approach is associated with the degree of correlation of the area-specific random effects and the intraclass correlation, whereas it is not strongly related to the area sample size.

This article examines the citation practices of the provincial administrative courts in Poland in a sample of judgments issued in the years 2009–2016. The analysis strives to assess the factors affecting the use of other court citations and the prestige of provincial courts manifested in the higher citations of their verdicts. The methods used involve logistic and zero-inflated negative binomial regressions on the set of factors relating to court circuit characteristics, the performance of courts, the features of cases and the efficiency of the administration in a given province. The results indicate that, out of sixteen courts, there is only one provincial administrative court with high prestige. The number of citations is higher for more populated circuits and decreases with the number of employed judges in a court. While small courts cite more they are also more frequently cited than larger ones.

The Open Science movement is gaining tremendous popularity and tries to initiate changes in science, for example the sharing and reuse of data. The new requirements that come with Open Science poses researchers with several challenges. While most of these challenges have already been addressed in several studies, little attention has been paid so far to the underlying Open Science practices (OSP). An exploratory study was conducted focusing on the OSP relating to sharing and using data. 13 researchers from the Weizenbaum Institute were interviewed. The Weizenbaum Institute is an interdisciplinary research institute in Germany that was founded in 2017. To reconstruct OSP a grounded theory methodology (Strauss in Qualitative Analysis for Social Scientists, Cambridge University Press, Cambridge, 1987) was used and classified OSP into open production, open distribution and open consumption (Smith in Openness as social praxis. First Monday, 2017). The research shows that apart from the disciplinary background and research environment, the methodological approach and the type of research data play a major role in the context of OSP. The interviewees’ self-attributions related to the types of data they work with: qualitative, quantitative, social media and source code. With regard to the methodological approach and type of data, it was uncovered that uncertainties and missing knowledge, data protection, competitive disadvantages, vulnerability and costs are the main reasons for the lack of openness. The analyses further revealed that knowledge and established data infrastructures as well as competitive advantages act as drivers for openness. Because of the link between research data and OSP, the authors of this paper argue that in order to promote OSP, the methodological approach and the type of research data must also be considered.

This study aims to explore public perceptions and emotions toward crisis response strategies when unethical business practices occur. United Airlines’ passenger-dragging scandal is examined through the lens of the situational crisis communication theory. United Airlines employed two apology strategies, partial and full, on their Facebook page. Public responses toward the two apology strategies are analyzed through opinion mining of public comments by employing mixed text mining techniques. A total of 51,147 public comments generated by 50,103 unique users were extracted with an API-based social network analysis tool, NodeXL. First, the structural characteristics of eWOM communication in response to the two apology strategies were comparatively investigated. Next, topic modeling was employed to detect salient topics, and the intensity of positive and negative emotions to the apology strategies were compared. Additionally, semantic network analysis was used to uncover public reactions to and brand attitude toward United as a consequence of the apology strategies. The public generated a larger comment network for the full apology strategy than the partial apology. The public discussed problems with United’s apology strategies and deemed that wrong public relations strategies were used. Findings from the sentiment analysis and semantic network analysis suggest that the matched response strategy (full apology) did not change public responses and emotions. Public perception on United’s crisis management was primarily negatively associated with its hypocritical apology and image-focused reaction. This study offers insights on crisis communication strategiesfor public relations practitioners . The findings highlight that timing matters even when implementing matched response strategies in the intentional crisis condition.

The need for an effective comprehensive financial performance score of the firm derived from accounting-based variables is increasingly felt in the stream of empirical research on relationships between financial performance and other dimensions of corporate performance. The solution to this problem must be pursued in literature on statistical-mathematical techniques to synthesize financial performance through financial ratios derived from financial statements. Until now, however, studies have mainly focused on mathematical modeling and ranking of companies, without using appropriate benchmarks to verify the relevance of the scores obtained and to establish, from a comparative perspective between different techniques, which one provides the assessment that best summarizes financial performance. To make a contribution to this research gap, using a sample of 845 companies observed from 2014 to 2020, we compared Data Envelopment Analysis and Principal Component Analysis with two other applications based on new methodologies derived from the Multi-Criteria Decision Methods: The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Grey Relational Analysis. We developed the 4 techniques on a set of 9 financial ratios expressive of profitability and operating efficiency, solvency, and liquidity, and Tobin's q was used as a benchmark to compare the results provided by the 4 techniques and identify the best performing one. We found that TOPSIS is the most effective methodology for synthesizing an effective accounting-based score of the firm’s financial performance.

The current research tries to contribute to the prospect theory by examining how personality factors affect behaviour biases. Moreover, the study tries to inspect how risk-tolerance behaviour moderates the relationship between personality traits and behavior biases. The research considered a cross-sectional research design to collect responses from 847 individual investors through a questionnaire. The study considered a convenience sampling technique. Further to examine the hypotheses, the study used SEM and PROCESS macro v3.0 for SPSS. The findings of the study suggest that conscientiousness and extroversion traits significantly influence behaviour biases. The findings also explain that neuroticism was associated with herding, disposition, and anchoring bias. The findings confirmed the moderating effect of risk-tolerance on the association between personality traits and behaviour biases. The findings contribute to the existing literature of behaviour finance by focusing on the prospect theory as well as some practical implications for investors and financial advisors. The study suggests to the individual investors with different traits how they can overcome these biases while investing. The study suggests that financial advisors should educate their clients and also establish a lock-gain point and stop-loss point to reduce the effect of such biases. The study also suggests that investment advisors should provide information more efficiently so that investors’ portfolios could be amassed into a well-diversified investment and tries to set up efficient approaches associated with investment quality and give swapping options as per their risk-tolerance behavior. The research contributes to behaviour finance literature by signifying the moderation effect of risk tolerance on the association amid personality factors and behavioural biases and how it reduces the influence of biases while taking investment decisions among Indian investors. To the best of our knowledge, this is the first comprehensive study that examines the moderation effect of risk-tolerance among the relationship between personality traits and behaviour biases. Furthermore, it demonstrates that an individual’s risk-tolerance enhances their involvement in the decision-making process, allowing them to make the best financial option possible.

