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This study aims to explore the influence of creative tourist experiences (CTE) on tourists’ recommendation intentions (RCI) toward creative tourist destinations. This study examines the roles of tourist engagement and satisfaction in this relationship, proposing a sequential mediation model based on the cognitive-affective-conative model.

The research surveyed travelers to Kinnaur, Himachal Pradesh, India, using a structured questionnaire. Through purposive sampling data were collected from 413 generation Z (Gen Z in short) tourists. The study used exploratory factor analysis, confirmatory factor analysis and sequential mediation analysis. The present research used Model 6 by Andrew Hayes in the SPSS process macro to test the serial mediation.

The findings emphasize the importance of active engagement in creative tourism and its potential to transform cultural heritage into enriching experiences, thereby influencing tourists’ recommendation behaviors. Results of sequential mediation analysis show that there is a significant and positive impact of CTE on tourist’s RCI. Further, results showed the sequential mediation effect of tourist engagement and satisfaction on the relationship between CTE and RCI.

The findings offer valuable insights for destination managers and policymakers in creative tourism. Enhancing tourist engagement and satisfaction through creative experiences can effectively increase RCI, contributing to the sustainable management of cultural resources and preventing the over-commercialization of cultural heritage.

This research contributes to the literature on creative tourism by empirically testing the sequential mediation model and highlighting the pivotal role of tourist engagement and satisfaction in transforming creative experiences into positive behavioral outcomes. This study provides a nuanced understanding of the factors influencing tourists’ RCI in creative tourism contexts.

The purpose of this paper is to understand a new restaurant venture's target segment and create a consumer profile for the new restaurant; to design a positioning statement for the new restaurant; to appraise the marketing strategy and suggest improvements in the marketing mix of a new restaurant venture in the new normal; to discuss the augmentation of services by a new restaurant to compete effectively in the market; and to identify and discuss the vital marketing steps for opening a restaurant in the new normal.

Kelvin, an ambitious and budding restaurateur, had high aspirations with great plans. V café was his first running venture. The income from V café was not enough to improve his social position. He wanted to open a new restaurant (Haikou) and earn more. Kelvin was well aware of COVID-19's current condition and its severe implications for the restaurant business. He did not have any experience in marketing a restaurant. So he was puzzled about understanding the target segment, positioning and marketing mix of the proposed restaurant in the new normal.

The case will cater to business management students pursuing a postgraduate management program. The case can be applied in Marketing Management, Entrepreneurship, Hospitality Management and Services Marketing courses. The prerequisite for this case is a basic understanding of marketing concepts.

Supplementary materials teaching notes are available for educators only.

CSS 8: Marketing

This study describes a short-term project designed for students to develop important skills needed for artificial intelligence (AI) literacy and to understand how the concepts of AI literacy may be viewed in different countries.

This mixed-methods study sets out to investigate students’ perceptions on the use of AI-generated communication in the workplace, particularly the use of ChatGPT for business communication. We discuss student responses to questions regarding AI-generated business communication according to the diamond model of AI literacy.

This study’s findings reflect students’ awareness of the use of AI at their workplace, their perceptions of the benefits and challenges of AI-generated business communication, as well as their perceived need for institutionalizing policies at their respective companies. The study reports on potential differences in cultural attitudes regarding generative AI.

This study involved a project with a limited sample. It involved students in the United States of America, Germany and India and was replicated. Despite the sample size, we feel it has relevance for underlining opportunities for AI literacy skills in higher education (HE) institutions, especially in terms of heightening awareness of cultural appropriateness, sensitivity towards data privacy needs and developing students' intercultural communication skills.

The following study addresses the need for institutions of HE to develop students’ AI literacy, including how concepts of AI literacy may be viewed differently and how AI-generated business communication must be adapted to suit diverse environments. The study encourages instructors to incorporate AI tools in their curricula by illustrating one best use-case for developing students’ abilities to utilize this technology for professional business communication.

