Which cognitive factors are necessary for the manifestation of mathematical talent? A necessary condition analysis approach

Identifying single necessary conditions with NCA and fsQCA

Purpose The investigation on the complexity of customer retention towards green products/services requires more solid analytical approaches. This study evaluated the net effects of antecedents of customer retention and the validity of configurational causal recipes that lead to customer retention in the green hotel context. Design/methodology/approach This study combined structural equation modeling (SEM), a fuzzy-set qualitative comparative analysis (fsQCA) and a necessary condition analysis (NCA). An online survey was conducted in China to evaluate the green hotel consumption. Findings Research findings showed that cognitive factors (perceived health benefits, green product performance, responsible employee performance and green physical environment performance) and affective factors (emotional well-being, feeling of happiness, attractiveness of green product and feeling of pride), played a distinctive role in generating customer retention toward green hotel products. The NCA found no factor was essential in order to achieve customer retention, which indicates that green hotel performance and brand management should pay more attention to emotional factors alongside cognitive factors. Practical implications Research findings provide significant managerial implications for improving green hotel services and business operations and enhancing consumers’ approach intention toward green hotel products. Originality/value This study adopted mixed approaches to investigate both the linear and nonlinear impacts of cognitive and affective factors that potentially lead to customer retention for green hotel products.

PurposeLarge supermarket chains are adopting customer-service robots to improve service delivery in physical stores. Successful deployment of these robots depends on shoppers' willingness to interact with them, requiring an understanding of influencing factors. This study, grounded in the Cognitive-Affective-Normative (CAN) theory, seeks to systematically explore the factors influencing Gen Z shoppers' willingness to interact with customer-service robots.Design/methodology/approachA hybrid approach combining Structural Equation Modeling (SEM) and Necessary Condition Analysis (NCA) was employed to analyze survey data collected from 945 Gen Zs in the Czech Republic.FindingsThe results from SEM highlight significant cognitive, normative, and affective factors that influence the intention of Gen Z shoppers to interact with a customer-service robot. Specifically, cognitive factors such as effort and performance expectancy, along with normative factors like subjective norms, emerged as critical determinants. Furthermore, affective factors such as technology anxiety and positive emotions significantly influence users' readiness to use customer-service robots for service requests. The study also underscores that positive emotions, effort expectancy, performance expectancy, and subjective norms are vital prerequisites for interacting with customer-service robots.Originality/valueThe originality of this work lies in its two significant contributions to the burgeoning field of SRs in retail literature. First, it extends the CAN theory to the context of SRs among Gen Z shoppers in Czechia, thereby enriching the existing literature on SRs in retail. Second, by employing a hybrid analytical approach, our research offers both empirical and methodological advancements, providing rigorous insights crucial for enhancing the understanding of the pivotal factors influencing shoppers' interactions with SRs in physical store environments.

This study aims to assess the suitability of students in Bachelor of Science in Statistics program. Seemingly, several students who enrolled in the said program does not possess the qualities of being mathematically inclined. Hence, this study was conducted. By complete enumeration, the study employed all BSS students from different year level. Secondary data were used such as two psychological tests from the University Student Services Office which measures the intelligence and numerical aptitude. A primary data was also employed through an instrument called Brainard Occupational Preference Inventory which measures the interest of students in the field of statistics. The gathered data were then analyzed with the aid of some descriptive measures and correlational methods. Results revealed that there are only a few (11.9%) who have high levels of intelligence and numerical aptitude but they happen to have low level of interest in statistics. Of those students highly interested (47.6%) in the field of statistics one-fourth (11.9%) of them have low levels of intelligence and numerical aptitude. It is found out that there is a significant linear relationship between intelligence and numerical aptitude among BSS students. Moreover, intelligence and interest in statistics is inversely and significantly correlated among BSS junior students. Furthermore, results showed that there is no significant linear relationship between numerical aptitude and interest in statistics across year level. Hence, the interest of the BSS students must be cultivated in order to increase their level of achievement.

Identifying must-have factors and should-have factors affecting the adoption of electric motorcycles – A combined use of PLS-SEM and NCA approach

The article is devoted to the formation and improvement of competencies of teachers and psychologists of secondary schools to identify and develop mathematically gifted students. It has been identified the components of the training program of basic competencies that psychologists and subject teachers must have to recognize and develop mathematical talent. The results of an empirical study of an educational project are online training for educators to deepen their theoretical knowledge of mathematical talent and the development of practical skills of organizing the educational process for students with a high level of ability in the field of exact sciences. It was found that training in the development of competencies is an effective way to improve the skills of teachers to understand the essence of talent, the peculiarities of its detection in students, prevention of loss of potential, development of individual educational trajectories, use of new learning technologies and ways to develop personal skills.

Beyond borders: The transcendent effect of Korean celebrity credibility on brand perceptions among Malaysian youth - A necessary condition analysis.

