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The Scottish economy was experiencing steady growth prior the hit of the COVID-19, with the pandemic causing the government to announce extreme lockdown measures with unprecedented impact on small and medium-sized enterprises (SMEs). Whilst some of the industry sectors in Scotland took a lighter hit, a large part of the organizations had to either adapt or completely disrupt their business. An essential aspect of their survival, risk management (RM) was among the areas requiring the most significant acceleration. This study compared the RM practices implemented by Scottish SMEs prior and after the outbreak as well as examined the attitudes of key decision-makers in the SMEs in relation to risk, including their perceived readiness for another crisis of similar significance.

An online survey based on ISO 31000 RM guidelines was distributed to 232 Scottish SMEs. Based on the official government reports and existing knowledge on how SMEs around the world have handled crisis events within the past 20 years, the authors developed the hypothesis that crisis events significantly accelerate SMEs' RM implementation. Around 13 items were tested in relation to the hypothesis and responses were tested via two-tailed T-test to establish significant statistical difference.

The research provides insight into the current state of risk management practices implemented by Scottish SMEs. As expected, SMEs showed significant difference in their RM implementation prior and after the COVID-19 outbreak. Whilst this has been viewed as a positive, motivations, priorities and approaches in managing risk demonstrated by the SMEs is questionable with views to their sustainable long-term recovery. The study highlights the lack of confidence instilled within the SMEs that they can handle another crisis of similar significance and provides directions for further investigation and improvements with the aim of helping the SMEs prepare better to mitigate the consequences of future crisis events.

In academic sense, the study offers a tested universal framework and a detailed questionnaire for assessment of RM strategy, applicable to organisations of various type, size and geography. Several implications with regards to managerial practices have been highlighted, including the neglect of the SMEs’ own internal environments and its significance in their risk strategies, the predominantly reactive approach to RM displayed by most Scottish SMEs as well as the neglect of compliance risk leading to potential quality and customer satisfaction issues preventing SMEs from full post-crisis recovery.

In this study, the central objective is to foresee stock market signals with the use of a proper structure to achieve the highest accuracy possible. For this purpose, three hybrid models have been developed for the stock markets which are a combination of support vector machine (SVM) with meta-heuristic algorithms of particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).All the analyses are technical and are based on the Japanese candlestick model.

Further as per the results achieved, the most suitable algorithm is chosen to anticipate sell and buy signals. Moreover, the authors have compared the results of the designed model validations in this study with basic models in three articles conducted in the past years. Therefore, SVM is examined by PSO. It is used as a classification agent to search the problem-solving space precisely and at a faster pace. With regards to the second model, SVM and ICA are tested to stock market timing, in a way that ICA is used as an optimization agent for the SVM parameters. At last, in the third model, SVM and GA are studied, where GA acts as an optimizer and feature selection agent.

As per the results, it is observed that all new models can predict accurately for only 6 days; however, in comparison with the confusion matrix results, it is observed that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the data for stock market of the years 2013–2021 were analyzed; the long length of timeframe makes the input data analysis challenging as they must be moderated with respect to the conditions where they have been changed.

In this study, two methods have been developed in a candlestick model; they are raw-based and signal-based approaches in which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

The proposed model has been aimed to predict stock market signals by designing an accurate model. In this sense, the stock market is analysed by the technical analysis of Japanese Candlestick, which is combined by the following meta heuristic algorithms: support vector machine (SVM), meta-heuristic algorithms, particle swarm optimization (PSO), imperialist competition algorithm (ICA) and genetic algorithm (GA).

In addition, among the developed algorithms, the most effective one is chosen to determine probable sell and buy signals. Moreover, the authors have proposed comparative results to validate the designed model in this study with the same basic models of three articles in the past. Hence, PSO is used as a classification method to search the solution space absolutelyand with the high speed of running. In terms of the second model, SVM and ICA are examined by the time. Where the ICA is an improver for the SVM parameters. Finally, in the third model, SVM and GA are studied, where GA acts as optimizer and feature selection agent.

Results have been indicated that, the prediction accuracy of all new models are high for only six days, however, with respect to the confusion matrixes results, it is understood that the SVM-GA and SVM-ICA models have correctly predicted more sell signals, and the SCM-PSO model has correctly predicted more buy signals. However, SVM-ICA has shown better performance than other models considering executing the implemented models.