A major concern for policymakers and researchers is to ascertain the movement of price levels and employment rates. Predicting the trends of these variables will assist the government in making policies to stabilize the economy. The objective of this chapter is to forecast the unemployment rate and Consumer Price Index (CPI) for the period 2022 to 2031 for the Indian economy. For this purpose, the authors analyse the prediction capability of the univariate auto-regressive integrated moving average (ARIMA) model and the vector autoregressive (VAR) model. The dataset for India's annual CPI and unemployment rate pertains to a 30-year time period from 1991 to 2021. The result shows that the inflation forecasts derived from the ARIMA model are more precise than that of the VAR model. Whereas, unemployment rate forecasts obtained from the VAR model are more reliable than that of the ARIMA model. It is also observed that predicted unemployment rates hover around 5.7% in the forthcoming years, while the forecasted inflation rate witnesses an increasing trend.

We study the performances of various predictive models including decision trees, random forests, neural networks, and linear discriminant analysis on an imbalanced data set of home loan applications. During the process, we propose our undersampling algorithm to cope with the issues created by the imbalance of the data. Our technique is shown to work competitively against popular resampling techniques such as random oversampling, undersampling, synthetic minority oversampling technique (SMOTE), and random oversampling examples (ROSE). We also investigate the relation between the true positive rate, true negative rate, and the imbalance of the data.

This aim of the research is to explore the link between offline and online retail sales through a comprehensive analysis of 15 years of retail sales data in Great Britain. By quantifying the impact of online sales on offline sales and vice versa on different retail categories, the study seeks to expand upon prior research that often focused on unidirectional relationships or specific retail segments. With channel- and sector-specific dynamics, the present study aspires to shed light on the dynamics shaping the retail industry and provide valuable insights for retailers.

The present study draws on 15 years of retail sales data in Great Britain to examine the interplay between offline and online sales across various retail categories. By grounding the analysis in the theoretical underpinning of cross-channel synergies and dis-synergies and applying principles of substitution and complementarity in online and offline retail sales, the research aims to quantify how shifts in online retail influence offline sales and vice versa. The study follows a systematic analysis approach that includes analysis of seasonality, autocorrelation, correlation, lagged cross-correlation and time series regression to provide a holistic understanding of the strength and direction of the relationship.

Among the myriads of findings, the most noteworthy ones are these. It was found that offline sales tend to increase significantly when online sales increase and online sales also increase significantly when offline sales increase. However, the extent of influence offline sales have on online sales is lower than the extent of influence online sales have on offline sales. For instance, it was found that for every one-unit increase in online sales, on average, the offline sales of retail stores increase by 5.82 units, but for every one-unit increase in offline sales, on average, the online sales of retail stores increase only by 0.07 units. This pattern was found true for all categories for retail stores, with predominantly non-food stores showing the lowest increase (0.02) and non-store retails exhibiting the highest increase (0.23). Additionally, evidence suggested that the retail stores can expect a sizable level of offline sales when there are no online sales (β₀ = 5155474.1); however, this was not true for online sales if they have no offline sales.

The study deepens our insight into the complex interplay between offline and online sales in the retail sector by providing empirical evidence on how online sales and offline sales interact with each other in a retail setting. It also adds nuances to existing knowledge on the relationship between online and offline sales based on channel and sector-specific dynamics. Such findings, coming from the analysis of a whole retail economy, strengthen the existing body of literature and provide a solid foundation for future research in this area. However, the study would have benefited from including qualitative interviews of different categories of retailers to provide more context and depth on interpreting quantitative patterns.

The study provides practical guidance for retailers on how they can optimize their omnichannel strategies for maximum impact. It also provides insights into the changes in relationship in terms of channel-specific and sector-specific differences. This is particularly important for retailers who operate in those categories of retail as it provides them with an insight into how they can allocate their resources for reaping substantial benefits.