Numerous companies are considering implementing virtual reality (VR) technology in both online and offline stores to attract more customers, as they realized in recent years that the shopping contexts built with VR help provide consumers with feeling of presence experience. To explore various factors affecting users’ shopping intention toward using VR, a research model based on the theory of reasoned action and the ABC (affective- behavioral- cognitive) model of attitude was developed. The model, which also encompassed spatial presence, media richness, flow, and VR context empathy, was tested in VR and conventional online shopping contexts. This study involved an online questionnaire used for empirical research, along with the collection and analysis of data of 240 samples by structural equation modeling approach. The findings indicated that attitude is the most important factor affecting the intention of using online shopping while empathy is the most critical factor affecting offline shopping intention. In addition, flow has strong influence on both attitude and empathy. This study also uncovered that spatial presence and media richness have positive influence on users’ flow experience. The implications of these findings are discussed.

Limited diversity is a term employed in the context of Ragin’s Qualitative Comparative Analysis (QCA), but it describes a phenomenon which is widespread in social contexts: cases are usually not distributed evenly across all the possible combinations of factors linked to some outcome. Instead, they are often clustered together. To deal with limited diversity, the three solution types parsimonious, intermediate, and complex (or conservative) solution have been proposed. There is an ongoing debate as to the merits of each solution type. This paper contributes to the debate by focussing on what the implications of choosing each solution type are. In making this choice, researchers have to make certain assumptions, and the paper discusses what these are and how they vary depending on which solution type is being implemented, drawing on invented examples and examples from published work to bring out the consequences of these assumptions. It concludes that it is not obvious that any one solution type is superior, certainly not to the degree that the others always have to be ruled out. They rely on different kinds of assumptions and models of causation. Thus, depending on the research situation, it may be helpful to analyse different scenarios, including one(s) where we assume that our initial assumptions are wrong. Ideally, researchers are able to take steps to reduce or eliminate limited diversity since none of the existing solution types are without problems. However, since this is not always possible, it is best to be aware of the consequences of choices.

In this article, we propose a formative-reflective scheme for the assessment of Tourism Destination Competitiveness (TDC) based on a combined use of Partial Least Squares-Path Modelling (PLS-PM) and the method recently proposed by Fattore, Pelagatti, and Vittadini (FPV). TDC is conceived as a construct reflecting the tourism performance of a destination, and several determinants are considered, including endowed resources, created resources, and supporting factors. The proposed scheme is applied to a case study on 1575 Italian municipalities for which the Italian National Institute of Statistics released data on tourist flows. Our contribution is innovative for three aspects: (i) the consistency of the formative-reflective scheme for TDC assessment is discussed on a theoretical basis; (ii) an empirical comparison between PLS-PM and the FPV method is performed; (iii) data with higher granularity than most studies on TDC assessment are employed. Our findings highlight that endowed resources are the primary driver of TDC, followed by created resources and supporting factors, and emphasize that the best ranked destinations are big cities with a multifaceted tourism alongside sea and mountain destinations with cultural attractions.

Based on previous research on political trust on the one hand and the effects of perceived survey sponsors on political attitudes on the other, this paper sets out to explore the effects of misperceiving the survey sponsor on political trust among citizens. The article explores the significance of the effect of survey sponsor misperception among factors that are traditionally used to explains political trust. Using Afrobarometer data, which includes thirty-six democratic and autocratic countries and more than fifty-thousand respondents, the paper demonstrates that such an effect is significant and substantive. Hence, researchers should definitely take survey sponsor misperception into account when designing and analyzing surveys. In conclusion, the article provides an outlook on what this means for future survey research.

The Indian education sector is booming with increasing number of students enrolling for various educational courses for acquiring higher education. The competition for lucrative jobs adds to the pressure on students to perform in competitive exams for higher education and other skill development courses.Query As a result, students turn to Private Coaching Classes also called Shadow Education and Private Supplementary Tutoring for additional help. Equally competitive environment exists for shadow or coaching institutes. They too face demanding customers which exerts a lot of pressure on them to achieve academic excellence. In this study, quality management perspective was applied to institutional practices along with Interpretive Structural Model methodology and MICMAC technique for developing a framework to enhance students’ learning and academic performance in shadow institutes for higher education. Attempt has been made to construct a hierarchical structural model for decision making which takes into account all strategic issues and their interrelationships encountered by shadow institutions. This model or structure if implemented can also help shadow institutes to achieve sustained growth in a highly competitive and dynamic environment.