In this study, the authors to analyze the data the long length of time between the years 2013–2021, makes the input data analysis challenging. They must be changed with respect to the conditions.

In this study, two methods have been developed in a candlestick model, they are raw based and signal-based approaches which the hit rate is determined by the percentage of correct evaluations of the stock market for a 16-day period.

This case study exposes students to conflicts with distributors, escalated scenarios of a trade association and the possible repercussions of such a scenario. Upon completion of this case study, the students will be able to understand the critical success factors for a distribution setup and alignment of channels for driving growth; understand and manage the power dynamics with a stakeholder, like trade associations, distribution reach, fallacies in managing the distributors and identifying the gaps; critically evaluate negotiation opportunities when a trade association is not directly related to the principal organization.

This case study showcased a conflict between the distributor and Universal Heater Industries (UHI), a leading player in the water heater business in India. In 2015, the global leadership of UHI identified India as an emerging market and undertook a complete management overhaul to implement a new growth plan. Several measures were put in place that leveraged the global product portfolio and new people were appointed to push the agenda. Manish Singhal, the national sales head of UHI, selected Kerala as the pilot state to implement the new plan. However, the projects failed, as the distributor escalated the treatment meted out by UHI to the Electrical Trade Association (ETA). Trade associations have had a history of playing truant with players like UHI, and because of this, business came to a complete halt. The UHI and ETA teams met once; however, the suggested closure by ETA needed to be aligned with UHI’s interests. Singhal’s dilemma deepened, and they had to decide the next steps.

This case study is suitable for a postgraduate marketing course in a segment on managing channels, intermediaries, distribution management and channel conflicts. The uniqueness of this case is in the dimension of the trade association and managing the stakeholders.

Teaching notes are available for educators only.

CSS8: Marketing.

This study aims to provide a systematic review and meta-analysis to explore the strength of the path relationship in the proposed model for online travel booking service adoption. It also explores the moderating effect of national or regional economic development levels and cultural differences.

A meta-analysis was conducted on the factors correlated with users' intention to adopt online travel booking services (OTBS) and the moderating effects of economic development levels and culture based on 42 empirical studies.

Perceived usefulness, perceived ease of use, attitude and perceived behavioral control have a significant positive impact on adoption intention. By contrast, subjective norms have a significantly negative impact on adoption intention. Furthermore, the economic development level of a country or region significantly moderates the relationships between perceived usefulness and perceived behavior control, attitude and perceived behavior control, and subjective norms and adoption intention. At the same time, national or regional cultural differences significantly moderate the relationships between attitudes and adoption intention, perceived usefulness and perceived behavior control, and subjective norms and adoption intention.

This study was conducted to obtain a unified conclusion regarding the research field of online travel booking service adoption. Its content was original. The conclusion provides theoretical references for follow-up research and the development of targeted marketing programs for online travel-booking service providers.

The rapid advancement of technology has transformed the health-care industry and enabled the emergence of m-Health solutions such as health apps. The viability and success of these apps depends on the definition of a monetization model appropriate to their specificities. In this sense, the purpose of this paper is to study the mechanisms of monetization of health apps, to stablish how alternative revenues determine if a health app is to be free or paid.

Probability models are used to identify the factors that explain if a health app is free or paid.

Results show that the presence of alternative monetization mechanisms negatively impacts the likelihood of a health app being paid for. The use of personal data to customize advertising (the monetization of “privacy capital”) or the inclusion of ads on the app are alternative means of monetization with potential to decrease the likelihood of a health app being paid for. The possibility of in-app purchases has a lower negative impact on the probability of a health app being paid for. The choice of platform to commercialize an app is also a strategic decision that influences the likelihood of an app being paid for.

This work stands out for bringing together the two largest platforms present in Portugal and for focusing on the perspective of revenue and monetization of health apps and not on the perspective of downloads.

The evolution of e-health technologies presents promising alternatives for health-care excellence. Despite the benefits arising from mobile e-health (m-health) and wearables technologies, the literature stands many contradictories signs regarding how users accept and engage in using these technologies. This study aims to synthesize the estimations about m-health user acceptance technologies.

