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Current economic upheavals and supply chain uncertainty have threatened the profitability and sustainability of business organisations. Procurement has proved to be one of the strategies for enhancing firm performance without necessarily increasing revenue with its attendant increase in costs. However, rather than investigating the complex asymmetric relationship between procurement practices and firm performance (which this study advocates), past research engaged in a symmetric evaluation of the relationship between the phenomena. Accordingly, this study, using complexity theory, employs fsQCA and NCA on a sample of 150 respondents from private universities in Ghana to (a) identify different combinations of procurement practices, namely procurement planning, supplier partnership, contract management, and compliance, that lead to firm performance and (b) explore the necessity of these procurement practices (in kind and degree) for firm performance. Whereas the findings from fsQCA reveal three distinct combinations of procurement practices for high firm performance and further suggest that none of the procurement practices was necessary for firm performance, the NCA results suggest that two out of the four procurement practices investigated are necessary for firm performance and hence must be present in the causal recipes produced by fsQCA to guarantee that they lead to firm performance. The study offers pathways to firm performance through procurement practices and demonstrates how to complement fsQCA with NCA to ensure that causal recipes produced by fsQCA can produce the outcome.

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In this contribution, the Schemper-Henderson measure of explained variation for survival outcomes is extended to accommodate competing events (CEs) in addition to events of interest. The extension is achieved by moving from the unconditional and conditional survival functions of the original measure to unconditional and conditional cumulative incidence functions, the latter obtained, for example, from Fine and Gray models. In the absence of CEs, the original measure is obtained as a special case. We define explained variation on the population level and provide two different types of estimates. Recently, the authors have achieved a multiplicative decomposition of explained variation into degrees of necessity and degrees of sufficiency. These measures are also extended to the case of competing risks survival data. A SAS macro and an R function are provided to facilitate application. Interesting empirical properties of the measures are explored on the population level and by an extensive simulation study. Advantages of the approach are exemplified by an Austrian study of breast cancer with a high proportion of CEs.

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Resilience in service operation is essential for minimising service failures. Based on a necessity logic, this study sought to examine whether innovation, transparency, flexibility, collaboration and agility are necessary antecedents of resilience in service operations of hotels, and to determine the minimum level of each capability required for optimal level of resilience. A survey of managers of Star-rated hotels in the Greater Accra region of Ghana was conducted. The conditions (agility, transparency, innovation, collaboration and flexibility) and outcome (resilience) variables were operationalised and modelled as necessary determinants of service resilience. Structured questionnaires were administered, from which 167 validated responses was analysed with necessary condition analysis (NCA) package in R. NCA explores necessity relationships both 'in kind' and 'in degree' to identify underlying capabilities and the corresponding levels that must be developed and sustained. The study found innovation, transparency, collaboration, flexibility, and agility to be necessary determinants of service resilience. However, from lower to medium levels of resilience (30%-80%), transparency, collaboration, flexibility, and agility are identified as bottlenecks. Innovation becomes important when higher resilience goals are sought. From a managerial standpoint, this study provides insight into critical antecedents of resilience within service operations. In addition, the study sheds light on the degree of resource allocation appropriate for different levels of firm resilience.

