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This paper aims to extend the technology acceptance model (TAM) to explore the factors influencing a hotel customer’s intention to use a fingerprint system instead of a traditional keycard system and the moderating factors (i.e. gender and age) on the relationships between the proposed factors and the customer’s intention to use fingerprint technology. When hotels add new technologies, the potential vulnerability of their systems also increases. Underestimating such risks can possibly result in massive losses from identity theft and related fraud for hoteliers. Customers who are aware of these risks may become more open to innovative methods of identification or verification, such as biometrics.

The online survey instrument was developed based on TAMs. The authors collected complete 526 responses from hotel customers and tested the hypotheses using structural equation modeling.

This study found seven factors (i.e. perceived usefulness, perceived ease of use, subjective norm, perceived convenience, perceived data security, perceived property security and personal concerns) which significantly influence a hotel customer’s intention to use fingerprint technology. Gender and age played important moderating roles in the relationships between some of these factors and the intention to use.

Recommendations are made as to how hotels can benefit from the implementation of biometrics, particularly fingerprint systems. For example, a hotel’s marketing campaign can be more effective by emphasizing the advantages of fingerprint technology related to “data security and convenience” for younger consumers (i.e. Gen X and Gen Y).

Both educators and practitioners will benefit from the findings of this empirical study, as there are very few published studies on a customer’s fingerprint technology acceptance in the hotel context.

Sellers view facial recognition mobile payment services (FRMPS) as a convenient and cost-saving way to receive immediate payments from customers. For consumers, however, these biometric identification technologies raise issues of usability as well as privacy, so FRMPS are not always preferable. This study uses the stressor–strain–outcome (S–S–O) framework to illuminate the underlying mechanism of FRMPS resistance, thereby addressing the paucity of research on users' negative attitudes toward FRMPS.

Drawing from the stressor–strain–outcome (S–S–O) framework, the purpose of this study is to illuminate the underlying mechanism of FRMPS resistance. To this end, they invited 566 password authentication users who had refused to use FRMPS to complete online survey questionnaires.

The findings enrich the understanding of FRMPS resistance and show that stressors (i.e. system feature overload, information overload, technological uncertainty, privacy concern and perceived risk) aggravate the strain (i.e. technostress), which then leads to users’ resistance behaviors and negative word of mouth.

Advances in payment methods have profoundly changed consumers’ consumption and payment habits. Understanding FRMPS resistance can provide marketers with strategies for dealing with this negative impact. This study theoretically confirms the S–S–O paradigm in the FRMPS setting and advances it by proposing thorough explanations of the major stressors that consumers face. Building on their findings, the authors suggest ways service providers can eliminate the stressors, thereby reducing consumers’ fear and preventing resistance or negative word-of-mouth behaviors. This study has valuable implications for both scholars and practitioners.

This study aims to examine the blockchain landscape in supply chain management by drawing insights from academic and industry literature. It identifies the key drivers, categorizes the products involved and highlights the business values achieved by early adopters of blockchain technology within the supply chain domain. Additionally, it explores fingerprinting techniques to establish a robust connection between physical products and the blockchain ledger.

The authors combined the interpretive sensemaking systematic literature review to offer insights into how organizations interpreted their business challenges and adopted blockchain technology in their specific supply chain context; content analysis (using Leximancer automated text mining software) for concept mapping visualization, facilitating the identification of key themes, trends and relationships, and qualitative thematic analysis (NVivo) for data organization, coding and enhancing the depth and efficiency of analysis.

The findings highlight the transformative potential of blockchain technology and offer valuable insights into its implementation in optimizing supply chain operations. Furthermore, it emphasizes the importance of product provenance information to consumers, with blockchain technology offering certainty and increasing customer loyalty toward brands that prioritize transparency.

This research has several limitations that should be acknowledged. First, there is a possibility that some relevant investigations may have been missed or omitted, which could impact the findings. In addition, the limited availability of literature on blockchain adoption in supply chains may restrict the scope of the conclusions. The evolving nature of blockchain adoption in supply chains also poses a limitation. As the technology is in its infancy, the authors expect that a rapidly emerging body of literature will provide more extensive evidence-based general conclusions in the future. Another limitation is the lack of information contrasting academic and industry research, which could have provided more balanced insights into the technology’s advancement. The authors attributed this limitation to the narrow collaborations between academia and industry in the field of blockchain for supply chain management.