A meta-analytic structural equation modeling was carried out using the 778 relationships estimated by 100 previous research. The estimations follow the relations and constructs proposed in the UTAUT2 technological acceptance model.

The results indicate the performance expectancy, effort expectancy, social influence and habit constructs are most important for predicting the behavioral intention of use of m-health technologies. The Latin American users of e-health technologies are still underestimated in the literature.

The study presents a guide to understanding the acceptance process of m-health technologies and delivers a general orientation for developing new m-health devices considering their acceptance by users.

This study aims to empirically explore several factors that encourage muzakki (zakat payers) to pay their zakat through institutions by elaborating on their extrinsic and intrinsic motivations as the composite factors regarding the attitude and intention improvement of muzakki. This study specifically studies zakat payment via digital means and categorizes the muzakki groups into two (urban and suburban) to be considered in the results.

Overall, this study gathers the data from 298 muzakki using a partial least squares technique the multigroup analysis to compare the analysis.

This study found that different sociodemographic aspects will result in varied performances of motivation in using technology between the two groups. Furthermore, positive preference aspects, such as muzakki’s attitude, can be a catalyst in improving their motivation to pay zakat through institutions.

The findings of this study can be used as a foundation to improve the technology-based services that will be more accessible and reachable. Provision of technical follow-ups regarding the utilization of technology, including community-based digital platform socializations, availability of online customer service that will respond to muzakki’s needs and synergy between stakeholders, are the primary obligations that a zakat institution must fulfill.

As far as the researchers are concerned, the studies focusing on the motivational factors and attitude of muzakki as an intervention in paying zakat via institutions are limited in numbers, especially studies on digital payment. In this study, however, classifying the groups into two will help gain a deeper understanding of this topic.

Adoption of Clinical Decision Support Systems (CDSS) is a crucial step towards the digital transition of the healthcare sector. This review aims to determine and synthesise the influential factors in CDSS adoption in inpatient healthcare settings in order to grasp an understanding of the phenomenon and identify future research gaps.

A systematic literature search of five databases (Medline, EMBASE, PsycINFO, Web of Science and Scopus) was conducted between January 2010 and June 2023. The search strategy was a combination of the following keywords and their synonyms: clinical decision support, hospital or secondary care and influential factors. The quality of studies was evaluated against a 40-point rating scale.

Thirteen papers were systematically reviewed and synthesised and deductively classified into three main constructs of the Technology–Organisation–Environment theory. Scarcity of papers investigating CDSS adoption and its challenges, especially in developing countries, was evident.

This study offers a summative account of challenges in the CDSS procurement process. Strategies to help adopters proactively address the challenges are: (1) Hospital leaders need a clear digital strategy aligned with stakeholders' consensus; (2) Developing modular IT solutions and conducting situational analysis to achieve IT goals; and (3) Government policies, accreditation standards and procurement guidelines play a crucial role in navigating the complex CDSS market.

To the best of the authors’ knowledge, this is the first review to address the adoption and procurement of CDSS. Previous literature only addressed challenges and facilitators within the implementation and post-implementation stages. This study focuses on the firm-level adoption phase of CDSS technology with a theory refining lens.

Although much attention has been paid to understanding employee resistance to reform, little study has been done to explore the effect of employee resistance to public service units' (PSUs) reform in China. To address this need, this work aims to investigate the antecedents of employee resistance to PSUs' reform, especially from the perspective of the heterogeneity of the employees' age.

This study considers the PSUs in Harbin, China, as an example and uses survey questionnaires to analyze the factors influencing employees' resistance when PSUs reform. Besides, the authors developed a research model based on the status quo bias theory, the equity-implementation model.

According to the applied research model, employee resistance to PSU change is primarily influenced by perceived switching costs and benefits. According to their age, this survey also confirms how the employees responded to the reform implementation.

The results of this empirical study inform suggestions for the sustainable development of PSUs and organizational transformations. Overall, this work advances the theoretical understanding of employees' resistance to PSUs’ reform, thereby offering practical insights for managing employee resistance during organizational change.

Overall, given that employee resistance emotion exists in an organization, this study offers theoretical and practical implications for change management strategies.