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The study aims to investigate the time for social media posts and reviews in order to determine the best timing to ensure maximum outreach and interactions from users. The study intends to analytically investigate a company's Facebook and Instagram pages to get meaningful insights for effective post management.

“Great Deal Tires” company’s Facebook and Instagram business pages were taken as the case study and patterns and analytical insights for different posts using Facebook and Instagram analytics were identified. The study categorically selected posts from Facebook pages and Instagram pages that were posted at different times and assessed on their impact.

According to the study, Tuesday and Wednesday have higher engagement on the Great Deal Tires Facebook page, while Friday has higher engagement on Instagram. The study also provided valuable insights into post content and timing in order to increase the marketing impact of the posts.

The study provides an analytical framework for analyzing post and review timing on various company business pages, allowing marketers to initiate more user visits and interactions.

This study examines the role of Saudi women’s work motivational drivers on work–home conflict (WHC) and work–home enrichment (WHE). It considers the moderating role of psychological detachment from work during the evening between one workday and the next and how it impacts on the mediating role of WHC and WHE on work performance.

Drawing on self-determination theory and enrichment and depletion perspectives on the work–home interface, the study employs a quantitative lagged study over a period of three consecutive working days, with data collected from 358 Saudi women working in academia.

Moderated mediation structural equation modelling (MMSEM) analysis reveals that intrinsic motivation is significantly, positively related to WHE and home–work enrichment (HWE), whereas extrinsic motivation is only significantly and positively related to home–work conflict (HWC). Results show that detaching from work weakens the positive association between intrinsic motivation and HWE. In line with predictions, WHE is significantly, positively related to citizenship behaviour towards both individuals and organizations, whilst HWE is significantly related to citizenship behaviour towards individuals only. HWC is only significantly related to citizenship behaviour towards the organisation.

The research highlights the enriching experiences for Saudi women, who have the opportunity to pursue a career and highlight benefits of their ongoing empowerment.

This research brings a unique motivational perspective to work–life research and addresses a shortcoming of work–life research in considering an understudied culturally distinct population. It emphasises on the enriching experiences of Saudi women who have the opportunity to pursue a career, highlighting the benefits of their ongoing empowerment.

The purpose of this study is to analyse the brand communication on social media (SM) made by two- and three-starred restaurants and the customer reaction in terms of engagement effects during a crisis. The research highlights the connections between brand communication and engagement dynamics on Instagram by looking for differences in the strategies of two and three-starred restaurants and by highlighting the changes in the background engagement drivers.

Using data collected from 5,666 Instagram posts by 34 Italian Michelin-starred restaurants, the authors analysed the crisis-driven changes in online communication and customer engagement comparing three phases of the COVID-19 pandemic by applying a linear regression model with fixed effects.

Michelin-starred restaurants changed their strategies of brand communication to overcome the effects of the crisis. The findings highlight the importance of SM as a tool to stay in touch with consumers and the pivotal role of customers in engagement, especially during a pandemic.

To the best of the authors’ knowledge, this is among the first studies to investigate the changes in brand communication and the effects on customer engagement during a pandemic, with a focus on Instagram. It contributes to understanding the role of platform and the main drivers of engagement on Instagram, as well as suggesting how managers can improve brand value using SM.

Online labour markets (OLMs) have recently become a widespread phenomenon of digital work. While the implications of OLMs on worker well-being are hotly debated, little empirical research examines the impact of such work on individuals. The highly competitive and fast-paced nature of OLMs compels workers to multitask and to perform intense technology-enabled work, which can potentially enhance technostress. This paper examines the antecedents and well-being consequences of technostress arising from work in OLMs.

The authors draw from person–environment fit theory and job characteristics theory and test a research model of the antecedents and consequences of worker technostress in OLMs. Data were gathered from 366 workers in a popular OLM through a large-scale online survey. Structural equation modelling was used to evaluate the research model.

The findings extend existing research by validating the relationships between specific OLM characteristics and strain. Contrary to previous literature, the results indicate a link between technology complexity and work overload in OLMs. Furthermore, in OLMs, feedback is positively associated with work overload and job insecurity, while strain directly influences workers' negative affective well-being and discontinuous intention.

This study contributes to technostress literature by developing and testing a research model relevant to a new form of work conducted through OLMs. The authors expand the current research on technostress by integrating job characteristics as new antecedents to technostress and demonstrating its impact on different types of subjective well-being and discontinuous intention. In addition, while examining the impact of technostressors on outcomes, the authors consider their impact at the individual level (disaggregated approach) to capture the subtlety involved in understanding technostressors' unique relationships with outcomes.

This study aims to deepen the understanding of the challenges and implications entailed by deploying mobile clinics in conflict zones to reach populations affected by violence and cut off from health-care services.

