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This study aims to determine the most similar set of recommendation books to the user selections in LibraryThing.

For this purpose, 30,000 tags related to History on the LibraryThing have been selected. Their tags and the tags of the related recommended books were extracted from three different recommendations sections on LibraryThing. Then, four similarity criteria of Jaccard coefficient, Cosine similarity, Dice coefficient and Pearson correlation coefficient were used to calculate the similarity between the tags. To determine the most similar recommended section, the best similarity criterion had to be determined first. So, a researcher-made questionnaire was provided to History experts.

The results showed that the Jaccard coefficient, with a frequency of 32.81, is the best similarity criterion from the point of view of History experts. Besides, the degree of similarity in LibraryThing recommendations section according to this criterion is equal to 0.256, in the section of books with similar library subjects and classifications is 0.163 and in the Member recommendations section is 0.152. Based on the findings of this study, the LibraryThing recommendations section has succeeded in introducing the most similar books to the selected book compared to the other two sections.

To the best of the authors’ knowledge, itis for the first time, three sections of LibraryThing recommendations are compared by four different similarity criteria to show which sections would be more beneficial for the user browsing. The results showed that machine recommendations work better than humans.

This study aims to evaluate the current situation of the interlibrary loan in Türkiye using interlibrary loan tracking system raw data (2008–2019). Data was analyzed geographically to reveal the effect of geographical distance on the collaboration and thematically to identify the prominent areas/topics in the requests. In addition, the requests were evaluated in terms of the type and age of the university, the size of the collection and the number of library users.

The effect of geographical distance on the collaboration was revealed using VOSviewer. The place numbers and titles of the requests were used for thematic analysis, which was performed using Flourish and VOSviewer. Statistical tests were conducted with SPSS to investigate the effects of factors such as university age and type.

Geographic analysis revealed that the prominent regions are Marmara, Central Anatolia and Aegean Regions, respectively. The fact that these regions host Türkiye’s largest cities with the highest number of universities has been effective in achieving this result. The results of the thematic analysis are in accordance with the literature. Almost half of the requests are from Social Sciences, Language and Literature, History and Law. While the effects of collection size and the number of library users have relatively insignificant effects on requests, age and university type were found to be more relevant.

Interlibrary loan data of this size has not been the subject of an evaluation before in Türkiye. Hence, the results obtained have the potential to influence relevant policies and decisions on the subject. On the other hand, to the best of the authors’ knowledge, bibliographic mapping was used for the first time on interlibrary loan data.

This study aims to describe the design and synthesis of two novel azo and imine chromophores-based dyes derived from two different aldehydes with intramolecular colour matching that are pH sensitive.

The visible absorption wavelength (λ_max) was extended when azo chromophore was included in imine-based systems. The dyed patterns created sophisticated colour-changing paper packaging sensors with pH-sensitive chromophores using alum as a mediator or mordant. Due to the tight adhesive bonding, the dyes on paper’s cellulose fibres could not be removed by ordinary water even at extremely high or low pH, which was confirmed by scanning electron microscopy analysis. The dyed patterns demonstrated an evident, sensitive and fast colour-changing mechanism with varying pH, from pale yellow to red for Dye-I and from pale yellow to brown-violet for Dye-II.

The λ_max for colour changing was recorded from 400 to 490 nm for Dye-I, whereas from 400 to 520 for Dye-II. The freshness judgement of food was checked using actual experiments with cooked crab spoilage, where the cooked crab was incubated at 37 ^oC for 6 h to see the noticeable colour change from yellow to brown-violet with Dye-II. The colour-changing mechanism was studied with Fourier transform infrared (FTIR) spectra at different pH, and thin layer chromatography, nuclear magnetic resonance and FTIR spectroscopy studied the desired structure formation of the dyes. Potential uses for smart packaging sensors include quickly detecting food freshness during transportation or right before consumption.

1. Two novel azo-imine dyes have been synthesized with a pH-responsive effect. 2. The pH-responsive mechanism was studied. 3. The study was supported by computational chemistry using density functional theory. 4. The obtained dyes were used to make pH-responsive sensors for seafood packaging to judge the freshness.

Genetics, a discipline of biology, is one of the most recent and rapidly advancing disciplines in science. This study aims to present a bibliometric analysis of the genetics research output of Iranian authors, map the intellectual structure of these studies and investigate the development path of this literature and the interrelationships among the main topics.

This study searched the Web of Science database for documentation of Iranian-published genetics research published up to 2020. Further, this study used HistCite software to profile and analyze the most cited articles and references and to draw their historiographies.

