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This commentary discusses the paper by Reardon et al. (2024; this issue) entitled, “Overcoming implementation challenges through using a train-the-trainer approach to teach numeracy in a special school setting.” This commentary outlines the necessary contribution this paper adds to the substantive area of research it is couched within while also identifying potential areas of future research to expand the understanding of this phenomenon and its impact upon practice.

What is fidelity of implementation, how do we measure it and how does it impact our interpretations of experimental findings? This commentary focuses specifically on the nebulous construct of fidelity in experimental studies and how this impacts experimental findings’ internal and external validity.

Although fidelity is frequently referenced as an important aspect to consider, the measurement of the construct has been critiqued in experimental studies. To understand if an intervention was “implemented as intended,” the core dimensions of the intervention must be considered in the measurement process, as well as potential confounding variables.

With an increased need for experimental work to inform what works, for whom and under what conditions, there becomes a need to better investigate the implementation of the intervention in these contexts – thus, fidelity must be reconceptualized. This commentary provides an overview of this dilemma with potential ideas to investigate moving forward.

Algorithmic and computational thinking are necessary skills for designers in an increasingly digital world. Parametric design, a method to construct designs based on algorithmic logic and rules, has become widely used in architecture practice and incorporated in the curricula of architecture schools. However, there are few studies proposing strategies for teaching parametric design into architecture students, tackling software literacy while promoting the development of algorithmic thinking.

A descriptive study and a prescriptive study are conducted. The descriptive study reviews the literature on parametric design education. The prescriptive study is centered on proposing the incomplete recipe as instructional material and a new approach to teaching parametric design.

The literature on parametric design education has mostly focused on curricular discussions, descriptions of case studies or studio-long approaches; day-to-day instructional methods, however, are rarely discussed. A pedagogical strategy to teach parametric design is introduced: the incomplete recipe. The instructional method proposed provides students with incomplete recipes for parametric scripts that are increasingly pared down as the students become expert users.

The article contributes to the existing literature by proposing the incomplete recipe as a strategy for teaching parametric design. The recipe as a pedagogical tool provides a means for both software skill acquisition and the development of algorithmic thinking.

Technostress reduces employees' work performance and increases their turnover intentions, such that technostress harms organizations' success. This paper investigates how the digital mindset of employees, reflecting their cognitive filter while using digital technologies, influences reactions to techno-stressors.

In this quantitative study, the authors conducted a survey among 151 employees who regularly use digital technologies and encounter various techno-stressors in their daily work. To build this research model and evaluate the influence of employees’ digital mindset on technostress, the authors followed arguments from the transactional model of stress. The authors evaluated our research model using the covariance-based structural equation model.

The study findings reveal that employees’ digital mindset influences technostress. Employees with high levels of digital mindset react with less adverse effects on perceived techno-stressors. Further, the authors find that employees with high levels of digital mindset perform well and are satisfied with their job. The authors contribute to technostress research by revealing that digital mindset buffers the adverse effects of techno-stressors. The authors also contribute to research on digital mindset by showing that it influences psychological and behavioral reactions to techno-stressors.

This study develops and empirically tests an integrated model of technostress to explain how digital mindset mitigates technostress. The study findings outline relevant research avenues for studies investigating employees’ characteristics and technostress.

The aim of the present work is to develop a method to find the preventive measures for COVID-19 and their priorities. These preventive measures are prioritized according to the expert opinion.

An integrated method using the Kano model and Fuzzy-AHP is used to achieve the study objectives. First, the preventive measures are identified according to the expert. Next, the Kano model is used to determine the different Kano categories for remedial activities that are identified by the World Health Organization (WHO) and other medical authorities. Finally, Fuzzy-AHP is applied to determine the weights of these activities and assign the priorities according to their impact.

It is observed that the activity Avoid Travelling is the most important classification or category with the highest weight as compared to the other activities and sub-activities. It is also noticed that the category packed food items get the lowest weight and is the least important classification or category. In this work, two different approaches, designed for different purposes, provide similar results and verify each other.

Research contributing to the classification and prioritization of preventive activities using Kano and Fuzzy-AHP methods is not available. In the critical time of COVID-19, when governments are obliged to deal with many infected patients and a high number of deaths, one can focus on different preventive activities according to their classification, weights and ranks.