Metasynthesis is an approach to synthesizing primary qualitative research, and may take either an aggregative or an interpretive approach. In either case, the resulting synthesis inevitably occurs at a remove from both the empirical and the theoretical contexts of the original research. We argue that seeking to retain these contexts in the synthesis poses specific challenges. Thus, the empirical context of an original study and the individuality of participants’ first-order accounts will be incompletely and selectively represented in a published study, and will be further out of reach at the level of synthesis. Syntheses should therefore be faithful to, but not seek to reproduce, the empirical context of the primary studies. As regards theoretical context, accommodating the concepts and the broader theoretical frameworks of primary studies may require potentially divergent philosophical assumptions to be reconciled with each other and with the theoretical standpoint of the synthesist. Selecting studies where these assumptions are compatible, at the level of both theory and methodology, may lessen this challenge. Some metasyntheses seek to integrate not just concepts but also theories (metatheorizing), but here the challenges of philosophical and theoretical compatibility are more acute, and the means of achieving such integration appear to be underdeveloped.

This study investigates the dynamic relationship between economic policy uncertainty (EPU), geopolitical risks (GPR), the interaction of EPU and GPR (EPGR), and inflation in the USA, Canada, the UK, Japan, and China. We employ the continuous wavelet transform (CWT) to track the evolution of model variables and the wavelet coherence (WC) to examine the co-movement and lead-lag status of the series across different frequencies and time. To strengthen the WC, we apply the multiple wavelet coherence (MWC) to determine how good the linear combination of independent variables co-moves with inflation across various time-frequency domains. The CWT reveals heterogeneous characteristics in the evolution of each variable across frequencies. Inflation across samples shows strong variance in the short-term and medium-term while the volatility fizzles out in the long-term. For the explanatory variables, a similar pattern holds for EPU except for Japan and China, where coherence is evident in the short-term. The USA’s and Canada’s GPR reveal strong coherence in the short- and medium-term. Also, the UK and China reflect strong coherence in the short-term but weak significance in the medium-term, while Japan’s GPR reflects only strong coherence in the short-term. The EPGR shows strong variation in the short-and-medium-term in the samples except in China. The WC’s phase-difference reflects bidirectional causalities and switches in signs among series across different scales and periods in the samples, while the MWC reveals the combined intensity, strength, and significance of both EPU and GPR in predicting inflation across frequency bands among the countries. Findings also show significant co-movement among series at date-stamped periods, corroborating critical global events such as the Asian financial crisis, Global financial crisis, and COVID-19 pandemic. The paper has policy implications.

An estimated 55% of the global population live in cities, with this expected to increase to 70% by 2050. Thus, the strain from urbanisation generates issues like water pollution and land degradation leading to further social and environmental problems. Smart sustainable cities have been proposed as a possible solution but are a relatively new concept and are theoretically underdeveloped, and implementation applicability continues to be understud-ied. Despite the uncertainty around the idea, many cities globally have created distinctive visions of a smart, sustainable city. This paper developed a measurement instrument based upon a prior conceptualisation that embraced the subjective nature of the citizenry's perceptions of a smart sustainable city. The measurement instrument was initially refined from a large statement list of 80 from the initial conceptualisation before statistically honing this instrument through exploratory factor analysis and confirmatory composite analysis. This is before applying the tool in the real-world context in various cities in Malaysia and UK. Known group validity was additionally used to verify the instrument, comparing between Malaysian and UK participants and between four different cities. A twenty-item measurement instrument consisting of four factors, Planning, Environment, Social and Smart, was developed from this study. These results support current theoretical perspectives with only minor variations from the core theory; however, this better reflects the dynamics of the smart sustainable city phenomenon.

In economics, the Expected Utility (EU) model is the normative benchmark for decision making under risk. However, empirical studies have shown that many behavioural phenomena are inconsistent with the predictions of this model. Observing empirical evidence, behavioural economics has demonstrated that people do not always make rational, or optimal, decisions, even if they have the information and tools with which to do so. People’s choices are influenced by their emotions and impulsiveness, and depend on their environment and circumstances (context): overconfidence, loss aversion, and self-control are the main statements in behavioural economics. Starting from past studies concerning the use of multi-criteria method based on prospect theory for describing people behavior (Gomes and Lima 1991), this paper aims at proposing a multi-criteria method as a tool to model some behavioural anomalies. It begins by analysing some decision problems proposed in the literature (Kahneman and Tversky 1979), showing that people’s preferences may violate some of the axioms of the EU model. Then, it proposes a multi-criteria approach to model human behaviour in these problems, that is, when emotional factors and cognitive biases influence people’s choices: e.g., the certainty effect and the reflection effect. Among several multi-critria methods, the ELimination Et Choix Traduisant la REalité (ELECTRE) method is used due to its main features: it allows dealing with positive and negative reasons in order to model the preferences, including with heterogeneous scales; its preference and indifference thresholds allow taking into account an imperfect knowledge of the data; no systematic compensation exists between “gains” and “losses”. The results show that the ELECTRE III method provides the same preferences expressed by the majority of respondents in the experiments considered. In other words, a multi-criteria tool accounts for some behavioural anomalies, in particular, those relating to certainty and reflection effects.