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ObjectiveTo adapt to the dynamic multidimensional changes in traditional Chinese medicine （TCM） constitution， the paths of improving the quality of life through TCM constitution intervention are proposed from a configuration perspective. MethodsFive hundred and nine subjects were included in the study， all of whom voluntarily participated in and completed TCM constitution interventions， and completed 60-item Constitution in Chinese Medicine Questionnaire （CCMQ） （including 9 subscales： neutral type， qi deficient type， yang deficient type， yin deficient type， phlegm-dampness type， damp-heat type， blood stasis type， qi stagnation type， and special constitution type） to evaluate the TCM constitution， and the Short Form Health Questionnaire （SF-36） in Chinese version （including overall， physical component scores （PCS）， and mental component scores （MCS）） before and 6 months after the intervention to evaluate the quality of life. The necessary condition analysis （NCA） and fuzzy set qualitative comparative analysis （fsQCA） were used to explore the necessary conditions and advantage configurations for improving the overall， physical and mental quality of life of TCM constitution dimension changes. ResultsThe results of NCA showed that the changes in the scores of neutral type and qi stagnation type can serve as necessary conditions for improving the SF-36 overall quality of life scores （P＜0.05）. The changes in the scores of neutral type and blood stasis type can serve as necessary conditions for improving PCS scores. The changes in scores of the other 7 types can serve as necessary conditions for improving MCS scores. The fsQCA results showed that the necessary consistency of a change in a particular physical subscale score alone did not reach to a necessary condition for a change in SF-36 total score， PCS score， and MCS score （consistency ＜ 0.9 for all）， so the results of NCA were considered. The results of configuration analysis found that 6 configurations were included in increasing the overall quality of life scores and PCS scores， and 7 in increasing the MCS scores， with overall consistency ＞ 0.7. Improving the overall quality of life scores required a focus on lowering the qi deficient type scores， or reducing the yang deficient type scores while increasing the neutral type scores； reducing the scores of phlegm-dampness type and qi stagnation type is beneficial for improving the PCS scores； increasing the neutral type scores and reducing the scores of deficient type， yang deficient type， phlegm-dampness type， and stagnation type is beneficial for increasing MCS scores. ConclusionTCM constitution interventions can improve physical， mental and overall quality of life， and its improvement in the mental domain of quality of life is closely related to the paths of overall quality of life. In order to improve the quality of life， in addition to increasing the tendency of the neutral type， more focused and targeted measures should also be taken to decrease the degrees of qi deficient type， yang deficient type， qi stagnation type， and phlegm-dampness type.

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Businesses operate in an increasingly dynamic environment requiring that they strike a delicate, healthy, and equitable balance among the interests of their numerous stakeholders. This study examined how employee orientation and shareholder orientation influence supply chain orientation and how employee orientation, shareholder orientation, and supply chain orientation influence firm performance. The study used a sample of 265 respondents and applied both linear and triangular data analysis techniques in Partial-Least Squares Structural Equation Modelling (PLS-SEM) and Necessary Condition Analysis (NCA) to examine how employee orientation, shareholder orientation, and supply chain orientation influence the performance of oil marketing companies (OMCs) in Ghana. The PLS-SEM results revealed that employee orientation and shareholder orientation are significant predictors of supply chain orientation. The NCA results revealed that employee orientation is the most important for supply chain orientation. Also, from the PLS-SEM results, employee orientation and supply chain orientation predict firm performance, but shareholder orientation does not. However, the NCA results suggest that all three orientations are necessary for firm performance and highlight supply chain orientation as the most important for firm performance. This study is the first to examine how employee orientation and shareholder orientation influence supply chain orientation, besides how the three orientations influence firm performance from both sufficiency and necessity theory perspectives. The study also uncovers supply chain orientation as an underlying process through which employee and shareholder orientation enhances firm performance.

