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Technology is widely considered the main source of economic progress, but it has also generated cultural anxiety throughout history. The developed world is now suffering from another bout of such angst. Anxieties over technology can take on several forms, and we focus on three of the most prominent concerns. First, there is the concern that technological progress will cause widespread substitution of machines for labor, which in turn could lead to technological unemployment and a further increase in inequality in the short run, even if the long-run effects are beneficial. Second, there has been anxiety over the moral implications of technological process for human welfare, broadly defined. While, during the Industrial Revolution, the worry was about the dehumanizing effects of work, in modern times, perhaps the greater fear is a world where the elimination of work itself is the source of dehumanization. A third concern cuts in the opposite direction, suggesting that the epoch of major technological progress is behind us. Understanding the history of technological anxiety provides perspective on whether this time is truly different. We consider the role of these three anxieties among economists, primarily focusing on the historical period from the late 18th to the early 20th century, and then compare the historical and current manifestations of these three concerns.

Despite the advancement in autonomous vehicles, public trust and acceptance are crucial for AV’s widespread adoption. This study examines how different collaboration levels between human drivers and artificial intelligence influence users’ trust and acceptance of AVs. Using an extended Technology Acceptance Model, this study incorporates psychological factors and technological attitudes such as perceived safety, perceived risk, AI literacy, and AI technophobia. Data collected from 392 vehicle owners across 11 Chinese cities were analyzed using Partial Least Squares Structural Equation Modeling and Multi-Group Analysis. The findings reveal that at the fully manual level, perceived ease of use significantly influences perceived usefulness, while trust remains grounded in mechanical reliability rather than AI systems. In contrast, as AI assumes driving responsibilities at collaborative automation levels, the findings show that AI literacy significantly increases perceived trust and ease of use, while AI technophobia decreases them, with these effects varying across different driving automation levels. As AI takes on greater driving responsibilities, perceived ease of use becomes less critical, and perceived trust increasingly influences users’ acceptance. These findings highlight the need for targeted public education and phased automation strategies, offering guidance for AV developers to address user concerns and build trust in autonomous technologies. By enhancing public trust and acceptance, this study contributes to sustainable development by promoting safer roads and enabling more efficient, resource-conscious transportation systems. Gradually integrating AVs into urban mobility also supports smart city initiatives, fostering more sustainable urban environments.

Purpose The purpose of this study is to explore robot-phobia as a source of occupational stress among hospitality employees in the context of increasing robotization in the industry. Design/methodology/approach The study sampled 321 lodging employees and 308 food service employees in the USA. An online panel company recruited the participants and administered an online survey. The study used various analytical methods, including structural modeling, t-tests and multi-group analyses. Findings The study results reveal that hospitality workers experience robot-phobia regardless of their sector or position. Robot-phobia causes job insecurity and stress, which increases turnover intention. These negative outcomes are more pronounced for those who interact more frequently with robots. Practical implications The study findings suggest that hospitality workers fear being replaced by robots in the near future. Therefore, hospitality organizations should offer adequate training and education on the advantages and drawbacks of robots and establish a supportive and collaborative work environment that values human–robot interaction. Originality/value This study offers new insights regarding human–robot interaction from the employee perspective by introducing the concept of robot-phobia in the hospitality workplace. A comprehensive picture of how hospitality employees confront the increasing presence of robots is provided in this study.