A database search revealed 21,329 documents that created the study population. The highest cited publications based on the Global Citation Score (GCS) and Local Citation Score (LCS) achieved scores of 602 and 47, respectively. The publication growth rate study demonstrated consistent expansion over time. The scientific maps based on LCS and GCS had five and four clusters, respectively. Furthermore, journal articles emerged as the predominant type of publication.

The significance of this study is in its contribution to understanding the genetics research position in Iran, informing policymakers and researchers, helping scientific collaboration and its impact on public attitudes and quality of life. The results of the present study, with benefits for various groups of communities, such as policymakers, academic groups and public society, can bridge the gap between theoretical research and practical implications.

The results of this study, by helping future advancement in health care, medical genetics and disease prevention, may have a direct and indirect positive influence on the quality of life. Furthermore, it may lead to more informed discussions on health care and biotechnology as well as influencing public attitudes and perceptions.

Ultimately, this study concludes that despite the proliferation of publications in terms of quantity and complexity, especially in areas such as disease diagnosis, prevention and treatment, there remains a need for more attention to other facets of genetics such as biology and biotechnology. Iranian publications are most related to population genetics, human genetics, molecular genetics, medical genetics, genomics, developmental genetics and evolutionary genetics out of 10 branches of genetics. This study reveals patterns in scientific outputs and authorship collaborations and plays an alternative and innovative role in revealing Iranian research trends in genetics.

The health crisis has highlighted the shortcomings of the industry sector which has revealed its vulnerability. To date, there is no guarantee of a return to the “world before”. The ability of companies to cope with these changes is a key competitive advantage requiring the adoption/mastery of industry 4.0 technologies. Therefore, companies must adapt their business processes to fit into similar situations.

The proposed methodology comprises three steps. First, a comparative analysis of the existing CPSs is elaborated. Second, following this analysis, a deep learning driven CPS framework is proposed highlighting its components and tiers. Third, a real industrial case is presented to demonstrate the application of the envisioned framework. Deep learning network-based methods of object detection are used to train the model and evaluation is assessed accordingly.

The analysis revealed that most of the existing CPS frameworks address manufacturing related subjects. This illustrates the need for a resilient industrial CPS targeting other areas and considering CPSs as loopback systems preserving human–machine interaction, endowed with data tiering approach for easy and fast data access and embedded with deep learning-based computer vision processing methods.

This study provides insights about what needs to be addressed in terms of challenges faced due to unforeseen situations or adapting to new ones. In this paper, the CPS framework was used as a monitoring system in compliance with the precautionary measures (social distancing) and for self-protection with wearing the necessary equipments. Nevertheless, the proposed framework can be used and adapted to any industrial or non-industrial environments by adjusting object detection purpose.

Augmented reality is one of the technologies that developed and became widespread, especially after the 2000s. It is seen that innovations such as 3D virtual reality glasses, artificial intelligence-supported smart chatbots that communicate with customers in a virtual environment, keyless hotel systems, the Internet of Things, augmented reality applications, smart personal assistants, digital infrastructure concepts and face recognition systems are used in tourism businesses. The place and use of augmented reality technologies in tourism enterprises are discussed in this study. This study was evaluated from the perspective of UTAUT2. The purpose of this study is to examine the usability of augmented reality technologies in tourism businesses.

Within the scope of the study, interviews were held with augmented reality application developers, digital marketing agencies and accommodation business managers. The obtained data were processed with the MAXQDA analysis program.

It has been determined that augmented reality is a useful and informative technological tool for tourists, it can help sales, promotion and marketing activities, and its usability potency in accommodation businesses is high.

When the literature is reviewed, it is understood that the studies that apply to the opinions of the application developers and the managers of the accommodation establishments are limited. Therefore, this study has been prepared to contribute to the literature in this respect. This study examines and discusses the use of augmented reality technologies in tourism enterprises, the benefits and difficulties they will create from the perspective of application developers and accommodation business managers and their place in tourism enterprises. This study was evaluated from the perspective of UTAUT2. There is a need for research developed using UTAUT models in technology acceptance studies. In this respect, it will contribute to the literature.

The purpose of this study is to reveal the research trends in guest experiences of service robots in the hospitality industry.

In this study, a review was carried out on the Web of Science (WoS) database with the assistance of bibliometric analysis techniques. Cluster analysis was also employed for this to group important data to determine the relationships and to visualize the areas in which the studies are concentrated. The thematic content analysis method was used to reveal on which customer experiences and on which methods the focuses were.

