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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 the context of rapid technological progress, this study investigates the factors that improve the academic performance of Saudi Arabian university students when they use ChatGPT. Using the technology-to-performance chain theory as a framework, this study identifies the variables that may affect the students' academic performance, thereby contributing to the discourse on the use of technology in education.

A survey is conducted on 257 respondents, and an online questionnaire is used to collect the data. Analysis of Moment Structures (AMOS) is employed to analyse the structural model to determine the direct connections between the different elements.

Findings reveal that task characteristics, technology characteristics and individual characteristics can significantly impact task-technology fit. Furthermore, task-technology fit can influence the utilisation of ChatGPT and students' academic performance. In addition, utilisation can significantly impact students' academic performance. Students are likely to utilise ChatGPT efficiently and demonstrate improved academic performance when they believe that the technology is a good fit for their tasks.

This study’s shortcoming is its exclusive focus on a single public university in Saudi Arabia, which limits its generalisability. Comparative studies among multiple universities in Saudi Arabia and in other Gulf nations should be conducted to enhance the generalisability of the results. In addition, diversifying the participants by including students from various universities and exploring the moderating variables would deepen our understanding of the utilisation of ChatGPT by students.

The practical implications of this study include the existence of a positive relationship between task characteristics and task-technology fit, which can guide organisations in aligning ChatGPT with specific activities for enhanced efficacy and workflow integration. In addition, understanding the association between technology characteristics and task-technology fit can help in selecting suitable technologies that will encourage user adoption and improve academic outcomes. Furthermore, the recognition of the impact of individual characteristics on task-technology fit and their utilisation can inform tailored support and training programmes to enhance user acceptance and utilisation of ChatGPT, particularly in educational settings such as those in Saudi Arabia, which will ultimately improve students’ academic performance.

This study’s focus on ChatGPT and how it affects the academic performance of Saudi Arabian university students distinguishes it from previous studies. This study provides insightful information on technology adoption in educational settings and contributes to our understanding of the factors that can impact academic performance through ChatGPT adoption by utilising technology-to-performance chain theory. Moreover, this study’s examination of task characteristics, technology characteristics and individual characteristics can significantly enrich discussions on optimal technology integration for educational objectives. This contribution is relevant in dynamic contexts, such as the rapidly evolving technological environment of Saudi Arabia.

Project information is widely and thoroughly exchanged within construction projects. However, the risk management process exhibits deficiencies in coordination and visibility, particularly in developing countries. Practitioners in developing countries often engage in Project Risk Management (PRiM) using practices that inadvertently hinder project success, frequently resulting in suboptimal outcomes. Therefore, this research explores practices within Project Integration Management (PIM) and Project Communication Management (PCmM) that could enhance PRiM and improve managerial proficiency to achieve project success in developing countries.

The PIM, PCmM and PRiM processes were explored from the literature; the data was gathered initially by close-ended interviews conducted with a panel of twelve experts followed by a well-structured questionnaire. Structural Equation Modeling (SEM) was employed via AMOS to analyze the data and construct a model representing the intricate relationships between the processes. Additionally, the validity and reliability of the method were inspected.

The data analysis confirmed that PIM and PCmM were correlated according to contractors and consultants in the Jordanian construction industry. Furthermore, both have a positive influence on the PRiM. In addition, a 13-step process was developed to apply extra processes and practices to ensure better implementation of the PRiM in developing countries.

The literature highlights that integration and communication management influence project performance. However, there is a lack of research utilizing practices from these knowledge areas to achieve better project risk management implementation. This research highlights two of the most underrated knowledge areas in project management. Therefore, a framework was devised, comprising processes that practitioners should take into account during the planning phase leading to efficient PRiM to enhance their managerial proficiency.

Effective teamwork is crucial for patient safety in healthcare. The TeamSTEPPS Teamwork Perceptions Questionnaire (T-TPQ) is a widely used tool for assessing teamwork perceptions. The T-TPQ has been adapted and validated for hospital setting use in several countries. This study aimed to translate and validate the T-TPQ into French for use among Tunisian healthcare professionals, enhancing teamwork assessment and patient safety initiatives.

A rigorous process ensured cultural and linguistic adaptation of the T-TPQ, including back-translation, expert panel review, and pilot testing. 459 healthcare professionals from four hospitals in Kairouan, Tunisia participated. Confirmatory factor analysis (CFA) compared the original five-factor structure with a revised structure based on exploratory factor analysis (EFA).