IntroductionCOPD exacerbations lead to increased mortality and disease progression. Maintenance inhaled therapies can reduce exacerbation risk amongst COPD patients, but non-adherence reportedly ranges from 20–60% in this population. The ongoing cluster randomised trial (MAGNIFY) is investigating the use of digital adherence support as a solution to this problem, but there is little evidence regarding patients’ willingness to accept such devices.Aims and ObjectivesTo explore patient-reported barriers to accepting a technological adherence package.MethodsCOPD patients were screened for eligibility for the UK-based MAGNIFY trial (Price et al 2021 doi: 10.2147/POR.S302809), with main inclusion criteria being aged 40 years or above, with ≥2 moderate/severe exacerbations in the last two years and with ≤50% adherence to mono/dual therapy. Eligible patients received a phone call from a pharmacist who conducted a remote patient review and invited them to use the digital support package, comprising an Ultibro Breezhaler and adherence support technology (Propeller Health). Patients unwilling/unable to accept the package were asked to provide reasons.Results87 participating practices had a total COPD list size of 33211 patients, of which 1833 patients met the trial eligibility criteria. Pharmacists excluded 541 patients following electronic medical record review, and were unable to contact a further 111 patients. Of the 1181 patients contacted, 73 were clinically unsuitable for the adherence package. Of the remaining 1108 patients, 395 (36%) were unwilling/unable to accept the adherence package; reasons included: no smartphone/incompatible phone (n=273), unwilling to change inhaler (n=71), unwilling to use the support package (n=19), life events (n=12), partially sighted (n=2), no reason (n=18). Patient demographics are reported in table 1.ConclusionsThe main reasons for not accepting the adherence package were due to lacking a compatible smartphone or not wanting to change inhaler, rather than unwillingness to use technology. Though this is data from a single trial, the patients are from multiple diverse practices. The data suggest that technophobia may not be the most important barrier to patients accepting digital adherence support. A quarter of invited patients did not have a smartphone, highlighting the need for future implementation to ensure equitable access to digital support.Abstract P237 Table 1Patient demographic data, stratified by acceptance status Patients invited to use package (n=1108)‡ Patients accepting package (n=713)‡ Patients with no or incompatible phone (n=273)‡ Patients declining package (n=122)‡ Age; mean (SD) 70.3 (10.7) 68.0 (10.5) 75.6 (9.4) 69.9 (10.1) Sex; n (%) male 445 (44.8) 275 (45.3) 116 (43.5) 54 (45.4) FEV1% predicted; mean (SD)* 68.7 (18.2) 69.7 (17.7) 66.7 (19.0) 68.6 (18.8) Smoking status; n (%) Current smoker393 (39.6)250 (41.2)100 (37.5)43 (36.1) Ex-smoker550 (55.4)330 (54.4)151 (56.6)69 (58.0) Never smoker 50 (5.0) 27 (4.5) 16 (6.0) 7 (5.9) CAT score; mean (SD)* 13.3 (7.9) 13.4 (8.3) 13.3 (7.2) 12.7 (7.5) mMRC; n (%) grade 2–4* 373 (40.4) 208 (36.9) 113 (45.4) 52 (46.9) Cambridge multimorbidity score; mean (SD)* 2.9 (1.4) 2.8 (1.3) 3.1 (1.5) 2.7 (1.3) Index of Multiple Deprivation decile; n (%)* 178 (8.0)51 (8.6)18 (6.9)9 (7.6) 2230 (23.7)151 (25.5)59 (22.7)20 (17.0) 3123 (12.7)67 (11.3)37 (14.2)19 (16.1) 4108 (11.1)67 (11.3)30 (11.5)11 (9.3) 5142 (14.6)74 (12.5)49 (18.9)19 (16.1) 686 (8.9)48 (8.1)27 (10.4)11 (9,3) 728 (2.9)14 (2.4)10 (3.9)4 (3.4) 871 (7.3)48 (8.1)7 (2.7)16 (13.6) 976 (7.8)49 (8.3)19 (7.3)8 (6.8) 10 28 (2.9) 23 (3.9) 4 (1.5) 1 (0.9) ‡number of patients with extracted electronic medical record data: patients invited (n=993), patients accepting (n=607), patients with no/incompatible phone (n=267), patients declining (n=119).*missing data.

This study aimed to investigate the emotional impact of technology use in an Italian adult population and to detect technophobia. This cross-sectional study was conducted with 117 Italian participants (age range of 50–67 years). Measured variables were computer anxiety and technology use ability. The results revealed technophobia features in the Italian adult population related to inadequate management of technology. One-way analysis of variance and Bonferroni’s post-hoc analysis showed that non-autonomous (p < 0.01), low-frequency (p < 0.01), and feeling-a-need-for-help users (p < 0.01) had higher levels of computer anxiety. Based on our data, although lifelong learning is a powerful digital need, a considerable proportion of the adult population is not digitally skilled, enlarging the gap between young (native digital) and adult (digital and non-digital adults and seniors) populations. Adult inclusivity in digital living is inadequate and likely affects their quality of life. Thus, our findings highlight technophobia as a possible new risk factor for Italian adults because it can affect their daily life through low adherence to digital living; rather than aging successfully, they could develop fragile aging.

Technology adoption in business is essential for advancement, even if it frequently encounters user resistance. The current study model examined the resistance to adopting e-tourism by online tourists in Bangladesh using the innovation resistance theory (IRT) components such as usage barriers, value barriers, risk barriers, tradition barriers, and image barriers. The influence of technophobia and facilitating conditions were considered to extend the IRT model. The purposive sampling technique was utilized in this study. Data were collected using both Google Forms and printed questionnaires from users of online travel agencies in Bangladesh. Structural Equation Modeling (SEM) was used to analyze data from 292 online travelers. SPSS version 25 and AMOS version 26 were used for the analysis. Results showed that technophobia is the leading cause of resistance to e-tourism, followed by facilitating conditions, risk barriers, tradition barriers, and usage barriers. However, the current study has significant theoretical and practical ramifications for executives of the e-tourism industry with regard to developing strategies to deal with the factors responsible for the resistance to e-tourism.