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This quantitative study investigates the relationships of green human resource management (GHRM) practices (e.g., green training and involvement, green recruitment, green performance management and compensation, and green transformational leadership) on green organizational culture and pro-environmental behavior, and the moderating role of green social capital and green values. This study adopts a cross-sectional design and collects quantitative data from 232 respondents working in top-to middle-level managerial positions in medium and large enterprises using a questionnaire survey after obtaining a list of companies from the Securities and Exchange Commission of Lahore, Rawalpindi, and Multan in Pakistan, applying the snowball sampling technique. A combined approach including partial least squares structural equation modeling and necessary condition analysis is employed to unravel the underlying mechanism between GHRM practices, green organizational culture, and pro-environmental behavior using Smart PLS version 4. The findings reveal that green transformational leadership (ß = 0.267, p < 0.01), green performance management and compensation (ß = 0.412, p < 0.01), green training and involvement (ß = 0.226, p < 0.01) have a significant positive connection with green organizational culture. Moreover, green social capital (ß = 0.206, p < 0.01), green values (ß = 0.460, p < 0.01), and green organizational culture (ß = 0.143, p < 0.05) have a significant influence on workplace pro-environmental behavior. The study did not discover any moderating influence of green values and GS on the relationship between green organizational culture and pro-environmental behavior. Nevertheless, it did identify a mediating effect of green organizational culture in the connections between green recruitment, green training & involvement, green performance management & compensation, green transformational leadership, and pro-environmental behavior. The original contribution of this study includes offering in-depth insights into the relationship between GHRM practices and pro-environmental behavior through an integrated framework combining the GHRM framework, ability motivation opportunity (AMO) theory, and norm activation model to the extant literature. With its empirical investigation, this constitutes a pioneering study in the field of GHRM that offers numerous practical implications with the robust result obtained using sufficiency logic tests applying necessary condition analysis. Organizations should recruit employees with green values and give them training, and performance and compensation benefits to ensure green transformational leadership and enhance pro-environmental behavior in the organization.

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This research investigates the factors influencing carbon emission intensity in 94 countries during 2018 using two qualitative methods: necessary condition analysis (NCA) and fuzzy-set qualitative comparative analysis (fsQCA). The study covers variables related to economics, human geography, energy, and institutions, showing significant variations among them. The NCA model identifies economic complexity and fossil energy consumption as necessary conditions for high-carbon emission intensity. On the other hand, the fsQCA model reveals sufficient conditions for both high- and low-carbon emission intensity, presenting different causal combinations of variables. For high-carbon emission intensity, nine causal solutions are identified, emphasizing the roles of economic growth, urbanization, fossil energy consumption, and institutional quality. Reducing carbon emission intensity requires addressing economic complexity and reducing reliance on fossil energy consumption. Policymakers should focus on sustainable economic development, environmentally friendly urbanization, and transitioning to renewable energy sources. This research's originality lies in its qualitative approach, going beyond traditional regression methods to explore necessary and sufficient conditions for carbon emission intensity. It offers valuable insights into the complex interplay of variables, providing multiple causal configurations for both high- and low-carbon emission intensity.

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This study investigates the effect of green human resource management practices on green competitive advantage and the mediating role of competitive advantage between the green human resource management practices and green ambidexterity. This study also examined the effect of green competitive advantage on green ambidexterity and the moderating effect of firm size on green competitive advantage and ambidexterity. The results reveal that green recruitment and green training and involvement are not sufficient, but they are necessary for any outcome level of green competitive advantage. The other three constructs (green performance management and compensation, green intellectual capital, and green transformational leadership) are sufficient and necessary; however, green performance management and compensation is necessary only at an outcome level of more than or equal to 60%. The findings revealed that the mediating effect of green competitive advantage is significant only between three constructs (green performance management and compensation, green intellectual capital, and green transformational leadership) and green ambidexterity. The results also indicate that a green competitive advantage has a significant positive effect on green ambidexterity. Exploring the necessary and sufficient factors using a combination of partial least squares structural equation modeling and necessary condition analysis provides valuable guidance for practitioners to optimize firm outcomes.

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As technology has become indispensable in consumers' daily life and economic growth, understanding how and why consumers decide to accept and use a new technology has become essential to both academic researchers and practice. This article provides a detailed dataset based on a questionnaire that utilizes an extended technology acceptance model (TAM), incorporating the theory of consumer values and the innovation diffusion theory. Data collection was done with an online survey among French consumers, resulting in a sample size of 174. The dataset contains measures on various consumer attitudes and perceptions (e.g., consumption values) that influence intention and behaviors (adoption intention and technology use). This article supplements a published research article by Richter, Schubring, Hauff, Ringle and Sarstedt [1] which provides a detailed guide on how to combine partial least squares structural equation modeling (PLS-SEM) with necessary condition analysis (NCA) and a related illustration in a standard software published by Richter, Hauff, Ringle, Sarstedt, Kolev and Schubring [2].