Studying Information and Communications Technology (ICT) development is increasingly difficult because most advanced countries converge to similar network structures. However, developing countries still manifest meaningful variance in ICT development, affording theoretical elaboration on the nature of societal ICT processes. We examine the relationships between corruption, anti-corruption, interorganizational networks, and ICT development for 48 African countries. Previous studies observed that increased ICT development is associated with lower levels of corruption, although theory to explain this has yet to develop. In the context of interorganizational networks, we theorized that the degree of centralization-decentralization is a key variable in explaining ICT development. We found support for the proposition that there is less ICT development when corruption is high and interorganizational networks are more centralized. In contrast, there is greater ICT development as decentralization increases, coupled with more anti-corruption news. Nevertheless, media freedom was inconsequential regardless of the interorganizational network structure. Lastly, corruption was negatively related to economic growth.

Pakistan experienced consistent rise in budget deficit and current account deficit that dampened the country’s abilities to achieve sustained economic growth over the last several years. To this aim, this study intends to investigate the existence of twin deficit hypothesis in the presence structural breaks over the last four decades. Utilizing time series data for the years 1980–2019 and employing Zivot-Andrew unit root test, ADF, PP and ARDL approaches, we find that budget deficit being statistically significant in the short-run and long-run positively affect the current account deficit of the country. The effects are unaltered when we run regression with structural break and without structural breaks. The study, thus, confirms the presence of twin deficit hypothesis in both the short-run and long-run periods in case of Pakistan. Thus, a consistent, well devised and prudent fiscal policy help alleviate the current account deficit of Pakistan over the last four decades.

Pretesting survey questions via cognitive interviewing is based on the assumptions that the problems identified by the method truly exist in a later survey and that question revisions based on cognitive interviewing findings produce higher-quality data than the original questions. In this study, we empirically tested these assumptions in a web survey experiment ( n = 2,200). Respondents received one of two versions of a question on self-reported financial knowledge: either the original draft version, which was pretested in ten cognitive interviews, or a revised version, which was modified based on the results of the cognitive interviews. We examined whether the cognitive interviewing findings predicted problems encountered in the web survey and whether the revised question version was associated with higher content-related and criterion-related validity than the draft version. The results show that cognitive interviewing is effective in identifying real question problems, but not necessarily in fixing survey questions and improving data quality. Overall, our findings point to the importance of using iterative pretesting designs, that is, carrying out multiple rounds of cognitive interviews and also testing the revisions to ensure that they are indeed of higher quality than the draft questions.

It is critically important to correctly identify persons with a lifetime history (LTH) of suicide attempts (SA) from both a clinical and research perspective. Face-to-face interviews are often the best available method for researchers to collect data about a complex phenomenon like a LTH of SA. However, extensive survey methodology research has shown that probing sensitive topics like a LTH of SA are sensitive for interviewer-related errors or interviewer effects. Studies investigating these interviewer effects are scarce in the field of suicide studies. This study presents a possible roadmap for study of interviewerrelated measurement error and an exploration of role-dependent behaviour of interviewers by assessing the LTH of SA through an epidemiological design. Data from the baseline assessment of the Netherlands Study of Depression and Anxiety (N = 2981) was used to illustrate the proposed roadmap to study interviewer effects. Results show: : (1) that it was possible to identify the existence of interviewer effects in assessing a LTH of SA; (2) that interviewer effects occurred by probing and clarification activities of the interviewer but not with inadequate formulation of the original question and so give a possible explanation for these effects; and (3) that it was possible to study the impact of these effects on the association between a well-known risk factor and LTH of SA. Applying the Measurement Error framework for systematically examining errors in data collection on suicidality seems a promising method.

Sufficient and nourishing foods during the early years of a child’s life are essential for optimal growth and healthy life. The existing disparities among rural-urban populations also affect the dietary pattern as well. Therefore, this study aims to identify the factors that contribute towards the rural-urban disparity in children’s dietary diversity (CDD) and quantify their importance for the reduction of rural-urban disparities in achieving a minimum dietary diversity level. Using Pakistan Demographic and Health Survey (PDHS) data, version 2017-18, a non-linear decomposition analysis was performed. Eighty-one (81%) of the gap in CDD between rural-urban areas is attributed to the differences in the observed factors (endowments) and of these most of the difference is explained by three factors i.e. number of antenatal care visits (45%), maternal education (18%) and type of toilet facility (15%). There is a need to explore maternal education-related interventions to decrease the rural-urban gap regarding CDD as maternal education may affect CDD through different dimensions. Moreover, such programs should be initiated that may be helpful to enhance women’s role in society, such as skilled education, well-paid job opportunities and better health facilities.

Scores of researchers have paid attention to empirical and conceptual dimensions of Customer relationship management (CRM). A few studies summarise the research output of CRM focusing on a specific industry. Nevertheless, there is scant literature summarising the research output of CRM in contrast to the data mining-based CRM. This study presents a scientometric analysis that evaluates CRM research output with a special focus on data mining-based CRM. Bibliometric data were extracted for the period 2000–2020 from the Web of Science database to apply descriptive analysis and scientometric analysis to obtain the bibliometric profile of CRM research. Further, we generated the conceptual structure map using multiple correspondence analysis and clustering for CRM and data mining-based CRM research fields. Interestingly, the analysis revealed that the future trendfi of CRM research would be based on techniques associated with machine learning and artificial intelligence. The study provides extensive insight into the basic structure of the CRM and data mining-based CRM research domain and identifies future research areas.

