Search results

Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.

Research serves to elucidate and tackle real-world issues (e.g. capitalizing opportunities and solving problems). Critical to research is the concept of validity, which gauges the extent to which research is adequate and appropriate in representing what it intends to measure and test. In this vein, this article aims to present a typology of validity to aid researchers in this endeavor.

Employing a synthesis approach informed by the 3Es of expertise, experience, and exposure, this article maintains a sharp focus on delineating the concept of validity and presenting its typology.

This article emphasizes the importance of validity and explains how and when different types of validity can be established. First and foremost, content validity and face validity are prerequisites assessed before data collection, whereas convergent validity and discriminant validity come into play during the evaluation of the measurement model post-data collection, while nomological validity and predictive validity are crucial in the evaluation of the structural model following the evaluation of the measurement model. Additionally, content, face, convergent and discriminant validity contribute to construct validity as they pertain to concept(s), while nomological and predictive validity contribute to criterion validity as they relate to relationship(s). Last but not least, content and face validity are established by humans, thereby contributing to the assessment of substantive significance, whereas convergent, discriminant, nomological and predictive validity are established by statistics, thereby contributing to the assessment of statistical significance.

This article contributes to a deeper understanding of validity’s multifaceted nature in research, providing a practical guide for its application across various research stages.

In response to the problem of insufficient traction/braking adhesion force caused by the existence of the third-body medium on the rail surface, this study aims to analyze the utilization of wheel-rail adhesion coefficient under different medium conditions and propose relevant measures for reasonable and optimized utilization of adhesion to ensure the traction/braking performance and operation safety of trains.

Based on the PLS-160 wheel-rail adhesion simulation test rig, the study investigates the variation patterns of maximum utilized adhesion characteristics on the rail surface under different conditions of small creepage and large slip. Through statistical analysis of multiple sets of experimental data, the statistical distribution patterns of maximum utilized adhesion on the rail surface are obtained, and a method for analyzing wheel-rail adhesion redundancy based on normal distribution is proposed. The study analyzes the utilization of traction/braking adhesion, as well as adhesion redundancy, for different medium under small creepage and large slip conditions. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived.

When the third-body medium exists on the rail surface, the train should adopt the low-level service braking to avoid the braking skidding by extending the braking distance. Compared with the current adhesion control strategy of small creepage, adopting appropriate strategies to control the train’s adhesion coefficient near the second peak point of the adhesion coefficient-slip ratio curve in large slip can effectively improve the traction/braking adhesion redundancy and the upper limit of adhesion utilization, thereby ensuring the traction/braking performance and operation safety of the train.

Most existing studies focus on the wheel-rail adhesion coefficient values and variation patterns under different medium conditions, without considering whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train. Therefore, there is a risk of traction overspeeding/braking skidding. This study analyzes whether the rail surface with different medium can provide sufficient traction/braking utilized adhesion coefficient for the train and whether there is redundancy. Based on these findings, relevant measures for the reasonable and optimized utilization of adhesion are derived to further ensure operation safety of the train.

In the context of the COVID-19 pandemic, this study investigates a variety of approaches to supply disruption risk management for achieving effective responses for resilience at the supply management subunit level (e.g. category of items). Drawing on the attention-based view of the firm, the authors model the attentional antecedents of supply resilience as (1) attentional perspectives and (2) attentional selection. Attentional perspectives focus on either supply risk sources or supply network recoverability, and both are hypothesised to have a direct positive association with supply resilience. Attentional selection is top down or bottom up when it comes to disruption detection, and these are hypothesised to moderate the association between disruption risk management perspectives and resilience.

Conducted at the early phases of the COVID-19 pandemic, this study employs a hierarchical regression analysis on a multicountry survey of 190 procurement professionals, each responding from the perspective of their own subunit area of supply responsibility.

