ABSTRACT In the era of burgeoning artificial intelligence and robotics, the integration of robots within the hospitality sector has become a prevalent phenomenon. Despite this, scholarly attention has predominantly been directed toward the dynamics of robot-customer interactions, leaving a significant gap in our understanding of the impact of robots on employee learning behaviors. This study uses transformative learning theory to explore how robot compatibility influences employee learning. A PLS-SEM analysis of a survey of 347 hotel employees found that robot compatibility had a positive influence on employee explorative and exploitative learning. Both contextual intrinsic and extrinsic motivation partially mediated the effect of compatibility on employee learning behaviors. Robot awareness positively moderated the effect of compatibility on contextual motivation. The study’s conclusions provide enlightenment for hospitality organizations to achieve improved employee-robot collaboration and value co-creation.

This paper discusses the transparency and regulation of Artificial Intelligence (hereinafter the AI). It discusses the growing trend of using decision-making algorithms in various areas of life and the need for transparent and the understandable AI systems. The author highlights the concept of a ‟black box” where algorithms and their processes become hidden to users and developers, and emphasises the importance of explainability and accessibility of information about the operation of the AI. The article also addresses the right to explanation and possible strategies for ensuring transparency and accountability in algorithmic decision-making, including parameter documentation and system certification.

In this study the authors assessed the ability of Chat Generative Pretrained Transformer (ChatGPT) 3.5 and ChatGPT4 to generate readable and accurate summaries of published neurosurgical literature. Abstracts published in journal issues released between June 2023 and August 2023 (n = 150) were randomly selected from the top 5 ranked neurosurgical journals according to Google Scholar. ChatGPT models were instructed to generate a readable layperson summary of the original abstract from a statistically validated prompt. Readability results and grade-level indicators (RR-GLIs) scores were calculated for GPT3.5- and GPT4-generated summaries and original abstracts. Two physicians independently rated the accuracy of ChatGPT-generated layperson summaries to assess scientific validity. One-way ANOVA followed by pairwise t-test with Bonferroni correction were performed to compare readability scores. Cohen's kappa was used to assess interrater agreement between the two rater physicians. Analysis of 150 original abstracts showed a statistically significant difference for all RR-GLIs between the ChatGPT-generated summaries and original abstracts. The readability scores are formatted as follows (original abstract mean, GPT3.5 summary mean, GPT4 summary mean, p value): Flesch-Kincaid reading grade (12.55, 7.80, 7.70, p < 0.0001); Gunning fog score (15.46, 10.00, 9.00, p < 0.0001); Simple Measure of Gobbledygook (SMOG) index (11.30, 7.13, 6.60, p < 0.0001); Coleman-Liau index (14.67, 11.32, 10.26, p < 0.0001); automated readability index (10.87, 8.50, 7.75, p < 0.0001); and Flesch-Kincaid reading ease (33.29, 68.45, 69.55, p < 0.0001). GPT4-generated summaries demonstrated higher RR-GLIs than GPT3.5-generated summaries in the following categories: Gunning fog score (0.0003); SMOG index (0.027); Coleman-Liau index (< 0.0001); sentences (< 0.0001); complex words (< 0.0001); and % complex words (0.0035). A total of 68.4% and 84.2% of GPT3.5- and GPT4-generated summaries, respectively, maintained moderate scientific accuracy according to the two physician-reviewers. The findings demonstrate promising potential for application of the ChatGPT in patient education. GPT4 is an accessible tool that can be an immediate solution to enhancing the readability of current neurosurgical literature. Layperson summaries generated by GPT4 would be a valuable addition to a neurosurgical journal and would be likely to improve comprehension for patients using internet resources like PubMed.

ABSTRACT The rapid increase of technology and the sophistication in which it is used to obtain and manipulate information presents fertile ground for cybercriminals. Thus, the consequent increase of cybercrime in South Africa poses a threat to individuals, government, businesses, and security agencies; ultimately causing extensive financial losses to the economy. The huge preventive cost incurred by putting in place stringent preventative measures, affect the financial status of individuals and threatens the sustainability of many companies. This study aimed to examine the policing of cybercrime in South Africa by analysing the experiences of police officers who are involved in the policing of this crime. It focussed on the use of cyber intelligence and cybersecurity measures for monitoring, preventing, and combating cybercrime. An empirical study was conducted in five selected provinces of South Africa, and Tesch’s data analysis method was used. The findings indicate the challenges that investigators encounter in obtaining credible digital evidence such as the frequent expiry of digital forensic licences. These challenges are compounded by victims’ unwillingness to testify due to the fear of losing their clients, and the need to protect the reputation of their companies.

Animals have evolved to rapidly detect and recognize brief and intermittent encounters with odor packages, exhibiting recognition capabilities within milliseconds. Artificial olfaction has faced challenges in achieving comparable results-existing solutions are either slow; or bulky, expensive, and power-intensive-limiting applicability in real-world scenarios for mobile robotics. Here, we introduce a miniaturized high-speed electronic nose, characterized by high-bandwidth sensor readouts, tightly controlled sensing parameters, and powerful algorithms. The system is evaluated on a high-fidelity odor delivery benchmark. We showcase successful classification of tens-of-millisecond odor pulses and demonstrate temporal pattern encoding of stimuli switching with up to 60 hertz. Those timescales are unprecedented in miniaturized low-power settings and demonstrably exceed the performance observed in mice. It is now possible to match the temporal resolution of animal olfaction in robotic systems. This will allow for addressing challenges in environmental and industrial monitoring, security, neuroscience, and beyond.