This paper explores changes in age–specific mortality risk across periods and cohorts during the twentieth century in the developed world. We use and compare two approaches—one graphical (Lexis plots) and one statistical (an adapted Hierarchical age–period–cohort model)— that control out overall trends in mortality, to focus on discrete changes associated with specific events. Our analyses point to a number of key global and local events in the Twentieth Century associated with period and/or cohort effects, including the World Wars and the influenza pandemic of 1918–19. We focus particularly on the UK but look at other countries where results are particularly noteworthy, either substantively or methodologically. We also find a decline in mortality in many western countries, specifically in the 1948 birth cohort, which may be associated with the development of post–war social welfare policies, the economic investment in Europe by the United States, the accessibility of antibiotics such as penicillin, and, in the UK, the founding of the NHS. We finish by considering the advantages and disadvantages of using the two methods with different sorts of data and research questions.

Stereotypes do not have a unique definition, being mostly considered a generalized belief on the quality and characteristics of members of specific groups or social categories. Hence, various scales and measurements have been proposed to assess the endorsement of beliefs on the association of gender and scientific/language-related skills. The aim of the paper was to summarize, compare and discuss those measures, distinguishing between explicit, implicit and indirect measures. The review of the literature highlighted a huge but unrecognized heterogeneity in the constructs of gender stereotypes, especially for explicit measures. This can hamper findings comparability, reduce scales’ validity, affect the correlation between implicit and explicit measurements, and bias their interpretations due to ambiguous terminologies.

Choosing the University to attend is an important decision that is made once or twice in a lifetime and has relevant effects for a person's entire life. In such a process, advice from others, especially current students, is a powerful influencing factor. Therefore, understanding the factors that lead students to become active advocates for their university is strategically important. Social identity theory states that when students choose a university, the image of the institution becomes part of their identity. In case of strong positive identification , the resulting sense of pride enhances their own self-identity and brings positive benefits beyond simply obtaining an education, which are then passed on to everyone. The current study focuses on brand experience and brand reputation and uses a moderated mediation analysis to investigate the mechanisms by which current students can be tools for university choice. Stimulating word-of-mouth (WOM) implies the institution to have and maintain a good reputation and engage students to develop a positive brand experience and pride. This research contributes to the development of a greater strategic awareness of universities' appeal to better tailor their orientation activities to current or prospective students.

Patient decision-making concerning therapy choice has been thoroughly investigated in the Push/Pull framework: factors pushing the patient away from biomedicine and those pulling them towards Complementary and Alternative Medicine (CAM). Others have examined lay etiology as a potential factor in CAM use. We conducted semi-structured interviews with patients employing only biomedicine and those using CAM. The coded and segmented data was quantified and modelled using epistemic network analysis (ENA) to explore what effects push/pull factors and etiology had on the decision-making processes.There was a marked difference between our two subsamples concerning push factors: although both groups exhibited similar scaled relative code frequencies, the CAM network models were more interconnected, indicating that CAM users expressed dissatisfaction with a wider array of phenomena. Among pull factors, a preference for natural therapies accounted for differences between groups but did not retain a strong connection to rejecting conventional treatments. Etiology, particularly adherence to vitalism, was also a critical factor in both choice of therapy and rejection of biomedical treatments. Push factors had a crucial influence on decision-making, not as individual entities, but as a constellation of experienced phenomena. Belief in vitalism affects the patient’s explanatory model of illness, changing the interpretation of other etiological factors and illness itself. Scrutinizing individual push/pull factors or etiology does not explain therapeutic choices; it is from their interplay that decisions arise. Our unified, qualitative-andquantitative methodological approach offers novel insight into decision-making by displaying connections among codes within patient narratives.

The ageing population and society (APS) nexus is one of the key grand challenges of this millennium. And yet, the systematic analysis of scholarly literature on the APS nexus has remained under the radar. This study responds to this gap and employs a quantitative approach through a scientometric analysis of literature on the APS nexus to inform policy discussions and guide future research directions. This study adopts quantitative scientometric methods to examine the APS literature (n = 566) between 2011 and 2020 found in the Scopus database. The analysis reveals key research topics and recognizes the most important articles, authors, publication outlets, institutions, and countries in the field. The findings indicate that while issues such as ageing population, gender, quality of life, and socio-economic aspects of ageing have received significant interest, social exclusion of older adults, age diversity, social policy, and the eldercare workforce have received less attention. As challenges associated with the APS nexus will continue to gain currency in the future, this paper discusses the implications of the findings on (a) future research direction and (b) north-south research collaboration. The analysis shown in this paper should be of interest to scholars and policymakers interested in addressing the challenges associated with the APS nexus.

Technology has recently gained relevance within collaborative learning environments to provide robustness, agility and flexibility. Several recent studies have investigated the role of technology, as well as researchers have defined different metrics to assess learning outcomes and experience along the collaborative knowledge development process. More recently, technology has played a key role to face the new challenges related to COVID-19, which forced to move on remote or hybrid learning. This research focuses on the quality of learning experience in terms of academic performance and perceived satisfaction. From a methodological point of view, a conceptual framework has been proposed and a quantitative study has been conducted among undergraduate and postgraduate students that are undertaking programs related to System Design in Saudi Arabia universities. 152 responses have been collected through an online survey and analysed using SPSS and SmartPLS. Results show a positive impact of technology along the collaborative knowledge development process and a strong correlation among the different quality of learning experience parameters considered. Indeed, despite some challenges, an integrated use of technology seems to properly support the most pressing needs in terms of quality experience, while the well-known social/educational issues related to the COVID-19 pandemic are not object of this study. Those findings are expected to contribute to the Saudi Arabia’s vision 2030 and, more holistically, to the assessment of collaborative learning environments that extensively rely on technology.