Both CFA models demonstrated good fit, with no significant difference between them (∆χ² = 22.51, p = 0.79). The original five-factor structure was retained due to its established theoretical foundation. The French T-TPQ exhibited strong internal consistency (α = 0.9). Two-way Random ICCs indicated fair to good test-retest reliability for all the five dimensions (0.633–0.848).

Several limitations should be acknowledged. The use of a questionnaire as a data collection tool is the source of a reporting bias, for fear of being identified or for reasons of “social desirability”. Nevertheless, this social desirability was minimal, as Baker et al. (2010) took steps to mitigate this during the instrument's development. Additionally, for assessing attitudes and perceptions, self-reported measures are deemed more effective, whereas objective measures are advocated for behavioral assessments. Furthermore, the participants were informed of the absence of good or bad answers, the importance of answering as closely as possible, and the confidentiality. Moreover, considering the data collection period, the COVID- 19 pandemic and its potential impact on recruitment, data collection, and participant responses. Although the sample size of 459 met the recommended criteria for conducting confirmatory factor analysis, as suggested by Bentler and Chou (1987) and (Floyd and Widaman, 1995), the COVID-19 pandemic presented challenges in recruitment. The increased workload and stress on healthcare professionals, coupled with staff redeployment and research restrictions within hospitals and care units, likely hindered achieving an even larger sample size. These circumstances also necessitated adjustments to data collection methods to ensure safety and adherence to pandemic protocols. This involved incorporating online surveys option with paper-based questionnaires and implementing stricter hygiene measures during in person data collection. Furthermore, the pandemic impacted the teamwork perceptions as significantly redefined the healthcare environment, placing immense pressure on professionals due to surging patient volumes, staff shortages, and the emotional burden of caring for critically ill individuals. This heightened stress and workload likely influenced teamwork dynamics, potentially fostering both positive adaptations, such as increased cohesion and support, as well as negative consequences like communication breakdowns and decreased morale (Terregino et al., 2023).

We outline significant practical implications for leaders in health care for improving teamwork and patient safety. Or, healthcare leaders can significantly enhance teamwork and patient safety by incorporating the validated French T-TPQ into their improvement strategies. This reliable tool enables the assessment of staff perceptions regarding teamwork strengths and weaknesses, specifically in areas like communication and leadership. By identifying these crucial areas, leaders can implement targeted training programs and interventions. In fact, the existing body of research consistently demonstrates the positive impact of team training interventions, on both teamwork processes and patient outcomes. These interventions have been shown to enhance teamwork skills (Baker et al., 2010; Thomas and Galla, 2013; Weaver et al., 2014). In areas such as communication, leadership, situation monitoring, and mutual support, leading to decreased mortality and morbidity rates (Weaver et al., 2014). Implementing team training programs fosters trust and collaboration around shared goals, contributing to a more effective and safer healthcare environment for both patients and professionals. Additionally, the culturally adapted T-TPQ not only benefits individual healthcare settings but also unlocks opportunities for broader research and collaboration on a global scale. By enabling cross-cultural comparisons and benchmarking, the T-TPQ can deepen our understanding of how teamwork dynamics vary across diverse healthcare environments and cultural contexts. This knowledge is invaluable for tailoring teamwork interventions and training programs to specific populations and settings, ensuring their effectiveness and cultural relevance. Moreover, integrating teamwork training into continuing professional development, interprofessional and medical education initiatives is crucial for cultivating collaborative competencies and building high-performing healthcare teams. Research has shown that interprofessional teamwork experiences significantly enhance collaborative competencies among nursing and medical students, emphasizing the importance of incorporating teamwork training early in healthcare education. This approach equips future healthcare professionals with the necessary skills to navigate complex team environments, ultimately improving patient care quality and mitigating workload issues that contribute to burnout (Simin et al., 2010; Ceylan, 2017; Fox et al., 2018).

The French version of the T-TPQ was semantically equivalent and culturally relevant with adequate test-retest reliability as compared to the English version, expanding its applicability and contributing to understanding teamwork perceptions in this context. The French T-TPQ offers a valuable tool for assessing teamwork, identifying areas for improvement, and implementing interventions to enhance teamwork and patient safety in Tunisia and potentially other French-speaking regions.

Project portfolio benefit (PPB) evaluation is crucial for project portfolio management decisions. However, PPB is complex in composition and affected by synergy and ambidexterity. Ignoring these characteristics can result in inaccurate assessments, impeding the management and optimization of benefit. Considering the above complexity of PPB evaluation, this study aims to propose a refined PPB evaluation model to provide decision support for organizations.