Big data analytics capabilities (BDAC) have emerged as a critical factor in driving innovation and achieving sustainable competitive advantages, particularly in the context of green innovation (GI). This study conducts both a meta-analysis and a necessary condition analysis (NCA) to investigate the relationship between BDAC and GI. Synthesizing findings from 28 independent studies (N = 48,887), we confirm a significant positive relationship between BDAC and GI. Furthermore, our results reveal that firm-level factors significantly moderate this relationship: the positive effect is stronger for large firms (vs. SMEs), service industries (vs. manufacturing), and green technological innovations (vs. non-technological innovations). At the country level, meta-regression analysis indicates that ecological sustainability and ICT infrastructure positively moderate the BDAC-GI link, while R&D investment shows a negative average moderating effect. However, the complementary NCA reveals that all three country-level factors—ecological sustainability, R&D investment, and ICT infrastructure—are necessary conditions, establishing critical thresholds that must be met for BDAC to effectively foster GI. By integrating these two analytical approaches, the study provides a comprehensive understanding of the BDAC-GI relationship, identifies indispensable contextual prerequisites, and discusses the theoretical and practical implications for leveraging BDAC for sustainable development, suggesting directions for further investigation.

This study offers a novel analytical approach on the relationships between renewable energy consumption, capital, labor force, new firm formation rate, and economic growth. It aims to investigate such causal relationships using different estimation techniques such as the ordinary least squares (OLS) model, dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and canonical cointegrating regression (CCR), along with necessary condition analysis (NCA), which are applied to data for France over the period 1987–2017. Our results show that all necessary conditions yield outcomes ranging from small- to large-sized effects on economic development. The French government should readdress its efforts towards encouraging more beneficial investments in renewable energy consumption. This study opens up new insights for policymakers to maintain environmental protection and ensure sustainable economic growth. Finally, the use of NCA reduces complexity and allows a better understanding of the relationships involved.

Purpose Given the heavy pollution feature of the construction industry, construction corporations need to adopt an effective environmental governance strategy. The quality and quantity of environmental information disclosure (EID) implementation, as an essential part of a corporate environmental governance strategy, is impacted by the characteristics of the top management team (TMT). This paper aims to analyze the relationship between the demographic characteristics of the TMT (i.e. gender, age, tenure, educational level, and duality) and corporate EID. Design/methodology/approach Using data from listed construction corporations generated between 2014 to 2018 in China, this study employs the Tobit regression model to test the research hypotheses. Also, this study applies a novel analytical approach, necessary condition analysis (NCA), to conduct a series of additional tests. Findings The results reveal that tenure and educational level are significantly and positively related to EID, while gender, age, and duality in the executive role are not significantly related to EID. When considering the TMT size as a moderator, the TMT age is positively related to the corporate EID, and the size of the TMT acts as a moderator to weaken the positive effect of the TMT age on the EID. The NCA results show that TMT gender, age, tenure, and educational level are necessary when the levels of EID exceed 40%. Originality/value Our findings suggest that TMT characteristics have a relatively significant effect on corporate EID levels, which extends EID research to the construction industry. Corporate planners can endeavor to shape TMT characteristics to improve EID levels. The results of NCA provide insights into what TMT characteristics construction corporations need to satisfy in their pursuit of transparent EID, as well as the levels at which these characteristics are desired.

• A new complementary approach, combining PLS-SEM and NCA, is introduced. • Critical conditions for improving overall travel satisfaction are identified. • Necessary Condition Analysis is used to identify critical bottlenecks. • A medium level of comfort is necessary for achieving high overall travel satisfaction. • An increase in comfort, functionality/reliability and value for money is sufficient for improving overall travel satisfaction. In order to effectively manage transportation systems, and improve the attractiveness of public transport, public authorities, policymakers and researchers need a better understanding of the conditions necessary for improving attractiveness and those that can be considered sufficient. The purpose of this study is to expand the analytical toolbox of transportation research and introduce an analytical approach to identifying and distinguishing between the conditions that are necessary and sufficient for a desired outcome. Specifically, we suggest a complementary approach to combining partial least square structural equation modelling (PLS-SEM) and necessary condition analysis (NCA) in order to examine which service quality attributes (functionality, information, security/safety, comfort, and cost) are sufficient, and what degree of satisfaction with these attributes is necessary for high overall travel satisfaction. The data consists of subjectively reported experiences from over 900 users of public transportation in four northern European countries. We find that, for high overall travel satisfaction, a minimum level of satisfaction with comfort (equal to 33.1%) is necessary. Furthermore, an increase in satisfaction with comfort, functionality/reliability and cost is sufficient to improve overall travel satisfaction. This means that comfort is both a necessary and a sufficient condition, whereas functionality/reliability and cost are sufficient but non-necessary conditions in this context. We conclude that using this complementary approach can guide public transport managers and researchers in identifying important bottlenecks and establishing priorities for improving service quality, essential knowledge when developing effective strategies for attractive public transport services.