Notwithstanding the huge literature on state studies, both definition and method have always been subject of intense debate. This debate is still open and equally intense despite two millennia of philosophical and methodological attempts to define what the state is, to describe how the state works, and why does it work. As times, geopolitical contexts, and human action have shaped the historical and conceptual trajectory of polity studies, the theories as well as methodologies have increasingly emphasized focus on individuals, (political) cultures, power, and relationships both between individuals and between individuals and the state. With time, the study of these types of relationships have revealed the complexity of the state, and the dynamics of its change. Though economy and political economy theories of the twentieth century gradually diminished the central role of the state in economy in favor of the free market and individual and company small as well as big entrepreneurship, the increased focus on individuals and individual (inter)action(s) has paradoxically turned into a revival of the state, a reinforcement of its role, as latest neo-statism trends reveal. It was the COVID-19 global pandemics to highlight what people think and expect from the state in the volatile European and global political context of our time. Not only that isolation and social distance conditions have deepened and strengthened the perception of the state as the source of their security and receptor of their highest level of trust but have also revealed that the state studies are about to reach again a turning point in the philosophical thinking about society and polity.

Rural depopulation in advanced economies has negative economic consequences for local communities and requires effective policies at different spatio-temporal and governance scales. Since the 1980s, an economic subsidy (Rural Employment Plan, PER-PFEA) was implemented at municipality scale in two autonomous communities of Spain (Andalusia and Extremadura) with the aim at improving the economic performances and counteracting depopulation. Within this national plan, each municipality requests and receives funds for improving the labour conditions of temporary agricultural workers. Long-term implementation of the PER-PFEA programme allows assessment of the effectiveness of this subsidy scheme in containing depopulation. The present study classifies municipalities in the two Spanish regions according to their demographic dynamics over a decade (2002 − 2012) and their capacity to attract public funds from the plan. A threshold value based on logistic probabilities was estimated to assess its success in counteracting depopulation. Results identified four municipal profiles. On the one hand, subsidy application reduced depopulation probability in 23.3% and 8.4% of Andalusia and Extremadura municipalities, respectively. On the other hand, given the low level of requested funds, the programme did not reduce depopulation in 26.3% and 61.8% of Andalusia and Extremadura municipalities with a stagnant economy based on silviculture and livestock. While requesting few funds, 36.9% and 16.7% of municipalities exhibited population growth and a dynamic economic base. Finally, despite a large provision of funds, the subsidy seemed to have not reduced depopulation probability in 13.5% and 13.1% of municipalities, representing the most problematic contexts because of population shrinkage and economic backwardness. The study finally discusses some proposals for future improvements of the subsidy provision system.

Subjective wellbeing (happiness or life satisfaction) has become a fundamental goal in assessing social progress. However, given the limited understanding of the drivers of SWB in the context of India, formulating public policy has been difficult. The current study attempted to address this difficulty by examining social capital, or a particular type of it, as a potential predictor of life satisfaction. Further, we surmised that given the inherence of sharp gender-based social stratification in Indian society, the relationship between social capital and life satisfaction is bound to be different for males and females. We used trifurcate theoretical descriptions of social capital—bonding, bridging, and linking—and conducted principal component analysis (PCA) to empirically identify distinct components of social capital utilizing the data extracted from the sixth wave of the World Values Survey (2010–2014). In India’s gender-based stratified society, we found that men tend to have higher social capital levels than women. The multivariate regression analyses reveal gender preferences of social capital: while bridging social capital was significantly crucial for happiness among men, it was bonding social capital for women. We found that linking social capital predicts life satisfaction for both genders, indicating that policies to improve the functioning of, and nurturing a trustworthy environment towards formal institutions—such as the legislative and judiciary bodies—among the citizens, may maximize and optimize their happiness.

This paper investigates the impact of subcomponents of collaboration: information shar�ing, connectivity, coordination, integration, and visibility on the tea supply chain resilience of Sri Lanka during the Covid-19 pandemic. This research was carried out with mixed methods. The quantitative approach adopted a systematic random sampling technique to determine the sample size (n=137), whereas the qualitative study used the purposive sam�pling technique to determine the sample size (n=6). Self-administered questionnaires were distributed to tea supply chain professionals and structured interviews were conducted to collect data for the qualitative approach of the study. This study found that all the sub�components of collaboration positively impacted on tea supply chain resilience during the Covid-19. Moreover, connectivity and coordination have the highest positive impact on tea supply chain resilience while the other three sub-components: information sharing, inte�gration and visibility have signifcant but relatively less positive impact on supply chain resilience. Thus, tea exporting companies must prioritise, take action steps for enhancing connectivity and coordination when formulating supply chain strategies to enable supply chain resilience. The study being one of the latest empirical studies taking Sri Lankan tea supply chain as a case study, contributes to the knowledge having identifed the impact of sub-components of collaboration on tea supply chain resilience during Covid-19. Sri Lanka is one of the most vulnerable middle-income countries and its economy sufered severely during the Covid-19 outbreak. The fndings will be supportive in making tea supply chains much stronger, providing a robust contribution to the country’s GDP as part of Sri Lanka’s national eforts in economic rebuilding.