A back propagation neural network optimized via genetic algorithm and pruning algorithm (P-GA-BPNN) is constructed for PPB evaluation. First, the benefit evaluation criteria are established. Second, the inputs and expected outputs for model training and testing are determined. Then, based on the optimization of BPNN via genetic algorithm and pruning algorithm, a PPB evaluation model is constructed considering the impacts of ambidexterity and synergy on PPB. Finally, a numerical example was applied to validate the model.

The results indicate that the proposed model can be used for effective PPB evaluation. Moreover, it shows superiority in terms of MSE and fitting effect through extensive comparative experiments with BPNN, GA-BPNN, and SVM models. The robustness of the model is also demonstrated via data random disturbance experiment and 10-cross-validation. Therefore, the proposed model could serve as a valuable decision-making tool for PPB management.

This study extends prior research by integrating the impacts of synergy and ambidexterity on PPB when conducting PPB evaluation, which facilitates to manage and enhance PPB. Besides, the structural redundancy of existing assessment methods is solved through the dynamic optimization of the network structure via the pruning algorithm, enhancing the effectiveness of PPB decision-making tools.

This study aims to explore the determinants of digital technology adoption within micro, small and medium enterprises (MSMEs) operating in a developing economy. Grounded in the theoretical frameworks of fit-viability and task-technology fit models, this study investigates the impact of environmental fit, task-technology fit and viability on the intention to adopt digital technologies among MSMEs.

This study validates the theoretical model using structural equation modeling, using data collected from 280 experienced respondents employed in the MSMEs in India.

The results indicate that both functional and symbolic benefits positively impact managers’ intention to adopt digital technology. However, subgroup analysis reveals that in the case of service enterprises, only functional benefits have a positive influence on managers’ intention to adopt digital technology. In addition, the findings underscore the crucial role of viability in shaping the intention to adopt digital technologies among MSMEs. This study highlights how functional and symbolic benefits motivate digital technology adoption in MSMEs.

There is a dearth of empirical studies investigating the factors influencing the adoption of digital technology by MSME firms, especially within the context of developing economies and specifically within the MSME domain. This study contributes to the theoretical discussion surrounding digital technology adoption among MSMEs in India. Through empirical research, it expands on the fit-viability model and formulates a technology adoption model within the MSME context.

The construction industry is facing an enormous number of challenges due to continuous advancements in construction technologies and techniques. Hence, construction management theories have to confront critical newly issues concerning market globalization and construction innovations. The key factor to address these challenges is to ameliorate the competitive abilities of the competing construction firms. In this context, measuring competitiveness of construction firms is an efficacious approach to amplify their competitive growth and profitability. To this end, the purpose of this research paper is to design a three-tier multi-criteria decision making model for competitiveness assessment and benchmarking of construction companies, meanwhile tackling a wide range of essential factors and attributes that covers broad aspects of the present competitive market.

In the first tier, four new pillars (4P) of competitiveness assessment are introduced for construction firms, namely, organization performance, project performance, environment and client and innovation and development. These pillars are able to aid in construction firms’ management on both long and short term basis. Hence, 21 key competitive factors and eighty key competitive criteria are identified, incorporated and analyzed in this research study. The second tier encapsulates carrying out a questionnaire survey in the Canadian and Vietnamese market to garner two main sets of information. The first set of information incorporates responses of the pairwise comparisons between competitiveness factors and criteria. The second set involves gathering utility scores pertinent to each competitiveness criteria. The developed model then leverages the use of analytical hierarchy process to scrutinize the relative importance priorities of competitiveness factors and criteria. The third tier of the developed model encompasses the use of multi-attribute utility theory to compute competitiveness scores for construction companies through blending criteria’ relative importance weights alongside their respective utility functions. In addition, the third tier comprises conducting a sensitivity analysis to derive the most important criteria influencing the overall competitiveness of construction companies. The developed model is tested and validated using three case studies; one construction company from Canada and two construction companies from Vietnam.

Results demonstrated that the developed model has a potential to render a synthesized and methodical performance evaluation for the competitive ability of a given construction company. Furthermore, it was found that Vietnamese companies are more considerate towards pillars pertaining to environment and client while Canadian companies are more attentive towards innovation and development. The outcome of sensitivity analysis revealed that effectiveness of cost management highly affects the competitive ability of Vietnamese companies while effectiveness of cost management exhibits the most significant influence on the competitive of Canadian companies.

The developed model can benefit construction companies to understand their competitiveness in their market and diagnose their strengths and weaknesses. It is also can be useful in efficient utilization of their limited resources and development of sustainable and long-term strategic plans strategic plans, which consequently leads to maintaining better position in their dynamic business markets.

