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With over-the-top (OTT) streaming services rapidly transforming the media industry and saturating the market, the authors' study seeks to enrich the goal-directed behaviour model by exploring how perceived risks and descriptive norms influence OTT consumption.

Survey data from OTT subscribers were collected online to assess their risk behaviours. The 353 responses obtained were analysed with SmartPLS, validating the structural equation modelling (SEM) through structural and measurement model verification.

The authors' findings illustrate that descriptive norm, perceived behavioural control, as well as positive and negative anticipated emotion (NEM) and attitude, contribute positively to the desire to engage with OTT streaming services. Interestingly, the authors' study contradicts common assumptions, revealing that subjective norms do not significantly impact the propensity to utilise OTT services. This counterintuitive finding necessitates a reconsideration of prevalent theories and contributes to a nuanced understanding of OTT adoption determinants.

The data gathering for this study were conducted from the perspective of a single nation. Therefore, caution must be exercised when generalising this study's results.

The practical ramifications of this research are vast, providing OTT service providers and marketers with actionable insights to maximise user engagement and navigate perceived risks related to OTT service adoption and consumption.

This study's exploration of perceived risks and descriptive norms enhances the goal-directed behaviour model's breadth, facilitating a holistic comprehension of the constructs shaping OTT consumption behaviours. It would be the first attempt to combine perceptual, affective and behavioural factors and perceived risks to understand the user's predisposition to engage in OTT streaming services.

Plastic pollution is a major issue that plagues modern society. Scholars are interested in comprehending consumers’ behavioural actions to address plastic pollution. This study aims to delve into the determinants of consumers’ engagement with social media as a medium to address plastic pollution.

A conceptual model is developed that extends the behavioural reasoning theory (BRT). Using a quantitative approach, 476 responses underwent structural equation modelling analysis.

Results indicate that “reasons for” positively correlate with attitude and intention towards socially responsible engagement. Contrarily, “Reasons against” demonstrated a positive association with socially responsible engagement intention. Attitudes favouring socially responsible engagement correlate positively with the underlying intention. The moderation analysis underscores the positive relation of social return on social media with consumers’ attitude and their “reasons for” leaning towards socially responsible engagement intention. Notably, a positive connection was established between socially responsible engagement intention and the trifecta of consumption, contribution and content creation behaviours.

By enhancing the BRT, this research sheds light on novel perspectives regarding consumers’ engagement on social media platforms. Distinctively, it is among the handful of studies probing the influence of behavioural intention across diverse behavioural outcomes. The insights gained from this study, grounded in empirical evidence from an emerging market, are poised to guide policymakers, governmental agencies and industry practitioners in formulating effective strategies to combat plastic pollution. Additionally, the study can assist in achieving the UN sustainable development goals (SDGs), specifically SGD 12, SGD 13, SDG 14 and SGD 17.

This paper aims to investigate the relationship between employee training and bank risk to verify whether and to what extent an increase in employee training, as a soft component of total quality management (TQM), affects bank risk.

The research adopts a panel regression, based on a unique dataset of a sample of Italian banks over the period 2011–2018, to test whether employee training affects bank risk, measured alternatively in terms of Z-score, a proxy of bank stability and non-performing loans (NPLs)/gross loans ratio as a proxy of credit risk.

Research findings reveal that increasing employee training leads to growing bank stability. In contrast, credit risk is not affected by employee training. However, by investigating training heterogeneity, this study found that the increase in the number of managerial training hours, as a proxy for soft skills training, negatively impacts credit risk. Therefore, an increase in soft skills leads to a reduction in bank credit risk.

This study provides empirical evidence in support of the relationship between employee training and bank risk, which seems novel in the literature. From a managerial point of view, this study highlights the need for banks to pay attention to the skills, particularly soft skills, that banks' employees must possess to effectively manage bank risk and, more specifically, the core bank risk.

Empirical evidence on the relationship between employee training, soft/hard skills and bank risk appears limited if not absent. Therefore, the findings provide insights for a more nuanced interpretation of variables that affect bank risk.

The purpose of this study is to explore the use of smartphone-embedded microelectro-mechanical sensors (MEMS) for accurately estimating rotating machinery speed, crucial for various condition monitoring tasks. Rotating machinery (RM) serves a crucial role in diverse applications, necessitating accurate speed estimation essential for condition monitoring (CM) tasks such as vibration analysis, efficiency evaluation and predictive assessment.

This research explores the utilization of MEMS embedded in smartphones to economically estimate RM speed. A series of experiments were conducted across three test setups, comparing smartphone-based speed estimation to traditional methods. Rigorous testing spanned various dimensions, including scenarios of limited data availability, diverse speed applications and different smartphone placements on RM surfaces.

The methodology demonstrated exceptional performance across low and high-speed contexts. Smartphones-MEMS accurately estimated speed regardless of their placement on surfaces like metal and fiber, presenting promising outcomes with a mere 6 RPM maximum error. Statistical analysis, using a two-sample t-test, compared smartphone-derived speed outcomes with those from a tachometer and high-quality (HQ) data acquisition system.

The research limitations include the need for further investigation into smartphone sensor calibration and accuracy in extremely high-speed scenarios. Future research could focus on refining these aspects.

The societal impact is substantial, offering cost-effective CM across various industries and encouraging further exploration of MEMS-based vibration monitoring.

This research showcases an innovative approach using smartphone-embedded MEMS for RM speed estimation. The study’s multidimensional testing highlights its originality in addressing scenarios with limited data and varied speed applications.