Despite the wealth of research exploring the link between online and offline retail sales, there remains a gap in understanding the complex relationship between these two. This study is one of the first to fill this gap by exploring the link through a comprehensive analysis of 15 years of retail sales data in Great Britain. With these channel- and sector-specific findings, this study adds to the growing body of literature that provides an understanding of the bidirectional relationships between offline and online sales. These strengths strengthen the existing body of literature and provide a solid foundation for future research in this area.

Oxidative rancidity is a major problem in chicken meat because of its higher content of polyunsaturated fatty acids. Besides this, microbial contamination is also one of the major problems of chicken meat which leads to deterioration in quality during storage. Therefore, the purpose of this paper is to investigate anti-microbial and anti-oxidant effects of various blends of essential oils (EOs) on the quality of emulsion-based (ready-to-eat) chicken sausages.

Sausages were incorporated with optimum level of four different blends of EOs containing six different EOs (Clove oil, Holybasil oil, Thyme oil, Cassia oil, Ajowan oil and Beetel oil), namely, Blend-1 (0.25 per cent), Blend-2 (0.25 per cent), Blend-3 (0.25 per cent) and Blend-4 (0.125 per cent); vacuum packaged and stored at −18±1°C for 60 days. Duplicate samples were taken for each parameter, and three trials were conducted for each experiment, total being six observations (n=6) for consistency of the results.

Significant decrease (p<0.05) in pH of control products was observed at each interval of storage period; however, in case of treatment products, significant decrease (p<0.05) was noticed from day 30 onwards. Blend-2 was observed with significantly lower (p<0.05) thio-barbituric acid reacting substances followed by Blend-1. Significantly lower (p<0.05) total phenolics content was observed in Blend-4 products as compared to other treatments. Regarding DPPH activity, control products showed significant decrease (p<0.05); however, in case of treatment products, DPPH activity showed significant (p<0.05) decrease after day 15 of storage. Microbial count increased with progressive storage period; however, the counts were well below the permissible limit of frozen meat products. All the blend incorporated products received very good sensory scores in consistent manner.

The work under this study would be very useful for the meat processing industries dealing with the perishable meat products. Use of natural anti-oxidants will also restore faith towards the consumers who are becoming more health conscious day by day.

The research work is original.

Purpose: In recent times, ‘artificial intelligence (AI)’ has been pervasive even in organisations or at home. AI is defined as programming computers or other technological devices to act, react, respond, or assist the same way humans do. AI has undeniably made people’s lives easier. In organisations, the impact of AI is even more visible. The main aim of this chapter is to examine the significant role of future work skill’s (FWS) each component in the field of on-growing automation. The focus will be especially on emotional and social intelligence (ESI) (a key component of FWS) while adopting AI.

Need of the Study: In terms of human resource management (HRM), AI is useful for people management, payroll services, staff monitoring and improving the recruiting network, among other things. Even managers put their organisation’s job openings on the web and get applicant resumes electronically. People and employees in the organisation have become more advanced and innovative due to AI. A device obtains employee attendance, and human resource (HR) can track their employees and their organisation’s workforce data. HR has now been awarded more authority to manage and fix their employee’s problems because of AI. In a rapidly changing world, AI is affecting all aspects. AI is yearning to automate all of the jobs.

Methodology: Now a question arises how we can stay relevant in AI economic development? As humans, we learned that every issue is a problem of optimisation because we simply require human skills to develop, create and innovate new things. Therefore, researchers recognised that adopting sustainable growth skills encourages people to continue learning throughout their lives. Moreover, AI has enabled machines the ability to learn over time. Still, they will never be able to develop new ideas like human intelligence. A machine can use only one fixed data algorithm. Now humans have made significant progress in various fields with the help of FWS; without integrated computer sciences, brain science would not make such an outstanding achievement. On the other hand, human minds are masters of their intelligence, such as creativity, complex problem-solving, cognitive thinking, ESI and communication. Breakthrough human mind are masters of algorithms represented people have to understand new trends of technology around us, and the best way to move forward is to be aware, adapt and update skills.

Practical Implications: However, AI is required because, regardless of technological advancements, AI is leading Industry 4.0. The industry’s transformation is in 4.0, and hopefully, 5.0 will jump on board soon. Undoubtedly, AI should streamline the process and eliminate redundancy or administrative tasks.