The exponential growth of social media has brought an increasing propagation of online hostile communication and vitriolic discourses, and social media have become a fertile ground for heated discussions that frequently result in the use of insulting and offensive language. Lexical resources containing specific negative words have been widely employed to detect uncivil communication. This paper describes the development and implementation of an innovative resource, namely the Revised HurtLex Lexicon, in which every headword is annotated with an offensiveness level score. The starting point is HurtLex, a multilingual lexicon of hate words. Concentrating on the Italian entries, we revised the terms in HurtLex and derived an offensive score for each lexical item by applying an Item Response Theory model to the ratings provided by a large number of annotators. This resource can be used as part of a lexicon-based approach to track offensive and hateful content. Our work comprises an evaluation of the Revised HurtLex lexicon.

Without proper preparation by higher institutions, the COVID-19 pandemic has forced the world to rely on online learning. Even students of social science and science are looking for different knowledge and skills. Currently, both groups rely on the same method to gather knowledge for future undertakings. Given the uncertainty regarding the resolution of COVID-19, which has driven students to continue using online learning, the current study aims to identify the factors of willingness to continue online learning among social science and pure science students by extending the use of expectation-confirmation theory. Applying a purposive sampling method, 2,215 questionnaires were collected among undergraduate students from Universiti Malaysia Terengganu (UMT) using an online survey. Current study found that expectation and confirmation positively affect satisfaction. Attitude, satisfaction and readiness were found to have a positive relationship with willingness to continue online learning. Meanwhile, self-efficacy was found unsupported hypothesis for the direct effect. For multigroup analysis, readiness was found to have a significant difference between students of social science and pure science. The findings of this research enrich the literature about online learning, especially in the COVID-19 setting. Moreover, this work is useful for higher education institutions seeking to design a better strategy that allows students to return to campus.

The purpose of this paper is to suggest recommendations for improving research quality and theory development by addressing four issues. Two of them concern hypotheses, their statements, and observed inconsistencies found in articles; the other two address issues related to theoretical perspectives and triangulation. We used a purposive sampling of scientific fields and articles that support and ground the ideas of this general review. This study clarifies inconsistent opposite relationships among variables found in theoretical models, discusses the usage of verbs in hypotheses, focuses on the cause-and-effect relationship between variables, identifies ways in which to rectify inconsistencies by including additional theoretical perspectives, and reminds the research community about triangulation. This paper concludes with the implications for research, practice, and society.

Scanner data can be obtained from a wide variety of retailers (supermarkets, home electronics, Internet shops, etc.) and provide information at the level of the barcode, i.e. the Global Trade Item Number or its European version: European Article Number. One of advantages of using scanner data in the Consumer Price Index measurement is the fact that they contain complete transaction information, i.e. prices and quantities for every sold item. One of new challenges connected with scanner data is the choice of the index formula which should be able to reduce the chain drift bias and the substitution bias. Multilateral index methods seem to be the best choice in the case of dynamic scanner data sets. These indices work on a whole time window and are transitive, which is a key property in eliminating the chain drift effect. Following the so-called identity test, however, one may expect that even when only prices return to their original values, the index becomes one. Unfortunately, the commonly used multilateral indices (GEKS, CCDI, GK, TPD, TDH) do not meet the identity test. The paper discusses the proposal of two multilateral indices, the idea of which resembles the GEKS index, but which meet the identity test and most of other tests. In an empirical study, these indices are compared, inter alia, with the SPQ index, which is relatively new and also meets the identity test. Analytical considerations as well as empirical study confirm the high usefulness of the proposed indices.

Recent time the performance measurement of any institute (or author) is considered as one of the major issues. The reason is the given institute publishes several papers in distinct area which may contain multiple-affiliations or authors. In this case, the precise analysis of quantity and quality with respect to domain based expert, the founding author is difficult tasks. It requires different metric based on expert Turiyam awareness rather than just h-index, i10, g-index or t-index. To achieve this goal, a method is proposed to investigate the high quality papers, type of co-authorship, highly cited papers, regular paper, special issues, short notes, review, letter to editor, book chapter, conference papers, frontier research etc. The motive is to measure the two institutes and its performance equally on several other parameters rather than only document count and citation. The proposed method will also provide a way to measure the performance of any Institute using patent granted, copyright and software code. In addition another method is proposed to visualize the performance of two or more institute in hierarchical order using properties of concept lattice. To achieve this goal, one of the best Indian institutes i.e. IISC Bengaluru is considered for the illustration. The data is taken from SCOPUS to demonstrate the method and its validation. Graphical abstract

This research conducts a meaningful comparison of self-reported importance and satisfaction in various life domains to promote the understanding of subjective well-being (SWB). Results from a nationwide telephone survey with regard to 12 indicators of well-being in Taiwan suggest that satisfaction attitude rating does incorporate the judgment of importance among SWB indicators. We further reveal different patterns of association, similarity, and discrepancy between importance and satisfaction across SWB indicators by performing both explanatory and confirmatory analyses. All the various analytical results lead to crucial meanings for interpreting the general level of SWB, especially under a cultural context such as Taiwan society. The adapted Importance-Performance analysis classifies the SWB measurements into four meaningful quadrants according to the importance and satisfaction scores. Applying a regression modelling strategy, we explore how importance judgment together with other demographic factors influences the satisfaction attitude and further verify what potential factors relate to their discrepancy. Graphical analyses enhance the presentation.

