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The objective of this paper is to develop a condition-based maintenance (CBM) scheme for pneumatic cylinders. The CBM scheme will detect two common types of air leaking failure modes and identify the leaky/faulty cylinder. The successful implementation of the proposed scheme will reduce energy consumption, scrap and rework, and time to repair.

Effective implementation of maintenance is important to reduce operation cost, improve productivity and enhance quality performance at the same time. Condition-based monitoring is an effective maintenance scheme where maintenance is triggered based on the condition of the equipment monitored either real time or at certain intervals. Pneumatic air systems are commonly used in many industries for packaging, sorting and powering air tools among others. A common failure mode of pneumatic cylinders is air leaks which is difficult to detect for complex systems with many connections. The proposed method consists of monitoring the stroke speed profile of the piston inside the pneumatic cylinder using hall effect sensors. Statistical features are extracted from the speed profiles and used to develop a fault detection machine learning model. The proposed method is demonstrated using a real-life case of tea packaging machines.

Based on the limited data collected, the ensemble machine learning algorithm resulted in 88.4% accuracy. The algorithm can detect failures as soon as they occur based on majority vote rule of three machine learning models.

Early air leak detection will improve quality of packaged tea bags and provide annual savings due to time to repair and energy waste reduction. The average annual estimated savings due to the implementation of the new CBM method is $229,200 with a payback period of less than two years.

To the best of the authors’ knowledge, this paper is the first in terms of proposing a CBM for pneumatic systems air leaks using piston speed. Majority, if not all, current detection methods rely on expensive equipment such as infrared or ultrasonic sensors. This paper also contributes to the research gap of economic justification of using CBM.

Military values/virtues are a subset of ethical values. The purpose of this study is to examine the effect of military virtues on job performance, either directly, or indirectly through mediation by, loyalty, patience, respect, employee engagement, job performance, military ethics, courage, self-discipline, caring, military virtue, Sri Lanka Air Force (SLAF) employee engagement.

Military virtues were conceptualized as a collective construct, consisting of loyalty, courage, patience, respect, self-discipline and caring. Using a sample of 254 military officers in the SLAF, the authors measured the effect of military virtues on job performance. The first model was a direct measurement of the influence of military virtues on job performance. The second model measured the influence of military virtues on employee engagement, followed by measurement of the influence of employee engagement on job performance. Structural equation modeling was used in data analysis.

Both direct effects and mediated effects of military virtues on job performance were significant. However, the direct effect was stronger, suggesting that military virtues in and of themselves resulted in superior performance, more effectively, than by first increasing employee engagement with the task or the organization.

This may be an initial empirical examination of the effects of military virtues on job performance.

Utilizing the theoretical foundation of the stimulus-organism-response framework, the present work developed and investigated a conceptual model. The work explores the effects of perceived risk of COVID-19 on tourists' choice hesitation and choice confidence. Furthermore, it examines the impacts of choice hesitation and choice confidence on psychological distress, which, in turn, influences purchase intentions and risk-protective behavior. Additionally, the study assesses the boundary effects of vulnerability on the association between choice hesitation, choice confidence, and psychological distress.

An online survey was administered in China during COVID-19 to assess the postulated hypotheses. We collected 491 responses using purposive sampling, and covariance-based structural equation modeling (CB-SEM) was performed to investigate the relationships.

Results show that the perceived risk of COVID-19 positively influences the choice hesitation and negatively impact choice confidence. It was also found that choice hesitation and choice confidence positively developed psychological distress, which, in turn, negatively triggered purchase intentions and positively developed risk-protective behavior. Additionally, perceived vulnerability had a significant moderating impact on the proposed relationships, strengthening psychological distress.

In the current context, this study measures bipolar behavioral outcomes using the S-O-R model. Because cognitive processes influence participation in health preventative behavior during the spread of diseases, we highlighted how the perception of risk and vulnerability to a pandemic serves as a reliable indicator of certain behaviors. This study advances understanding of how the psychological mindset of tourists copes with such circumstances. Due to the pandemic, tourists face limitations in their choices and are placing greater emphasis on adopting protective measures to mitigate associated risks.

In high-tech markets, innovation is always generative and continuous both within the iteration in a product's development process and throughout the upgrade of multi-generational products. Inspired by this practical phenomenon, this study aims to explore the mechanism of innovation generativity and continuity to explain how future innovations benefit from current innovations.

The study conducted qualitative research to explore innovation generativity and continuity by investigating five electronic information enterprises. The authors employed the ambidexterity perspective to explore the research question.

The authors found innovation generativity has three dimensions: inheritance, metabolism and inspection. These three dimensions and their interactions are what forms the mechanism of innovation generativity and continuity. The authors also found many paradoxes that prompt enterprises to pursue innovation generativity and continuity, and through this innovation process, enterprises are able to attain continuous innovation.

This study theoretically uncovers “how” to carry out innovation generativity and continuity, as well as the antecedents and the outcome. The findings contribute to research on product innovation, continuous innovation and ambidexterity, and have implications for managers who seek to improve innovation generativity and continuity.

The recent pandemic disrupted the way in which businesses transact with each other. In response to maintaining cleanliness in business-to-business (B2B) settings, artificial intelligence (AI)-enabled robots have been adopted as substitutes for cleaning personnel, yet their implications remain largely underexamined. This study aims to examine B2B buyer responses to cleaning information notices (human vs AI-enabled agent) placed at either the entry to the premises or the sales counter, thereby adding to the nascent literature in this line of inquiry.

Three field experiments were conducted across diverse B2B businesses (wholesalers in Studies 1–2 and a commercial business in Study 3). To achieve greater empirical rigor and generalizability, this research used diverse stimuli across different B2B settings. In addition, the results ruled out alternate explanations and shed light upon political ideology as a boundary condition. Finally, a single-paper meta-analysis confirmed H1, consolidating the established effect.

Featuring over 1,000 B2B buyers, the results show that politically liberal B2B buyers express greater preference for human over AI-performed cleaning while labor-orientated buyers are indifferent. Importantly, this effect is driven by greater relaxation associated with humans, which in turn, increases their future patronage and referral intent.

The results enrich the collective knowledge of the adoption of AI-enabled robots, reinforcing for marketing practitioners and businesses that the reliance on human-based outcomes remains a preferred touchpoint in B2B settings, particularly for liberals.