Practitioners recognize the potential of blockchain in addressing industry-specific challenges, such as ensuring transparency and data provenance. Understanding the benefits achieved by early adopters can serve as a starting point for companies considering blockchain adoption. Blockchain technology can verify product origin, enable truthful certifications and comply with established standards, reinforcing trust among stakeholders and customers. Thus, implementing blockchain solutions can enhance brand reputation and consumer confidence by ensuring product authenticity and quality. Based on the results, companies can align their strategies and initiatives with their needs and expectations.

In essence, the integration of blockchain technology within supply chain provenance initiatives not only influences economic aspects but also brings substantial social impacts by reinforcing consumer trust, encouraging sustainable and ethical practices, combating product counterfeiting, empowering stakeholders and contributing to a more responsible, transparent and progressive socioeconomic environment.

This study consolidates current knowledge on blockchain’s capacity and identifies the specific drivers and business values associated with early blockchain adoption in supply chain provenance. Furthermore, it underscores the critical role of product fingerprinting techniques in supporting blockchain for supply chain provenance, facilitating more robust and efficient supply chain operations.

Because of the continued use of mobile, cloud and the internet of things, the possibility of data breaches is on the increase. A secure authentication and authorization strategy is a must for many of today’s applications. Authentication schemes based on knowledge and tokens, although widely used, lead to most security breaches. While providing various advantages, biometrics are also subject to security threats. Using multiple factors together for authentication provides more certainty about a user’s identity; thus, leading to a more reliable, effective and more difficult for an adversary to intrude. This study aims to propose a novel, secure and highly stable multi-factor one-time password (OTP) authentication solution for mobile environments, which uses all three authentication factors for user authentication.

The proposed authentication scheme is implemented as a challenge-response authentication where three factors (username, device number and fingerprint) are used as a secret key between the client and the server. The current scheme adopts application-based authentication and guarantees data confidentiality and improved security because of the integration of biometrics with other factors and each time new challenge value by the server to client for OTP generation.

The proposed authentication scheme is implemented on real android-based mobile devices, tested on real users; experimental results show that the proposed authentication scheme attains improved performance. Furthermore, usability evaluation proves that proposed authentication is effective, efficient and convenient for users in mobile environments.

The proposed authentication scheme can be adapted as an effective authentication scheme to accessing critical information using android smartphones.

Explores the potential use of behavioural and physiological biometric techniques in the battle against credit card fraud in the retail environment. Discusses automatic speaker and dynamic signature verification, fingerprint and facial recognition, retinal and iris scanning, and hand and finger geometry. The requirement for recognition performance, speed of use, usability and customer acceptance, device and card cost are considered, along with industry standards for biometric devices.

This study aims to explain how hotel guests form attitudes toward facial recognition technology in Singapore by integrating technology acceptance model (TAM), privacy calculus theory and personal innovativeness.

A self-administered online questionnaire was developed with measurements adopted from past research. Guests who stayed in four- or five-star hotels in Singapore were recruited via systematic random sampling. Structural equation modeling was conducted to examine the proposed integrated models.

Results showed that hotel guests performed calculative cognitive processes, weighing the benefits and risks of using facial recognition check-in system. Contradictory to the past research which suggested that trust activates both perceived risk and benefits, this study demonstrated that trust independently directed consumer attention on the benefits gained while risk perception was triggered by privacy concern. Furthermore, the current study revealed that the ease of use of facial recognition check-in system could possibly backfire.

The research indicates that the effort to adopt new technology in the hotel industry is promising in view of the growing millennials and Generation Z population who are digital natives. Furthermore, the current study highlights ways to elevate institutional trust and divert consumers’ attention from risk perception to enhance their positive attitude and behavior toward accepting facial recognition check-in system.

This study integrated TAM with privacy calculus theory and personal innovativeness in examining the acceptance of facial recognition check-in system in the hotel industry in Singapore. This study is also the first, to the best of the authors’ knowledge, to investigate the relationships among privacy concern, perceived risk, institutional trust and perceived benefits, as well as their effects on consumers’ attitudes and behavior toward the biometric system.

Purpose: The purpose of this chapter is to review and critically evaluate robots, artificial intelligence and service automation (RAISA) applications in the restaurant industry to educate professors, graduate students, and industry professionals.

Design/methodology/approach: This chapter is a survey of applications of RAISA in restaurants. The chapter is based on the review of professional and peer-reviewed academic literature, and the industry insight section was prepared based on a 50-minute interview with Mr. Juan Higueros, Chief Operations Officer of Bear Robotics.