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Objectives: Initially the stated goal of Necessary Condition Analysis (NCA) was to help identify conditions that are necessary but not sufficient for some outcome. However, later the developers of the test asserted that the test is for identifying if the association between two variables is characterized by some indeterminate type of non-randomness. The objective of the present study was to assess the ability of NCA to achieve its originally as well as its more newly stated objective. Furthermore, the performance of NCA was compared with the performance of ordinary linear regression analysis. Methods: Data simulating various deviations from randomness as well as empirical data on grit, depression, and anxiety in the 1997 National Longitudinal Survey of Youth (NLSY97) were analyzed with NCA and with linear regression. Results: For its initially stated objective, NCA displayed low specificity. For its more newly stated objective, NCA exhibited low sensitivity. Ordinary linear regression analysis was better than NCA at identifying non-random associations, especially negative associations. Conclusions: There does not appear to exist any convincing reasons to use the significance test in NCA instead of ordinary linear regression analysis. There appears to be confusion about how results from NCA should be interpreted, maybe even among the developers of the test.

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Recycling waste is important as it can help to reduce environmental pollution caused by "waste siege". Source classification is an important part of the municipal solid waste (MSW) sorting process. The factors that prompt residents to participate in waste sorting have been debated by scholars in recent years; however, there are not many papers that focus on the complex relationships between them. This study reviewed the literature that concerns residents' participation in waste sorting, and it summarized the external factors that might influence residents' participation. Then, we focused on 25 pilot cities in China, and we analyzed the configuration impact of external factors on residents' participation using a necessary condition analysis (NCA) and a fuzzy-set qualitative comparative analysis (fsQCA). We found no consistency between variables, nor was there one single condition that caused residents to participate in waste sorting. There are two main methods (environment-driven and resource-driven) that can help achieve a high participation rate, and three methods that can cause a low participation rate. This study provides suggestions for the implementation of waste sorting in other cities in China, as well as developing countries, with an emphasis on the importance of public participation.

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It is important to investigate how to achieve carbon unlocking in the transport sector, especially in transport infrastructure, in order to contribute to the achievement of carbon neutrality targets and the 2030 Sustainable Development Goals. This study aims to investigate the necessary and sufficient conditions to achieve carbon unlocking in transport infrastructure. To achieve this, a combination of partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA) methods have been used to examine whether there are unidentified necessity factors beyond the currently recognized 'technology-in-institution' (TIC) lock-in. This study also explores how the carbon unlocking of transport infrastructure can be achieved through the unlocking of relevant factors. The study includes 366 points from a subjective questionnaire from the government, transport infrastructure researchers, and relevant businesspeople. We found that, at the adequacy level, achieving institutional and technological unlocking is sufficient and economic factors have little impact on transport infrastructure (0.06), and that institutional and technical factors have a large impact on carbon unlocking (0.453, 0.280); however, from the necessary point of view, carbon unlocking at the economic level is necessary to achieve the goal of a medium to high level of carbon unlocking. To achieve carbon unlocking at this level (over 50%), a combination of technological, institutional, and economic factors is required. To achieve full carbon unlocking, the technology, system, and economy need to be at least 0.533, 0.791, and 0.63 unlocked. Therefore, we can conclude that by using the joint analysis of PLS-SEM and NCA, we have achieved an extension of the traditional TIC and identified sufficient and necessary conditions to achieve a medium to high degree of carbon unlocking.

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Most leadership studies primarily focus on formal leaders, often overlooking the influence of leaders within the team. While prior research has shown that peer leaders can have a beneficial impact on various team outcomes, it is yet unclear which peer leadership behaviors precisely foster a supportive and sustainable work environment. Building upon the recent identification of 10 peer leadership roles and 37 underlying functions, the current study aims to investigate the relationships between these peer leadership roles and functions and key outcomes (i.e., job satisfaction, team cohesion, team effectiveness, and OCB). A total of 31 organizational teams, comprising 182 employees from diverse sectors, participated in a quantitative survey. Employing multilevel modeling analysis, the findings demonstrated that each leadership role and nearly every function predicted at least one outcome, highlighting their significance within organizational teams. Additionally, Necessary Condition Analysis revealed that specific roles and functions were necessary for generating one or more outcomes. Finally, we found that most of the significant relationships remained consistent across teams, regardless of their size, tenure, or level of team identification. These findings refine our understanding of shared leadership and how peer leaders can create a sustainable workplace by fostering employee well-being and productivity in organizational teams.