In this article, we present a statistical significance test for necessary conditions. This is an elaboration of necessary condition analysis (NCA), which is a data analysis approach that estimates the necessity effect size of a condition X for an outcome Y. NCA puts a ceiling on the data, representing the level of X that is necessary (but not sufficient) for a given level of Y. The empty space above the ceiling relative to the total empirical space characterizes the necessity effect size. We propose a statistical significance test that evaluates the evidence against the null hypothesis of an effect being due to chance. Such a randomness test helps protect researchers from making Type 1 errors and drawing false positive conclusions. The test is an “approximate permutation test.” The test is available in NCA software for R. We provide suggestions for further statistical development of NCA.

PurposeThis study aims to examine the motivation to purchase electric vehicles (EVs) using the theory of planned behaviour (TPB) with environmental concern modelled as a mediator. Additionally, the study applies necessity logic to identify the “should-have” and “must-have” factors for EV adoption.Design/methodology/approachA quantitative research design was adopted with data gathered from the patrons of various EV outlets and online car forums using a purposive sampling technique. The research model was tested using partial least squares structural equation modelling, while necessary condition analysis (NCA) was employed to determine the necessary conditions for EV adoption.FindingsThe results show positive and significant relationships among TPB variables (i.e. attitude, perceived behavioural control and subjective norm) on EV purchasing behaviour. NCA results reveal necessary conditions for all identified motivators, with varying degrees of minimum thresholds.Research limitations/implicationsThis study focuses on the current factors influencing EV adoption, which may evolve as the field of EVs advances. With the rapid growth of EV technology, including emerging features like autonomous driving, future research should consider these advancements to remain relevant. Additionally, this study is context-specific, and findings may not be generalizable across different regions or markets, warranting further investigation into other cultural or economic contexts.Practical implicationsThe findings provide actionable insights for marketers and policymakers aiming to enhance EV adoption by understanding both sufficiency and necessity conditions that drive consumer behaviour.Originality/valueThis study contributes to the literature by exploring EV adoption in an emerging economy, emphasizing environmental concern as a mediator. The integration of necessity logic complements the sufficiency-based findings, offering new insights for EV marketers and policymakers.

PurposeThe purpose of this paper is to employ a necessary condition analysis (NCA) approach to investigate the level of necessity of two conditions, marketing capability, including responsiveness to customers, responsiveness to competitors, responsiveness to the macro environment, and business relationship quality, and innovativeness capability for firm performance.Design/methodology/approachUsing a survey data set collected from a sample of 311 Vietnamese firms, this study explored the levels of necessity of the components of marketing capability and innovativeness capability by NCA. The study also tested the net effects of these components on firm performance by multiple regression analysis (MRA).FindingsThe MRA results reveal that except for responsiveness to the macro environment, other components of marketing capability and innovativeness capability have positive effects on firm performance. Further, firm size affects performance but industry types do not. The NCA results indicate that these conditions exhibit different levels of necessity for the occurrence of firm performance.Research limitations/implicationsA major limitation of this study is the exploration of necessary levels of only two key firm capabilities, i.e., marketing and innovativeness. Several other capabilities, such as, research and development, operations capabilities, and other market-based assets should be investigated in future research.Practical implicationsThe findings suggest that firms should pay attention not only to the net effects (β weights) but also to the levels of necessity of firm capabilities for their target outcome.Originality/valueThis study is among first studies investigating the levels of necessity of marketing capability and innovativeness capability for firm performance.

Some learners are more successful in foreign language mastering than others. Among the plausible explanations discussed in the literature (Carroll 1981; Skehan 1991; Dörnyei 2005; Stansfield & Reed 2019; Griffiths & Soruç 2020), the concept of foreign language aptitude (FLA) is regarded as one of the key factors that can influence or predict learners’ success in the process of foreign language acquisition. The present pilot quantitative study aims to assess the extent to which learners’ level of foreign language aptitude can be correlated to their general phonological ability based on the example of first-year MA English Philology students (N=10). To assess the students’ level of aptitude, the Polish adaptation of the Modern Language Aptitude Test (MLAT), called the Test of Aptitude for the Learning of Foreign Languages (Test Uzdolnień do Nauki Języków Obcych – TUNJO), was used. On the other hand, to measure their level of phonetic ability, the test, which focused on several chosen areas covered during practical and theoretical phonetics classes during the BA programme, was constructed and submitted to the group. The quantitative data gathered throughout those two stages were subsequently analysed and interpreted. The results obtained revealed no significant correlation between the students’ level of aptitude and their general phonetic ability. Other individual differences and affective factors in language learning, alongside the structure of the measuring tools and the measurement itself, may justify the apparent lack of correlation.