Finding: AI can be more effective in organisations if they incorporate other FWS, particularly the soft human ESI skills, whereas AI is present everywhere, we can still not neglect FWS, especially ESI. So, this chapter highlights the important role of soft skills, that is, ESI and FWS, while adapting AI for an effective HRM.

The advancement of technology is fast, disruptive, and currently known as the new civilization of the Industrial Revolution 5.0. It causes changes in the human lifelines in their economy, politics, culture, and social aspects. Particularly, third world nations and developing countries need to prepare the alertness for this adjustment, especially for the young generation. International political events will come, and the developed countries will crush the other countries those are unable or not having the skill to manage technology and innovate learning process. It is following the proverb of who controls the technology will master the resources. Global advancements and its dynamics in the era of Industry 4.0 and Society 5.0 have impacted the education system in South Asian and South East Nations specifically due to changing characteristics of learning. The objective of this study is to investigate the characteristics of learning in the era of Industry 4.0 and Society 5.0, specifically in the master’s program of advertising and public relation of a private university in Indonesia. This study adopted a qualitative approach, through the technique of focus group discussion (FGD) to obtain the result on the basis of thematic analysis. There were 12 participants of the FGD representing various groups of stakeholders consisting of students, teachers, and leaders. The results of this FGD indicate that learning characteristics in the era of Industry 4.0 and Society 5.0 are swiftly transforming and there is a vital need of accommodation and acceptance by learner and instructor during dissemination of knowledge. Overloading of technology has become a blockage too during the facilitation of an educational activity in lack of appropriate awareness and training.

Motivated by the severe impact of global supply chain disruptions and the lack of understanding of supply chain resilience from an SME exporter perspective in the international marketing literature, this paper sets out to explore how SME exporters achieve resilience through strategic choices related to sourcing and manufacturing during global disruptions.

The study adopts a qualitative, multiple-case-studies approach to identify the key strategic drivers and contextual factors influencing SME exporters’ supply chain resilience. Our study adopted an SME exporter perspective and featured an unprecedented global supply chain disruption context. New Zealand (NZ) was chosen as the key home country context for the participants of this study. Five NZ SME exporters were selected for our case studies.

This study redefines buffering, bridging and a mixed strategy for an SME exporter facing global supply chain disruptions. SME exporters with high situational control are likely to pursue a buffering strategy that reduces their resource dependence on foreign suppliers and their reliance on information support from these suppliers. In contrast, when the firm perceives little control over the supply chain, it will decide upon a bridging strategy that aims to build solid relationships with supply chain partners. Exporters opt for a mixed strategy when they do not see themselves clearly in high or low situational control.

As global disruptions arise and evolve, supply chain uncertainty and exporters’ sense of situational control manifest differently across industries, product lines, markets and sourcing countries. It is essential to understand that firms can choose a mixed strategy based on these supply chain conditions. While our study was fortuitously timed, conducting multi-country studies could provide more comparative insights that transcend national borders. Additionally, our study did not examine organizational and capability-based factors. Future research may benefit from exploring how an SME exporter develops strategic capabilities to achieve resilience over its lifetime.

Businesses see survival as the most pivotal concern during a global supply chain disruption. Many companies have had to make on-the-spot decisions about whether they should shift or redesign their supply chains in the middle of a global disruption. There is no “best strategy” for an SME exporter to take. Rather, managers should make strategic decisions based on how much control or influence they have over a particular part of their supply chain. The level of control is determined by the SME exporter’s overall resource dependencies and information needs in particular parts of their supply chain.

We adopt resource dependence theory and information processing theory to guide our study and place exporters' situational control in the centre of drivers to firms' strategic choices during global disruptions. We make a novel attempt to incorporate the contextual conditions of the COVID-19 pandemic into the theorization of supply chain resilience. We make managerial recommendations to help SME exporters navigate global supply chain disruption challenges.