A total of 300 SMEs including beneficiaries and non-beneficiaries of the Presidential Business Development Support initiative were surveyed in Ghana using structured interview questionnaires. The survey objectives were to (i) investigate the extent of adoption of emerging technologies including the Internet of Things (IoTs), Artificial Intelligence (AI), e-Commerce, Mobile Banking and Social Media among SMEs in Ghana; (ii) identify the main barriers to adoption of emerging technologies; and (iii) explore any association between the level of technology adoption by SMEs and access to new markets. Close to 60% of the beneficiary SMEs had adopted less sophisticated technologies such as social media, mobile banking and e-commerce as compared to 50% adoption among non-beneficiaries. Overall, less than 5% of the SMEs surveyed were using sophisticated technologies such AI and IoTs. This study adopted the Technological-Organizational-Environmental (TOE) framework to identify barriers to technology adoption. The top five barriers to technology adoption by the SMEs identified from this study are: (i) lack of finance, (ii) insufficient government support, (iii) lack of absorptive capability, (iv) insufficient demand and (v) poor technology enabling infrastructure. The beneficiary SMEs had better access to sub-regional (West Africa), regional (Africa) and international markets than the non-beneficiaries. Logit regression results showed that the most significant factors positively influencing technology adoption are Education, Marketing Skills, Human Resource Skills, Gender, Market Opportunities and Financial Improvement. There was a positive but weak correlation between technology adoption and access to new market opportunities.

The article analyses works devoted to the development of design methods for warp-pile fabrics with lining, which showed that methods for calculating the parameters and algorithms for constructing such weaves are absent in known literary sources. The authors propose a method for designing weaves of two-piece warp-pile fabrics with lining bases, with the possibility of their production on a loom with single-shed and double-shed weaving methods. An algorithm for construction has been developed, formulas for calculating the parameters of weaves have been proposed, allowing increasing the thickness, strengthen the structure and increase the service life of warp-pile fabrics. An example of constructing a filling pattern of a two-piece warp-pile fabric with lining bases with single-shed and double-shed weaving methods is given. Velvets, plush, artificial fur and carpets with cut pile are produced according to the considered technology.

PurposeCompanies increasingly leverage artificial intelligence (AI) to enhance human performance, particularly in e-commerce. However, the effectiveness of AI augmentation remains controversial. This study investigates whether, how and why AI enhances human agents’ sales through a randomized field experiment.Design/methodology/approachThis study conducts a two-by-two factorial randomized field experiment (N = 1,090) to investigate the effects of AI augmentation on sales. The experiment compares sales outcomes handled solely by human agents with those augmented by AI, while also examining the moderating effect of agents’ experience levels and the underlying mechanisms behind agents’ responses.FindingsThe results reveal that AI augmentation leads to a significant 5.46% increase in sales. Notably, the impact of AI augmentation varies based on agents’ experience levels, with inexperienced agents benefiting nearly six times more than their experienced counterparts. Mediation analysis shows that AI augmentation improves response timeliness, accuracy and sentiment, thereby boosting sales.Originality/valueThis study highlights the role of AI augmentation in human–AI collaboration, demonstrates the varying impacts of AI augmentation based on agents’ experience levels and offers insights for organizations on how to regulate AI augmentation to enhance agent responses and drive sales.

The emergence of Chat GPT technology represents a significant breakthrough in natural language processing, revolutionizing human-machine interaction and presenting profound challenges to vocational education modernization in China. In the Chat GPT era, vocational education undergoes transformative changes, including personalized learning reaching new heights, big data-driven intelligent decision-making entering a new phase, and the integration of education resources on unprecedented scales. However, this modernization effort also encounters several critical issues, such as overreliance on technology and the associated risks of unemployment, threats to data privacy and security, erosion of ethical values, and the widening digital divide in education. To ensure the high-quality development of vocational education modernization with the support of artificial intelligence tools like Chat GPT, it is crucial to enhance supervision and regulation of Chat GPT technology applications, promoting its safe and responsible use. Moreover, prioritizing technology ethics and moral education becomes imperative to nurture students’ ethical awareness and moral compass in the utilization and development of technology. Lastly, optimizing the distribution of digital educational resources is essential to promote educational equity and bridge the digital divide among students.

Particle swarm optimisation (PSO) is a swarm intelligence algorithm that finds candidate solutions by iteratively updating the positions of particles in a swarm. The decentralised optimisation methodology of PSO is ideally suited to problems with multiple local minima and deceptive fitness landscapes, where traditional gradient-based algorithms fail. PSO performance depends on the use of a suitable control parameter (CP) configuration, which governs the trade-off between exploration and exploitation in the swarm. CPs that ensure good performance are problem-dependent. Unfortunately, CPs tuning is computationally expensive and inefficient. Self-adaptive particle swarm optimisation (SAPSO) algorithms aim to adaptively adjust CPs during the optimisation process to improve performance, ideally while reducing the number of performance-sensitive parameters. This paper proposes a reinforcement learning (RL) approach to SAPSO by utilising a velocity-clamped soft actor-critic (SAC) that autonomously adapts the PSO CPs. The proposed SAC-SAPSO obtains a 50% to 80% improvement in solution quality compared to various baselines, has either one or zero runtime parameters, is time-invariant, and does not result in divergent particles.