Findings: Various case studies presented in this chapter illustrate numerous possibilities for automation: from automating a specific function to complete automation of the front of the house (e.g., Eatsa) or back of the house (e.g., Spyce robotic kitchen). The restaurant industry has already adopted chatbots; voice-activated and biometric technologies; robots as hosts, food runners, chefs, and bartenders; tableside ordering; conveyors; and robotic food delivery.

Practical implications: The chapter presents professors and students with a detailed overview of RAISA in the restaurant industry that will be useful for educational and research purposes. Restaurant owners and managers may also benefit from reading this chapter as they will learn about the current state of technology and opportunities for RAISA implementation.

Originality/value: To the best of the authors’ knowledge, this chapter presents the first systematic and in-depth review of RAISA technologies in the restaurant industry.

This paper investigated the extent to which the predictive power of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) was robust against cultural variations and to what extent its predictive power could be improved by including face-valid individual differences (i.e. perceived risk and personal innovativeness) and cultural factors (i.e. individualism and uncertainty avoidance).

Data were collected from web surveys of Chinese, American and Belgian mobile Internet users (total n = 1,068).

The authors found that the UTAUT2 model was less predictive in the country where the adoption of mobile payment service (MPS) is high (i.e. China). In contrast, the UTAUT2 model was more predictive in countries where the adoption of MPS is lower (i.e. the United States and Belgium). The authors did not find additional variance explained by individual differences. Regarding the cultural variables, the authors found that individualism moderated the effect of social influence on behavioral intention to use MPS, such that the effect was more substantial among people with individualistic cultural traits. However, the authors found no moderation effect of uncertainty avoidance.

This research contributes to existing work on technology acceptance by exploring whether it is helpful to introduce individual and cultural factors into the UTAUT2 model when predicting technology adoption in different cultures. This research further examines the moderating role of cultural factors in predicting the adoption of MPS. The authors conclude that the UTAUT2 model is generally robust and appears to capture the predictive of face-valid individual and cultural factors.

Facial recognition payment (FRP) has been attracting attention as an alternative payment mode. This research aims to investigate the future use of FRP for both mobile payment and point of sale payment.

The body of information on this topic is promoted by proposing the valence framework, where the authors used relative advantage, initial trust, perceived playfulness and need for uniqueness as positive valence, and perceived risk, technophobia and perceived complexity as negative valence. This study also investigated the moderating effect of personal innovativeness on consumers' behavioral intention to use FRP-based payments. The authors collected data from 392 FRP users from China to test the model. The authors used structural equation modeling (SEM) to evaluate the significant determinants influencing FRP use.

The authors found that relative advantage and privacy risk are the two most influential predictors of FRP use. The findings indicate that personal innovativeness acts as a moderator between negative valence and behavioral intention. This study provides valuable policy guidelines for the mobile or point of sale (POS) payment companies for adding FRP service into their default payment method.

FRP is a relatively new technology that has not received much research attention in information system (IS) literature. Most studies on payment investigated enablers, and less effort has been given to study both enablers and inhibitors together. Furthermore, the authors employed SEM-based analysis to identify the most important factors influencing consumers' future use decisions.

This study aims to analyze the effects of performance expectancy, effort expectancy, social influence and facilitating conditions on the behavioral intention of using mobile health applications, especially during and after the COVID-19 pandemic.

A survey was developed using an online survey platform and distributed to Indonesian consumers for three weeks, and 149 usable responses were obtained. The principal component analysis, linear regression and analysis of variance tests were performed to test the validity and reliability of the measurement model and the hypothesized relationships among constructs.

Surprisingly, unlike previous studies on IT adoption, the findings show that social influence has no significant impact on behavioral intention. Facilitating conditions have a very weak to almost no significant impact on behavioral intention to use mobile health applications.

This research is conducted during pandemic COVID-19 where using mobile health apps is a must. In the future this research can be expanded as comparison study after the pandemic COVID-19 stated.

The result implies that digital technologies adoption intention is strongly affected by performance expectancy and effort expectancy, with performance expectancy as the most significant predictor. Nonetheless, the interaction of performance expectancy, effort expectancy, social influence and facilitating conditions influences behavioral intention significantly. Therefore, social influence and facilitating conditions are still important even with very insignificant effects.

To improve consumers’ behavioral intention to use mobile health applications, application providers should promote mobile health applications as useful telemedicine tools by primarily focusing on the application performance and usage experience.