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This paper investigates the short-term market impact of the beginning of the military conflict between Russia and Ukraine (February 24, 2022) on a set of airline stocks listed.

This study uses an event study methodology, cross-section analyses and interaction effects to study the effect of the war on airline stock prices and firm-specific characteristics that explain the cumulative abnormal return.

The authors observe a negative and statistically significant stock price reaction at and around the beginning of the military conflict between Russia and Ukraine, for 74 listed airlines. These results are consistent with investment portfolio rebalancing and asset pricing perspective. Moreover, this study's results show a higher negative stock market reaction for airlines based in Europe. Empirical evidence suggests the existence of a “proximity penalty” for European companies. Finally, this study's results provide insights into which airline-specific characteristics emerge as value drivers. Larger, well-capitalized (high liquidity and low debt) and profitable airlines firms with less institutional ownership have superior stock market returns and show more able to handle with the losses resulting from the war.

This paper fills a gap in the literature about the impact of the Russia–Ukraine war on the airline industry.

This study investigates barriers to social impact bond (SIB) implementation through a review of academic and gray literature. A SIB is a type of public policy instrument that leverages payment for performance (P4P), contracting together with private investments in the delivery of welfare programs. Outcome-based contracts, such as SIBs, are gaining attraction for public service providers in developed countries, but research regarding their implementation remains underexplored both empirically and theoretically.

A literature review is conducted in which two types of documents are included: (1) empirical research papers and (2) evaluations of completed SIB projects. In total, 43 documents have been investigated. The study engages in a comparative design where insights across sectors (healthcare, social care and employment/education), are leveraged. The insights rest on evidence from the UK and US.

The investigation reveals five types of barriers to SIB implementation related to: (1) the SIB model, (2) organizational competencies, (3) data infrastructure, (4) stakeholder engagement and (5) the institutional context. The study discusses ways of managing these barriers and develops a conceptual framework for empirically investigating SIB implementation.

This study is the first academic paper to systematically assess insights regarding the implementation of SIBs. Also, the article proposes a conceptual framework for investigating SIB implementation.

At the virus' US epicenter, New York City, teachers experienced the impact of the pandemic firsthand in real time. Consistent with intensification (Apple, 1986), as school struggles to adapt to a rapidly changing social and educational landscape, socioemotional stressors and occupational responsibilities increase. Through the metaphoric (Craig, 2018) image of a candle, and using the tools of narrative inquiry (Connelly & Clandinin, 1990) – broadening, burrowing, storying, and restorying – we surface four teachers' lived experiences in a year filled with incredible grief and loss, socio-political-cultural trauma, racial strife, and personal-professional challenges to show their resolve and resiliency to persevere through and beyond burning out.

The authors argue that privacy is integral to the well-being of consumers and an essential component in not only corporate social responsibility (CSR) but what they term uniquely as social media responsibility (SMR). A conceptual framework is proposed that delineates the privacy issues companies should pay attention to in artificial intelligence (AI)-fueled social media environments.

The authors review literature on privacy issues in social media and AI in the academic and practitioner literatures. Based on the review, arguments focus on the need for an SMR framework, proposing responsible use of consumer data that is attentive to consumers' privacy concerns.

Implications from the framework are a path forward for social media companies to treat consumer data more fairly in this new environment. The framework has implications for companies to reduce potential harms to consumers and consider addressing their power and responsibility. With social media and AI transforming consumer behavior so profoundly, there are a variety of short- and long-term social implications.

Since AI tools are becoming integral to social media company activities, this research addresses the changing responsibilities social media companies have in securing consumers' data and enabling consumers the agency to protect their privacy effectively. The authors propose an SMR framework based on CSR research and AI tools employed by social media companies.

This study is motivated to provide a theoretically informed, data-driven assessment of the consequences associated with the participation of non-human bots in social accountability movements; specifically, the anti-inequality/anti-corporate #OccupyWallStreet conversation stream on Twitter.

A latent Dirichlet allocation (LDA) topic modeling approach as well as XGBoost machine learning algorithms are applied to a dataset of 9.2 million #OccupyWallStreet tweets in order to analyze not only how the speech patterns of bots differ from other participants but also how bot participation impacts the trajectory of the aggregate social accountability conversation stream. The authors consider two research questions: (1) do bots speak differently than non-bots and (2) does bot participation influence the conversation stream.

The results indicate that bots do speak differently than non-bots and that bots exert both weak form and strong form influence. Bots also steadily become more prevalent. At the same time, the results show that bots also learn from and adapt their speaking patterns to emphasize the topics that are important to non-bots and that non-bots continue to speak about their initial topics.

These findings help improve understanding of the consequences of bot participation within social media-based democratic dialogic processes. The analyses also raise important questions about the increasing importance of apparently nonhuman actors within different spheres of social life.

The current study is the first, to the authors’ knowledge, that uses a theoretically informed Big Data approach to simultaneously consider the micro details and aggregate consequences of bot participation within social media-based dialogic social accountability processes.

Despite extensive research on supply chain management (SCM), the literature lacks a perspective to empirically assess the importance of poverty alleviation in social business. Using resources dependence theory, the purpose of this study is to analyze to ascertain whether financial innovation has a powerful solution for business sustainability and, hence, poverty alleviation in developing countries. This study reviews the financial innovations offered by Dompet Dhuafa Republika in integrated supply chain management (ISCM) of smallholder livestock business through Tebar Hewan Kurban (Spreading Sacrificial Animals) program to overcome capital and marketing problems at the farmer level and distribute Qurban meat to the recipients.

This study was conducted using descriptive qualitative method. The data were obtained through a field survey, by interviewing two crowdfunding-based investment companies, 250 partner farmers, program managers and assistants, marketing partners, donors/consumers/person who sacrifice and Mustahik (recipients of Qurban meat) involved in program implementation, using purposive sampling method. Focus group discussion was conducted with selected panelists to validate the results of the field survey.

The results of this study showed that the Tebar Hewan Kurban program provides greater benefits to farmers, while increasing the distribution of Qurban meat to be more equitable. The role of moneylenders and middlemen can also be eliminated. Donors feel satisfied because their goals are fulfilled in the Qurban ritual. Program implementers and investors also got decent returns. ISCM is very feasible to be developed on a wider scale, to improve the welfare of farmers or fishermen.

This study used a set of samples of the assisted areas from only one institution, which may lead to institution-specific results. Although the sample is small, the results of this study are expected to provide new insights into the implementation of the Qurban, which will provide more profits and benefits for partner farmers. In broader practice, the program flow is worth considering compared to similar programs in other institutions, in Indonesia or abroad. Because of the COVID-19 pandemic situation, the field survey and focus group discussion were carried out online.

The results show that ISCM is able to increase the income of farmers. Practically, this program can be duplicated in similar institutions, as well as in government or non-government organizations, in Indonesia and abroad, that have the same context and activity.

This study offers several social contributions by exploring how and why ISCM can eliminate the role of moneylenders and middlemen, increasing the small farmers' income, providing reasonable profits to parties involved in marketing and satisfying donors and equitable distribution of Qurban meat.

This study contributes to the literature by confirming the higher impact of ISCM in social business on poverty alleviation. Therefore, this paper provides an alternative solution to increase the income of small farmers through the supply of animals for Qurban or other religious rituals through ISCM arrangements.