This research combines an integrated literature review and an instrumental case study. The literature review comprises two targeted reviews to provide insights: one on conflict zones and one on mobile clinics. The case study describes the process and challenges faced throughout a mobile clinic deployment during and after the Iraq War. The data was gathered using mixed methods over a two-year period (2017–2018).

Armed conflicts directly impact the populations’ health and access to health care. Mobile clinic deployments are often used and recommended to provide health-care access to vulnerable populations cut off from health-care services. However, there is a dearth of peer-reviewed literature documenting decision support tools for mobile clinic deployments.

This study highlights the gaps in the literature and provides direction for future research to support the development of valuable insights and decision support tools for practitioners.

Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.

During the planning and delivery, iron triangle criteria, are essential for internal stakeholders (e.g. owner, sponsors and delivery company), mostly ignoring external stakeholders such as local communities (often perceived as inconvenient) or end users. In the medium-long term, infrastructure cost and benefit are far more important for external stakeholders and the environment.

The iron triangle criteria, i.e. delivering on time, budget and quality/scope, is the traditional perspective to assess the success of infrastructure projects. Delivering on cost and time is significant, but particularly for infrastructure, there are more relevant success criteria. The authors argue which criteria are important, and explain why.

The authors challenge the traditional view of judging projects based on respecting time, budget and quality/scope. The authors explain that discussing the social value and contribution to achieving the UN Sustainable Development Goals (SDGs) is extremely relevant. Crucially these metrics keep changing, even after the project is terminated.

The authors provide a new seven-step action plan for decision-makers to improve infrastructure provision by reflecting on SDGs and engaging with external stakeholders, particularly minorities and the weaker members of their communities. Such an action plan is focused on the cost and value for different stakeholders on different timeframes and progress toward social value and achieving SDGs.

By extending Edmans et al.’s (2021) music sentiment measures to the Vietnam market, the authors aim to investigate the impacts of music sentiment on stock market returns and volatility.

The authors adopted Edmans et al.’s (2021) music-based sentiment to proxy for investor mood. The current study uses linear regression analysis.

The authors find that music sentiment is significantly and positively related to both stock returns and stock market volatility. The authors also show that music sentiment has a contagious effect: Global music sentiment and those in the United States, France and Hong Kong are significant drivers of the Vietnamese stock market. The authors also examine the effect on different industry returns and find that returns on stocks of firms in the communication services, consumer discretionary, consumer staples, energy, financials, healthcare, real-estate, information technology and utility sectors are significantly related to music sentiment. In addition to valence, the authors find that other Spotify audio features can be used to quantify music sentiment.

This study contributes to the behavioral finance literature that focuses on investor sentiment. The authors address this topic in Vietnam using high-frequency data.

Robotic process automation (RPA) has recently emerged as a technology focusing on the automation of repetitive, frequent, voluminous and rule-based tasks. Despite a few practical examples that document successful RPA deployments in organizations, evidence of its economic benefits has been mostly anecdotal. The purpose of this paper is to present a step-by-step method to RPA investment appraisal and a business case demonstrating how the steps can be applied to practice.

The methodology relies on design science research (DSR). The step-by-step method is a design artefact that builds on the mapping of processes and modelling of the associated costs. Due to the longitudinal nature of capital investments, modelling uses discounted cashflow and present value methods. Empirical grounding characteristic to DSR is achieved by field testing the artefact.

The step-by-step method is comprised of a preparatory step, three modelling steps and a concluding step. The modelling consists of compounding the interest rate, discounting the investment costs and establishing measures for comparison. These steps were applied to seven business processes to be automated by the case company, Estate Blend. The decision to deploy RPA was found to be trivial, not only based on the initial case data, but also based on multiple sensitivity analyses that showed how resistant RPA investments are to changing circumstances.

By following the provided step-by-step method, executives and managers can quantify the costs and benefits of RPA. The developed method enables any organization to directly compare investment alternatives against each other and against the probable status quo where many tasks in organizations are still carried out manually with little to no automation.

The paper addresses a growing new domain in the field of business process management by capitalizing on DSR and modelling-based approaches to RPA investment appraisal.

This systematic review paper aims to investigate seasonal ambient change’s impact on the incidence of falls among older adults.

The population, exposure, outcome (PEO) structured framework was used to frame the research question prior to using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis framework. Three databases were searched, and a total of 12 studies were found for inclusion, and quality appraisal was carried out. Data extraction was performed, and narrative analysis was carried out.

Of the 12 studies, 2 found no link between seasonality and fall incidence. One study found fall rates increased during warmer months, and 9 of the 12 studies found that winter months and their associated seasonal changes led to an increase in the incidence in falls. The overall result was that cooler temperatures typically seen during winter months carried an increased risk of falling for older adults.

Additional research is needed, most likely examining the climate one lives in. However, the findings are relevant and can be used to inform health-care providers and older adults of the increased risk of falling during the winter.