Both attentional disruption risk management perspectives are needed to achieve supply resilience, and neither is superior in terms of achieving supply resilience. Both the efficiency of the top down and exposure to the unexpected with the bottom up are needed – to a balanced degree – for improved supply resilience.

The results encourage firms to purposefully develop their supply risk management practices, first, to include both perspectives and, second, to avoid biases in attentional selection for disruption detection. Ensuring a more balanced approach may allow firms to improve their supply resilience.

The results contribute to the understanding of the microfoundations that underpin firms' operational capabilities for supply risk and disruption management and possible attentional biases.

This study aims to examine the influence of Audit Committee (AC) composition on Firm Performance (FP) by measuring AC composition (ACC) with a composite score based on the varying effect of each composition-characteristic.

Partial Least Squares- Structural Equation Modeling (PLS-SEM) technique is used to weigh ACC characteristics. Based on 133 companies and covering five years from 2016 to 2020, the study analyses data after controlling endogeneity through the Gaussian Copula approach.

We find a significant positive influence of ACC on Firm Performance. Among the ACC characteristics, the absence of executive directors has the highest positive weight on ACC to influence FP, followed by AC size and Gender diversity. AC independence and members' accounting and financial expertise have no significant weight on its composition.

Apart from the theoretical contribution, the study reveals that each ACC characteristic has a varying effect on AC effectiveness to influence the FP that needs to be considered by regulators while framing regulations on ACC and by BOD while constituting AC for a company.

The study claims originality by being pioneering to reveal that AC composition, with a synergy of its disparate characteristics, positively impacts FP. It highlights that the absence of executive directors and gender diversity in AC (characteristics overlooked by the extant literature) significantly and positively influence FP. Methodologically, it introduces the use of the PLS-SEM algorithm to weigh the characteristics in governance studies. Further, these findings remain relevant amid recent Indian legal reforms, offering contemporary insights for policy consideration.

Partial least squares structural equation modeling (PLS-SEM) has become an established social sciences multivariate analysis technique. Since quality management researchers also increasingly using PLS-SEM, this growing interest calls for guidance.

Based on established guidelines for applying PLS-SEM and evaluating the results, this research reviews 107 articles applying the method and published in eight leading quality management journals.

The use of PLS-SEM in quality management often only draws on limited information and analysis results. The discipline would benefit from the method's more comprehensive use by following established guidelines. Specifically, the use of predictive model assessment and more advanced PLS-SEM analyses harbors the potential to provide more detailed findings and conclusions when applying the method.

This research provides first insights into PLS-SEM's use in quality management. Future research should identify the key areas and the core quality management models that best support the method's capabilities and researchers' goals.

The results of this analysis guide researchers who use the PLS-SEM method for their quality management studies.

This is the first article to systematically review the use of PLS-SEM in the quality management discipline.

Knowledge-intensive entrepreneurial firms (KIE) strongly rely on scientific and strategic research and development (R&D) capabilities to achieve higher performance levels. Hence, the purpose of this paper is to disentangle the effects of scientific capabilities and strategic R&D on KIE performance; and how the constituent elements of these dimensions can be configured to generate conditions for high performance.

The authors’ empirical setting involves companies that submitted projects to the Innovative Research in Small Businesses (PIPE) program in Brazil. The authors then run partial least square structural equation modeling to verify how scientific and strategic R&D capabilities influence the performance construct. Second, the authors apply fuzzy-set qualitative comparative analysis to identify configurations that are equifinal in terms of generating superior performance.

Findings indicate a strong association between scientific capabilities and KIE performance. The configurational approach outlines the existence of multiple paths to success, but human capital stands as a core condition throughout estimations.

The authors’ assessment has implications for how KIE firms are managed according to their organizational profiles and trajectories. Also, it advances the authors’ comprehension on how entrepreneurship policies can better target these distinct profiles.

The authors’ analysis provides new evidence on the inherent complexity behind the generation of high performance in KIE when addressing their portfolios of knowledge-related capabilities. More than that, the authors were able to identify the existence of heterogeneous profiles that can equally lead to higher levels of performance.