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There has been some controversy about the relationship between wisdom and constructs of the well-being complex. Some wisdom researchers argue that the ability to maintain a high level of well-being, even in the face of very negative experiences, is a core characteristic of wisdom. Other researchers argue that the willingness of wise people to reflect on the darker sides of life might jeopardize well-being. Studies mostly found moderate positive correlations of well-being with self-report wisdom measures and negative, zero, or low positive correlations with open-ended measures of wisdom. This paper tests the hypothesis that the relationship between wisdom and well-being is triangular rather than linear, with highly wise people being high in well-being, but people high in well-being not necessarily being highly wise. A sample of 155 participants (age 23 to 90 years) completed four wisdom measures and three measures from the well-being complex. We analyzed both linear relationships (using correlations) and triangular relationships (using Necessary Condition Analysis). Correlations of well-being with open-ended measures of wisdom were mostly insignificant; correlations with self-report measures of wisdom were mostly significant. However, scatterplots showed the expected triangular relationships and Necessary Condition Analysis indicated medium to large effect sizes for both open-ended and self-report wisdom measures. In sum, our findings show that even if wise individuals think more deeply about difficult aspects of the human existence, they are still able to maintain high levels of well-being.

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Even though classic effect size measures (e.g., Pearson's r, Cohen's d) are widely applied in social sciences, the threshold used to interpret them is somewhat arbitrary. This study proposes necessary condition analysis (NCA) to complement traditional methods. We explain NCA in light of the current limitations of classical techniques, highlighting the advantages in terms of interpretation and translation into practical terms and recognizing its weaknesses. To do so, we provide an example by testing the link between three independent variables with a relevant outcome in a sample of 235 subjects. The traditional Pearson's coefficient was obtained, and NCA was used to test if any of the predictors were necessary but not sufficient conditions. Our study also obtains outcome and condition inefficiency as well as NCA bottlenecks. Comparison and interpretation of the traditional and NCA results were made considering recommendations. We suggest that NCA can complement correlation analyses by adding valuable and applicable information, such as if a variable is needed to achieve a certain outcome level and to what degree.

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BACKGROUND: After conducting necessary condition analysis (NCA), researchers have concluded that a certain, not too low, level of well-being is necessary but not sufficient for a high level of resilience. However, as acknowledged by the developers of the test, NCA only evaluates if the association between two variables is characterized by some unspecified type of non-randomness and not conditions of necessity. METHOD: Earlier reported data on the association between well-being and resilience among Filipino adults (N = 533) in COVID-19 quarantine were re-analyzed with an extended version of NCA. RESULTS: Analyses indicated a significant necessity effect of resilience on overall well-being, which is not logically compatible with well-being being necessary but not sufficient for resilience. Analyses with an extended version of NCA suggested that the association between overall well-being and resilience was characterized by equal degrees of necessity and sufficiency. CONCLUSIONS: The original version of NCA is only capable of evaluating if the association between two variables is characterized by some unspecified type of non-randomness. The extended version of NCA allows researchers to draw more specific conclusions.

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Introduction: COVID-19 has altered human cognition and changed the cultural values of society. However, there has not been much debate among scholars about whether these above changes have led to an increase in pro-environmental behavior (PEB) of Chinese consumers. Methods: A comprehensive model was developed based on affective event theory. An online questionnaire was distributed, and 501 usable questionnaires were collected. In addition, two complementary approaches were employed: partial least squares structural equation modeling (PLS-SEM) and necessary condition analysis (NCA). Results: The PLS-SEM results showed that COVID-19 risk perception, nostalgia, the awe of nature, and attitude were found to have a positive effect on PEB; and the moderating effect of power distance belief (PD) between nostalgia (NO), attitude (AT) and PEB was confirmed. According to the NCA results, AT and NO are necessary conditions for the PEB of consumers. Discussion: This study provides deeper insight into the understanding of consumers' pro-environmental behavior in the context of COVID-19 through the combined use of PLS-SEM and NCA.

