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ChatGPT is a versatile technology with practical use cases spanning many professional disciplines including marketing. Being a recent innovation, however, there is a lack of academic insight into its tangible applications in the marketing realm. To address this gap, the current study explores ChatGPT’s application in marketing by mining social media data. Additionally, the study employs the stages-of- growth model to assess the current state of ChatGPT’s adoption in marketing organizations.

The study collected tweets related to ChatGPT and marketing using a web-scraping technique (N = 23,757). A topic model was trained on the tweet corpus using latent Dirichlet allocation to delineate ChatGPT’s major areas of applications in marketing.

The topic model produced seven latent topics that encapsulated ChatGPT’s major areas of applications in marketing including content marketing, digital marketing, search engine optimization, customer strategy, B2B marketing and prompt engineering. Further analyses reveal the popularity of and interest in these topics among marketing practitioners.

The findings contribute to the literature by offering empirical evidence of ChatGPT’s applications in marketing. They demonstrate the core use cases of ChatGPT in marketing. Further, the study applies the stages-of-growth model to situate ChatGPT’s current state of adoption in marketing organizations and anticipate its future trajectory.

Rural tourism facilities in Poland were very keen on amateur websites to promote their hospitality services from 2000 to 2018. In most cases, the websites were nonprofessional, hosted on free servers and made by family members or friends of the holding. After search engine algorithms changed in 2015–2019, the websites started to go extinct on a large scale; they were deleted and often replaced with a more modern design and a commercial domain. These resources offered a rare opportunity to gain insight into rural tourism, rural changes and socioeconomic and cultural phenomena.

The paper’s objective is to demonstrate with an analysis of archived Polish rural tourism websites that digital cultural artefacts are generated in rural areas. The study was an analysis of selected development attributes of rural tourism websites found in the Internet Archive. The analysis involved those attributes that are important for determining whether a website or content can be considered digital cultural heritage assets.

The conclusions demonstrate that rural digital cultural heritage is a set of digital artefacts created in rural areas with their characteristics. Rural digital artefacts are records of ICT, infrastructure, environmental, cultural and socioeconomic changes.

The “digital assets” of rural areas are yet to be discussed in the context of rural cultural heritage, as a set of artefacts created in these areas and characteristic of them.

This study aims to explore the challenges faced by the tourism and hospitality industry following the COVID-19 pandemic and to propose effective strategies for recovery and resilience of this sector.

The study analysed the challenges encountered by the tourism and hospitality industry post-pandemic and identified key strategies for overcoming these challenges. The study utilised the modified Delphi method to finalise the challenges and employed the Best-Worst Method (BWM) to rank these challenges. Additionally, solution strategies are ranked using the Criteria Importance Through Intercriteria Correlation (CRITIC) method.

The study identified significant challenges faced by the tourism and hospitality industry, highlighting the lack of health and hygiene facilities as the foremost concern, followed by increased operational costs. Moreover, it revealed that attracting millennial travellers emerged as the top priority strategy to mitigate the impact of COVID-19 on this industry.

This research contributes to understanding the challenges faced by the tourism and hospitality industry in the wake of the COVID-19 pandemic. It offers valuable insights into practical strategies for recovery. The findings provide beneficial recommendations for policymakers aiming to revive and support these industries.

Small business enterprises (SBEs) are a very critical element of the micro, small and medium enterprises (MSME) sector especially in the underdeveloped locations of the country. SBEs not only provide employment but also support the local economy by way of their products and services. In modern times, their presence on the digital platform is becoming essential and thus, these types of studies are required to bring to the table the issues and practices from the less represented sections of the sector. This study attempts to understand the responsiveness of the SBEs in Sikkim, India, and Bhutan in terms of their frequency of digital marketing (DM) usage. This study also attempts to understand the relationship between the factors affecting the practice or adoption of DM among the SBEs of Bhutan and Sikkim, India. For this purpose, empirical data were gathered from 320 SBEs from Sikkim and Bhutan representing the various sectors as defined by the national governments of the two countries. The SBEs of Bhutan and Sikkim are not found to be very active on the front of DM. The SBEs in these two locations need to become more active in using the digital platforms and tools to promote their products and services. The industry trends and government support in the form of infrastructural development are expected to play a vital role in orienting the SBEs into the adoption of DM in a better way. This study is crucial for the MSME sector, the government, telecom services providers, and local entrepreneurs.

This study aims to use a novel approach, focusing on the manager’s gender, to explore whether it acts as a differentiator in the following aspects of tourist accommodation companies in Extremadura (Spain): the level of information and communication technology (ICT) specialisation of employees, managers’ knowledge of ICTs and the social media and online tourism platform use intensity of managers.

A questionnaire was sent to 238 accommodation companies. The data collected from the questionnaire were analysed using statistical inference techniques and linear and logistic regression.

In general, ICT specialist profiles are more common amongst the employees of male-led companies. Male managers also use Booking and analyse online feedback more intensively. There appear to be no gender-based differences in terms of the ICT knowledge of managers.

These results highlight issues of major practical interest for the sector’s managers and decision makers, especially in Extremadura. They reveal the digital divide in certain aspects between men- and women-led firms in Extremadura. This finding has important consequences for the sector in terms of competitiveness. It highlights the need to continue working to eradicate gender gaps in digital settings.

The study shows the role of the manager’s gender as a differentiating factor in terms of the existence of specialist ICT profiles and ICT use intensity in tourism companies. To the best of the authors’ knowledge, this study provides the first evidence of such a finding for the tourism sector in general, as well as for the specific case of a rural destination such as Extremadura.