The purpose of this study was to investigate how and through which social-cognitive constructs, emotions influence healthy food shopping behaviors. Direct effects of those constructs, as well as indirect effects of consumer emotions are considered.

An altered version of the Social Cognitive Theory, including intention, socio-structural factors, outcome expectancies and self-efficacy with the addition of consumer emotions was analyzed using structural equation modeling. Data of 1,181 volunteers were collected in Germany in 2021 through an online survey.

Intention was the most important positive predictor of food choice, while socio-structural factors had the biggest impact on intentions. Those were mostly influenced by self-efficacy, which was strongly predicted by consumer emotions. Outcome expectancies did not influence the current model in any way. Consumer emotions did not directly influence intention, nor actual choice, however showed to be influencing those variables through indirect effects.

Marketers could benefit from these results by incorporating the current findings into existing marketing strategies through targeting a combination of social cognitive constructs, as well as consumer emotions to facilitate healthier food shopping behavior.

Affect has received increasing attention in regards to its impact on healthy eating behaviors in recent years. Less attention has been paid to the mechanisms through which emotions influence healthy nutrition behavior, specifically how consumer emotions influence healthy food shopping behavior.

This study aims to evaluate the influence of factors attributed to relationship attractiveness between supplier and customer, from the supplier’s perspective.

The empirical exercise was based on the use of multivariate data analysis with confirmatory factor analysis and a partial least squares approach to structural equation modeling.

The study resulted in a robust model, with a high explanatory factor for the latent variable relational attractiveness and commitment was the most influential factor, followed by expected value and interorganizational trust.

Some limitations of the study can be highlighted: conducting research in a single cooperative and with a single audience; choosing a short-cycle supply chain, which may make it difficult to generalize to other industries; the cross-section nature of data also hinders the analysis to understand how the association between variables of interest may vary over time.

The negative influence of interpersonal trust reinforces the importance of developing collaborative attitudes between parties, whereas the non-significant value for dependence (financial and volume dependence) shows this is not a risk factor for relational attractiveness in a supply chain. Such results provide evidence on how to develop relationship management between suppliers and customers that are part of the same supply chain.

It offers a new perspective for research in cooperatives, which still have no consensus on the motivating factors for members’ participation, including as evidence the results of the largest flower producing center in Latin America. The more a cooperative succeeds in increasing relational attractiveness, the greater the cooperative power and the ability to adapt to shocks and changes, which are fundamental factors for the success and longevity of the cooperative.

The research presents a model that is comprehensive enough to fit in different contexts and consider its specific characteristics. Additionally, this paper has added in-depth information on the relational attractiveness relationships in the context of a developing country.

Work is central to human life. It requires high mental and emotional involvement from organizational leaders, whose decisions have far-reaching impacts. Currently, industrial and technological societies struggle with growing problems of trust in leadership, mental health, feelings of emptiness and the search for relevant meaning, which highlights the importance of attending to spirituality at work. This study aims to fill two main gaps: the need for empirical research on spirituality in organizational leaders, and access to the meanings and experiences of leaders themselves.

Qualitative data from 25 Portuguese organizational leaders were collected through in-depth interviews. We followed a phenomenological approach to reveal participants' own meanings and experiences of spirituality. Content analysis was performed to identify emergent categories and a framework of spirituality development.

The results suggest the participant’s plural views and spirituality practices, which generate common achievements – Ways to Inner. Relations of leaders’ spirituality with their development and integration at work are also evidenced – Ways to Wisdom. A spirituality development framework is presented with aspects revealed in leaders’ ways, from deepening individual issues to the orientation for existential impacts.

This study proposes a framework of spirituality development based on empirical data, with the potential for adaptation to various professional contexts. Spirituality is approached as a human dimension in leaders, as whole beings and a decisive dimension of present organizational change towards a holistic work paradigm.