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The frequent practice of overall fit evaluation for latent variable models in educational and behavioral research is reconsidered. It is argued that since overall plausibility does not imply local plausibility and is only necessary for the latter, local misfit should be considered a sufficient condition for model rejection, even in the case of omnibus model tenability. The argument is exemplified with a comparison of the widely used one-parameter and two-parameter logistic models. A theoretically and practically relevant setting illustrates how discounting local fit and concentrating instead on overall model fit may lead to incorrect model selection, even if a popular information criterion is also employed. The article concludes with the recommendation for routine examination of particular parameter constraints within latent variable models as part of their fit evaluation.

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The purpose of this paper is to extend to ordinal and nominal outcomes the measures of degree of necessity and of sufficiency defined by the authors for dichotomous and survival outcomes in a previous paper. A cause, represented by certain values of prognostic factors, is considered necessary for an event if, without the cause, the event cannot develop. It is considered sufficient for an event if the event is unavoidable in the presence of the cause. The degrees of necessity and sufficiency, ranging from zero to one, are simple, intuitive functions of unconditional and conditional probabilities of an event such as disease or death. These probabilities often will be derived from logistic regression models; the measures, however, do not require any particular model. In addition, we study in detail the relationship between the proposed measures and the related explained variation summary for dichotomous outcomes, which are the common root for the developments for ordinal, nominal, and survival outcomes. We introduce and analyze the Austrian covid-19 data, with the aim of quantifying effects of age and other potentially prognostic factors on covid-19 mortality. This is achieved by standard regression methods but also in terms of the newly proposed measures. It is shown how they complement the toolbox of prognostic factor studies, in particular when comparing the importance of prognostic factors of different types. While the full model's degree of necessity is extremely high (0.933), its low degree of sufficiency (0.179) is responsible for the low proportion of explained variation (0.193).

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Nation-wide community quarantines and social distancing are part of the new normal because of the global COVID-19 pandemic. Since extensive and prolonged lockdowns are relatively novel experiences, not much is known about the well-being of individuals in such extreme situations. This research effort investigated the relationship between well-being elements and resiliency of 533 Filipino adults who were placed under the nationwide enhanced community quarantine (ECQ) during the COVID-19 pandemic. Participants comprised of 376 females (70.56%) and 157 males (29.45%). The median and mode ages of the participants is 23 years, while 25 is the mean age. PERMA Profiler was used to measure participants' well-being elements, while Connor-Davidson Resiliency Scale-10 (CD-RISC-10) was used to measure their resiliency. Collected data were analyzed using the regression model and necessary condition analysis. This study corroborated that all the five pillars of well-being are significant positive correlates of resiliency (p < 0.00) in quarantined adults. The results shown accomplishment (ß = 0.447, p < 0.01) positively predicts resiliency, while negative emotions (ß = -0.171, p < 0.00) negatively predict resiliency. Lastly, the five pillars of well-being are necessary-but-not-sufficient conditions (ceiling envelopment with free disposal hull, CE-FDH p < 0.00) of resiliency. Our results cast a new light on well-being elements as constraints rather than enablers of resiliency. This novel result shows that optimum resiliency is only possible when all the five pillars of well-being are taken care of and when a person is at least minimally contented with their physical health. The present findings underscore the importance of a holistic as against an atomistic approach to maintaining good mental health, which suggests that deficiencies in certain areas of well-being may not be fully addressed by overcompensating on other areas, as all five pillars of well-being are necessary-but-not-sufficient conditions of resiliency. The study ends with the recommendation for the use of necessary condition analysis to study both classical and novel psychological research problems.