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As drones are rapidly transforming tasks such as mapping and surveying, safety inspection and progress monitoring, human operators continue to play a critical role in ensuring safe drone missions in compliance with safety regulations and standard operating procedures. Research shows that operator's stress and fatigue are leading causes of drone accidents. Building upon the authors’ past work, this study presents a systematic approach to predicting impending drone accidents using data that capture the drone operator's physiological state preceding the accident.

The authors collect physiological data from 25 participants in real-world and virtual reality flight experiments to design a feedforward neural network (FNN) with back propagation. Four time series signals, namely electrodermal activity (EDA), skin temperature (ST), electrocardiogram (ECG) and heart rate (HR), are selected, filtered for noise and used to extract 92 time- and frequency-domain features. The FNN is trained with data from a window of length t = 3…8 s to predict accidents in the next p = 3…8 s.

Analysis of model performance in all 36 combinations of analysis window (t) and prediction horizon (p) combinations reveals that the FNN trained with 8 s of physiological signal (i.e. t = 8) to predict drone accidents in the next 6 s (i.e. p = 6) achieved the highest F1-score of 0.81 and AP of 0.71 after feature selection and data balancing.

The safety and integrity of collaborative human–machine systems (e.g. remotely operated drones) rely on not only the attributes of the human operator or the machinery but also how one perceives the other and adopts to the evolving nature of the operational environment. This study is a first systematic attempt at objective prediction of potential drone accident events from operator's physiological data in (near-) real time. Findings will lay the foundation for creating automated intervention systems for drone operations, ultimately leading to safer jobsites.

In the highly competitive business landscape, manufacturing firms need to adopt an effective manufacturing strategy to attain a successful world-class manufacturing status. Over the past few decades, the lean manufacturing (LM) approach has gained recognition as one of the foremost strategies for enhancing performance. However, the implementation of LM poses significant challenges due to several barriers. The purpose of this paper is to investigate the primary barriers to lean implementation within the apparel industry.

This paper used an exploratory study approach, using a three-part structured questionnaire to assess the level of agreement on different lean barriers. The measurement of these barriers was conducted using a five-point Likert scale. Empirical data were collected from 177 apparel companies located in Bangladesh.

The findings of the research highlight that the primary obstacles to implementing LI include a lack of understanding of the lean manufacturing system (LMS), the manufacturing process, the company culture and resistance from employees.

This paper could potentially limit the generalizability of this research, as it exclusively examines a single manufacturing sector – the apparel industry.

This paper will help practitioners in finding solutions to resolve discrepancies between current manufacturing practices and the LMS.

This paper fulfills an identified need to examine the extent of lean adoption within the apparel industry of Bangladesh.

This study aims to identify the factors influencing cashless transactions toward digital payment systems using the extended UTAUT model in developing countries. This model was extended with perceived usefulness, perceived ease of use, facilitating conditions, perceived security/trust and social influence for assessing consumer behavior toward cashless transactions.

Using structural equation modeling (SEM), this study conducted a cross-sectional survey to collect data, providing a snapshot of the relationship between exogenous and endogenous variables.

The results of the study indicate that perceived usefulness, facilitating conditions, perceived trust/security and social influence have a significant influence on consumer intentions toward cashless transactions. Oppositely, leaving the perceived ease of use has no significant influence on consumer intentions toward the usage of cashless transactions.

The contribution of this study is to extend the UTAUT model for adopting cashless transactions in developing countries that will help government agencies, service providers and financial institutions design effective strategies in the future.

The improper disposal of potentially harmful products is a problem that affects both developed and emerging countries. Using the Values-Beliefs-Norms (VBN) theory, this research attempts to uncover the key differences and similarities between both contexts and to extend the theory to include trust-in-government (TIG) as a moderating variable.

The data used in this study were drawn from two samples: Mexicans and Americans by administering a paper and pencil survey. To test the conceptual model and to contrast the results, partial least squares (PLS-SEM) and multigroup analysis were used.

This research finds that consumers in emerging countries like Mexico are less likely to act on their beliefs to engage in protesting behaviors when confronted with an environmental problem such as the improper disposal of potentially harmful products. Consumers on both sides of the border are more likely to engage in consumer activism behaviors if social economic norms (SEN) are considered. Furthermore, the multi-group analysis revealed that US consumers' TIG moderates the relationship between awareness of consequences (AC) and consumer activism intention (CAI) contrasting with Mexican consumers where such moderating relationship does not exist.

This research makes a significant contribution to the literature by evaluating TIG as an important predictor of consumer activism behaviors. TIG can significantly affect consumer activism behaviors in the United States, but not in Mexico. It also demonstrates that SEN rather than social benefit norms (SBN) can trigger CAI in both samples.