Key performance indicators (KPIs) play a pivotal role in evaluating the level of success of an organization in achieving its business objectives. The objective of the current research is to identify and prioritize effective KPIs in branding products and construction projects, which contribute to the success of construction companies in a competitive environment.

The present research is of an inferential, descriptive and survey nature. In this study, we identified the influential key performance indicators of construction companies in branding products and construction projects for success in a competitive environment through a literature review and expert opinions. The data were collected using a questionnaire, and a combination of the one-sample t-test method with a 95% confidence level and the fuzzy multiple attribute decision-making (FMADM) method was employed for analysis.

The results indicate that the most influential key performance indicators for construction companies in branding products and construction projects for success in a competitive environment are, in order of significance, the following indices: “Marketing and Advertising,” “Financial,” “Creativity,” “Technical and Operational” and “Social and Political.”

The present research examines the importance of branding construction products and projects for the success of construction companies by improving their business objectives and utilizing key performance indicators throughout the product lifecycle (production and construction). This study provides solutions on how construction companies can increase their competitive advantage through branding and achieve long-term success in the global construction industry.

Knowledge management (KM) is an important topic in the age of big data, and this study adds to the existing body of literature by providing a novel KM perspective on the technological phenomenon of artificial intelligence (AI). This study aims to discover how AI might facilitate knowledge-based business-to-business (B2B) marketing. In this chapter, the authors take a close look at the building blocks of AI and the relationships between them. Future research directions and also the effects of the various market information building components on B2B marketing are discussed. The study’s approach is theoretical; it tries to provide a framework for characterising the phenomenon of AI and its constituent parts. Additionally, this chapter provides a methodical analysis of the three categories of market information crucial to B2B marketing: knowledge of customers, knowledge of users, and knowledge of external markets. This research looks at AI through the lens of the conventional data processing framework, analysing the six pillars upon which AI systems are founded. It also explained how the framework’s components work together to transform data into actionable information. In this chapter, the authors will look at how AI works and how it can benefit B2B knowledge-based marketing. It’s not aimed at AI experts but rather at general marketing managers. In this chapter, the possible effects of AI on B2B marketing are discussed using examples from the real world.

The COVID-19 situation affected the whole landscape of retailing in India and around the world. However, some businesses have used the pandemic-related difficulties into opportunities. E-tailing is one of the ways that helped people in India to continue shopping their essential products and choosing their luxury products without making any physical visits during the lockdown. This research understands the current situation through an observation study and suggests the e-tailing model suitable during the COVID-19 and beyond.

We used secondary data to make the observational study. We also conducted two case studies and interviews with grocery shops and an automotive company.

This research suggests a simple collaborative e-tailing model combining all supply chain players to reduce people’s movement, timely delivery and enhanced service to meet customers demand during the lockdown period.

This paper has considered two real cases for discussion and also obtained information from public domain. The proposed model has been discussed with the case companies, and it hoped to support business planning for online services.

The purpose of this study is to conduct a systematic literature review of the adoption of immersive marketing technology (IMT) in terms of strategic planning of its adoption, resource requirements and its implications and challenges.

This study categorizes and contextualizes qualitative approaches to evaluate the literature, with Scopus databases serving as the primary source of 90 selected articles in the areas of information technology, business and marketing strands. Theme analysis was carried out using thematic techniques and grounded approach principles to facilitate thematic coding and generate theme analysis.

The analysis was supported by the three concepts of business flexibility, agility and adaptability, which were drawn as a strategy for IMT adoption. The findings presented three main themes: proactive flexibility, responsive agility and reactive adaptability that enable business owner–managers to craft a strategy for IMT adoption.

The novel contribution of this study is the inclusion of key implications related to IMT as a starting point of the next level of innovative marketing for all academics, practitioners and business owner–managers.

Prefabricated building has been widely applied in the construction industry all over the world, which can significantly reduce labor consumption and improve construction efficiency compared with conventional approaches. During the construction of prefabricated buildings, the overall efficiency largely depends on the lifting sequence and path of each prefabricated component. To improve the efficiency and safety of the lifting process, this study proposes a framework for automatically optimizing the lifting path of prefabricated building components using building information modeling (BIM), improved 3D-A* and a physic-informed genetic algorithm (GA).

Firstly, the industry foundation class (IFC) schema for prefabricated buildings is established to enrich the semantic information of BIM. After extracting corresponding component attributes from BIM, the models of typical prefabricated components and their slings are simplified. Further, the slings and elements’ rotations are considered to build a safety bounding box. Secondly, an efficient 3D-A* is proposed for element path planning by integrating both safety factors and variable step size. Finally, an efficient GA is designed to obtain the optimal lifting sequence that satisfies physical constraints.

The proposed optimization framework is validated in a physics engine with a pilot project, which enables better understanding. The results show that the framework can intuitively and automatically generate the optimal lifting path for each type of prefabricated building component. Compared with traditional algorithms, the improved path planning algorithm significantly reduces the number of nodes computed by 91.48%, resulting in a notable decrease in search time by 75.68%.

In this study, a prefabricated component path planning framework based on the improved A* algorithm and GA is proposed for the first time. In addition, this study proposes a safety-bounding box that considers the effects of torsion and slinging of components during lifting. The semantic information of IFC for component lifting is enriched by taking into account lifting data such as binding positions, lifting methods, lifting angles and lifting offsets.