On the subject of experiences of service robots, the greatest number of publications was in 2021. In terms of country, China has come to the fore in the distribution of publications. As a result of thematic content analysis, it was determined that the leading factor was the main dimension of emotional experience. In terms of sub-dimensions, social interactions attracted more attention. Most of the studies discussed were not based on any theory. Apart from these, the Technology Acceptance Model (TAM), the Service Quality Model (SERVQUAL) and Perceived Value Theory (PVT) were featured more prominently among other studies.

In this study, only the WoS database was reviewed. In future studies, it would be possible to make contextual comparisons by scanning other databases. In addition to quantitative research designs, social dimensions may be examined in depth following qualitative research methods. Thus, various comparisons can be made on the subject with mixed-method research designs. Experimental research designs can also be applied to where customers have experienced human-robot interactions (HRIs).

In the hospitality industry, it is critical to uncover every dimension of guests' robot acceptance. This study, which presents the current situation on this basis, guides future projections for the development of guest experiences regarding service robots in the hospitality industry.

This paper explores the orchestration of digital innovation in Industry 4.0 organisations.

The study applies the activity theory to explorative multiple case studies. Observations of innovation activities in five business cases take place at two large international organisations.

The results underline five logics of action that drive digital innovation: (1) digital transformation, (2) technology translation, (3) catalyst agents, (4) digital thread and (5) empowerment. Further, the case study organisations highlight the importance of developing a sustainable culture capable of continuously adopting new technologies, processes and infrastructure that will allow the management of digital innovations.

The study empirically shows the motivations and challenges in orchestrating digital innovation in Industry 4.0 organisations.

Employee engagement (EE) can result in multiple positive impacts not only on the individual and his/her team but also on the organisational and financial outcome of the business. If artificial intelligence (AI) can be used as a tool to facilitate EE, organisations will be more than satisfied to adopt it. The paper aims to study the penetration of AI for EE in corporate India.

Based on the information gathered through secondary research, a framework of questions was built and sent to some senior people in the area of AI and HR to check for its completeness. Respondents based on inclusion criteria were selected through random purposive sampling to be a part of the study. A total of 23 respondents participated in the study. Qualitative data analysis of the transcripts was conducted using MAXQDA 2022 (Verbi Software, Berlin, Germany), which is a qualitative data analysis software. Multiple readings were undertaken to identify the patterns and relationships in the data.

The participants described a variety of issues while using or planning to use AI for EE. Some of the issues mentioned were related to cost, challenges, mindsets and attitudes, demography of employees, comfort in the use of technology, size of the organisation, change management strategies, software vendors and vendor support. The most common responses were grouped into headings such as Organisation, Process, Employee and Software Choice Related aspects.

Lately, the overall work environment, work and personal life balance, and quality of life have become more desirable than earning a good salary. AI is becoming a part of various aspects of business but its role in HR is yet to be explored. AI’s capabilities to predict may result in more employee work satisfaction. The paper explores the possibility of using AI as a tool in every aspect of employee life cycle, thereby attempting to make HR processes more productive and enhance EE.

The main purpose of the study is to reveal the hotel managers' perspectives strategies, and predictions on smart technologies, and their expectations for current staff and potential staff to be employed in the future.

A qualitative research approach was used to reveal the internal perspectives of managers to determine their Smart Tourism (ST) perspectives. With the snowball sampling method, face-to-face interviews were conducted with 16 senior hotel managers working in a 5-star hotel in Antalya. The data obtained were carried out with inductive content analysis and descriptive analysis from qualitative research analysis methods.

In this study, hotel managers' broad perspectives on ST were discussed in depth and presented comprehensively. Managers' perceptions of smart technologies are classified in terms of aggregate dimensions and themes. Among the most important findings, rapidity/quickness, managing/holistic assessment, standardization, harmony/integration-coordination and experience-memory, defined as the crossroads of total dimensions or the heart of smart technologies, were identified as the most important themes. In addition, Stylos et al. (2021), another important finding is the classification of technologies used in top-quality hotels, based on the conceptual framework for smart technologies presented in the literature.

The effective and efficient use of technology, its internalization and openness to technology provides important advantages in hotels such as increasing revisits and satisfaction, providing loyalty and reducing costs. Thus, the perceptions, practices, strategies and prediction of senior managers working in high-level hotels about ST provide valuable data to other hotels that want to survive in the competition.

From a theoretical point of view, this study is valuable in that it deals with the ST perceptions of senior managers with an exploratory research approach. Managerially, the research findings offer valuable contribution about the attitudes, trends, forecasts and expectations of senior managers working in high-level hotels towards ST.