The scientific literature dealing with food security is vast and fragmented, making it difficult to understand the state of the art and potential development of scientific research on a central theme within sustainable development. The current article, starting from some milestone publications during the 1980s and 1990s about food poverty and good nutrition programmes, sets out the quantitative and qualitative aspects of a vast scientific production that could generate future food security research. It offers an overview of the topics that characterize the theoretical and empirical dimensions of food security, maps the state of the art, and highlights trends in publications’ ascending and descending themes. To this end the paper applies quantitative/qualitative methods to analyse more than 20,000 scientific articles published in Scopus between 2000 and 2020. Evidence suggests the need to find more robust links between micro studies on food safety and nutrition poverty and macro changes in food security, such as the impact of climate change on agricultural production and global food crises. However, the potential inherent in the extensive and multidisciplinary research on food safety encounters limitations, particularly the difficulty of theoretically and empirically connecting the global and regional dimensions of change (crisis) with meso (policy) and micro (individual behaviour) dimensions.

It is well known the effect on uncentered $$R^2$$ R 2 stemming from adding a constant to dependent variable in a linear regression model with intercept. In this paper, we investigate the effect of adding a constant to variables on the uncentered $$R^2$$ R 2 when a linear regression through the origin is used. In particular, we consider two cases. First, a constant $$c \in \mathbb {R}$$ c ∈ R is added to all observations of the dependent variable. Second, a constant $$c \in \mathbb {R}$$ c ∈ R is added to all the observations of both the dependent variable and at least an independent variable. We show that in both cases there is an artificial variation of the uncentered $$R^2$$ R 2 . This quantity is not invariant under location change.

This paper presents a method that uses observed data from an age-period table to set bounds on the age, period, and cohort effects in an age-period-cohort multiple classification (APCMC) model. The rationale is that with enough periods over a long time span the age distributions within periods on the dependent variable will be affected by different sets of cohorts for each of the periods. This is likely to result in different trends in these separate period age distributions such that the trends in the age distributions will encompass the trend in the age effects that generated the dependent variable values. This approach can help to identify bounds that likely encompass the age, period, cohort parameters that generated the data. The data used in this papers are estimated homicide arrests by single years for those aged 15–64 for the periods 1964 to 2019 in the United States. I utilize the observed trends in the age-distributions for each of the 56 periods as different constraints on the trends for the age effects in the APCMC fixed effects model. These estimates are used to form bounds on the age effects, period effects, and cohort effects.

The main objective of this empirical study to examine the impact of corruption, unemployment and inflation on economic growth for seventy nine (79) developing countries of the world for the period from 2002 to 2018. This study uses Panel unit root tests (PUT), Pooled Mean Group (PMG), Fully modified ordinary least square (FMOLS), and Dynamic least square (DOLS), for the data estimation. The estimates of PUT reveal that all the variables are mixed order of integration. The PMG, FMOLS and DOLS estimates reveal that corruption, unemployment and political stability have negative effect on GDP per capita, while Inflation, governance effectiveness and rule of law have positive effect on GDP per capita. This empirical study has some policy implication for government and policy makers. Graphical Abstract

The year 2020 has marked the beginning of a new life in which humans must struggle and adapt to coexist with a new coronavirus, known as COVID-19. Population density is one of the most significant factors affecting the speed of COVID-19’s spread, and it is closely related to human activity and movement. Therefore, many countries have implemented policies that restrict human movement to reduce the risk of transmission. This study aims to identify the temporal dependence between human mobility and virus transmission, indicated by the number of active cases, in the context of large-scale social restriction policies implemented by the Indonesian government. This analysis helps identify which government policies can significantly reduce the number of active COVID-19 cases in Indonesia. We conducted a temporal interdependency analysis using a time-varying Gaussian copula, where the parameter fluctuates throughout the observation. We use the percentage change in human mobility data and the number of active COVID-19 cases in Indonesia from March 28, 2020, to July 9, 2021. The results show that human mobility in public areas significantly influenced the number of active COVID-19 cases. Moreover, the temporal interdependencies between the two variables behaved differently according to the implementation period of large-scale social distancing policies. Among the five types of policies implemented in Indonesia, the policy that had the most significant influence on the number of active COVID-19 cases was several restrictions during the Implementation of Restrictions on Community Activities (Pelaksanaan Pembatasan Kegiatan Masyarakat/PPKM) period. We conclude that the strictness of rules restricting social activities generally affected the number of active COVID-19 cases, especially in the early days of the pandemic. Finally, the government can implement policies that are at least equivalent to the rules in PPKM if, in the future, cases of COVID-19 spike again.

Pakistan experienced consistent rise in budget deficit and current account deficit that dampened the country’s abilities to achieve sustained economic growth over the last several years. To this aim, this study intends to investigate the existence of twin deficit hypothesis in the presence structural breaks over the last four decades. Utilizing time series data for the years 1980-2019 and employing Zivot-Andrew unit root test, ADF, PP and ARDL approaches, we find that budget deficit being statistically significant in the short-run and long-run positively affect the current account deficit of the country. The effects are unaltered when we run regression with structural break and without structural breaks. The study, thus, confirms the presence of twin deficit hypothesis in both the short-run and long-run periods in case of Pakistan. Thus, a consistent, well devised and prudent fiscal policy help alleviate the current account deficit of Pakistan over the last four decades.

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