Literature review manifests that reported competitiveness assessment models and practices are not able to address present challenges, technologies and developments in construction market.

Economic evaluations often utilise individual-patient data (IPD) to calculate probabilities of events based on observed proportions. However, this approach is limited when interest is in the likelihood of extreme biomarker values that vary by observable characteristics such as blood glucose in gestational diabetes mellitus (GDM). Here, instead of directly calculating probabilities using the IPD, we utilised flexible parametric models that estimate the full conditional distribution, capturing the non-normal characteristics of biomarkers and enabling the derivation of tail probabilities for specific populations. In the case study, we used data from the Born in Bradford study (N = 10,353) to model two non-normally distributed GDM biomarkers (2-hours post-load and fasting glucose). First, we applied fully parametric maximum likelihood to estimate alternative flexible models and information criteria for model selection. We then integrated the chosen distributions in a probabilistic decision model that estimates the cost-effective diagnostic thresholds and the expected costs and quality-adjusted life years (QALYs) of the alternative strategies (‘Testing and Treating’, ‘Treat all’, ‘Do Nothing’). The model adopts the ‘payer’ perspective and expresses results in net monetary benefits (NMB). The log-logistic and Singh-Maddala distributions offered the optimal fit for the 2-hours post-load and fasting glucose biomarkers, respectively. At £13,000 per QALY, maximum NMB with ‘Test and Treat’ (−£330) was achieved for a diagnostic threshold of fasting glucose >6.6 mmol/L, 2-hours post-load glucose >9 mmol/L, identifying 2.9% of women as GDM positive. The case study demonstrated that fully parametric approaches can be implemented in healthcare modelling when interest lies in extreme biomarker values.

The pivotal aspect of aircraft assembly lies in precise measurement accuracy. While a solitary digital measuring tool suffices for analytical and small surfaces, it falls short for extensive synthetic surfaces like aircraft fuselage panels and wing spars. The purpose of this study is to develop a “combined measurement method” (CMM) that enhances measurement quality and expands the evaluative scope, addressing the limitations posed by singular digital devices in meeting measurement requirements across various aircraft components.

The study illustrated the utilization of the CMM by combining a laser tracker and a portable arm-measuring machine. This innovative approach is tailored to address the intricate nature and substantial dimensions of aircraft fuselage panels. The portable arm-measuring machine performs precise scans of panel components, while common points recorded by the laser tracker undergo coordinate conversion to reconstruct the fuselage panel’s shape. The research outlines the CMM’s measurement procedure and scrutinizes the data processing technique. Ultimately, the investigation yields a deviation vector matrix and chromatogram deviation distribution, pivotal in achieving enhanced measurement precision for the novel CMM device.

The use of CMM noticeably enhances fuselage panel assembly accuracy, concurrently reducing assembly time and enhancing efficiency compared to conventional measurement systems.

The research’s practical implication lies in revolutionizing aircraft assembly by mitigating accuracy issues through the innovative digital CMM for aircraft synthetic structure type product (aircraft fuselage panel). This ensures safer flights, reduces rework and enhances overall efficiency in the aerospace industry.

Introducing a new aircraft assembly accuracy compensation method through digital combined measurement, pioneering improved assembly precision. Also, it enhances aerospace assembly quality, safety and efficiency, offering innovative insights for optimized aviation manufacturing processes.

Numerous attempts at installing Six Sigma (SS) have faced challenges and fallen short of the desired success. Thus, it becomes vital to identify the critical factors and characteristics that play a pivotal role in achieving successful adoption. Research has highlighted that a considerable number of corporate Six Sigma initiatives, around 60%, fail primarily due to the improper incorporation of essential elements and flawed assumptions.

To validate the influence of CSFs on Six Sigma accomplishment, the study employed a research design combining exploratory and mixed-methods approaches. The analysis focused on 260 completed questionnaires, and statistical methods including SEM, EFA, and CFA were utilized for data analysis.

The study acknowledged four essential components of CSFs that are imperative for sustaining the success of Six Sigma (SS): (1) Competence of Belt System employees; (2) Project management skills; (3) Organizational economic capability; and (4) Leadership commitment and engagement. These factors were identified as significant contributors to the maintenance of Six Sigma’s success.

The practical implications of this research imply that institutions, practitioners, and researchers can utilize the four identified factors to foster the sustainable deployment of SS initiatives. By incorporating these factors, organizations can enhance the effectiveness and longevity of their Six Sigma practices.

The investigation’s originality lies in its contribution to assessing critical success factors in Six Sigma deployment within the European automobile industry, utilizing a mixed-methods research design supplemented by descriptive statistics.