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This paper aims to examine the interconnectedness between stakeholder expectations (SE), inter-organizational coordination (IOC) and procurement practices within humanitarian organizations (HOs) based in Uganda.

Employing a quantitative cross-sectional design, data were gathered from 43 HOs and analyzed using SmartPLS 4.0.8.3. Variance-based structural equation models (VB-SEMs) were employed to examine both direct and indirect effects.

The findings show a significantly positive relationship between SE, IOC and procurement practices. Additionally, the mediating role of IOC in the relationship between SE and procurement practices is evident.

While this study offers insights into procurement practices in HOs, the use of a quantitative approach might limit capturing dynamic changes over time. Future research could benefit from a nuanced approach involving interviews and longitudinal studies to uncover incremental changes.

During relief management, HOs need to understand their SE through information sharing and capacity building. This understanding can aid in selecting procurement practices that align with SE and ensure the delivery of relief.

Leveraging stakeholder theory, this research contributes to the understanding of how SE and IOC influence the adoption of procurement practices in HOs during relief delivery.

This paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.

The approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.

Risk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.

To better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.

The purposes of the present study were to analyse: (1) if there is a hierarchical relationship between cognitive dimension (attention) and emotional dimension (enthusiasm); (2) to what extent the cognitive and emotional dimensions of the customer engagement (CE) affect loyalty to consumer brands in traditional settings and in social media; and (3) if involvement moderates the relationships between cognitive and emotional dimensions of CE and between each CE dimension and the two kinds of loyalties (offline and social media).

The authors applied structural equation model on a sample of 272 passengers of main airlines operating the domestic Argentinian market in 2018. The authors also used multigroup analysis to combine involvement as a moderator variable.

Cognitive dimensions of engagement (attention) had a direct and positive effect on the emotional engagement (measured by enthusiasm). Capturing the attention of passengers is key to maintaining their loyalty both in offline and online environments, especially if they are lowly involved with brands.

The present study suggests and tests a model for the consequences of CE as a multidimensional construct in a specific service context.

Following the attitude formation approach and the hierarchy of effect of advertising theory, this study researched the dynamic of relationship between cognitive and emotional dimensions of CE. The results contribute on the line of considering CE as a process, including stages that consumers go through until they achieve the engagement with the brand. The results offer evidence of the specific impact of each CE dimension on loyalty at two environments (offline and social media).

本研究有以下三個目標：

(1) 、探討認知維度 (注意) 與情緒維度 (熱忱) 之間是否存在著一個等級關係;

(2) 、瞭解顧客契合的認知維度和情緒維度、分別在傳統的設置裡，以及在社交媒體內，影響消費者品牌忠誠的程度;和.

(3) 、研究參與會否調節顧客契合的認知維度和情緒維度之間的關係;再者，參與會否調節顧客契合每個維度與兩類忠誠 (即離線和社交媒體) 之間的關係。

我們以結構方程模型，分析一個涵蓋於2018年在阿根廷國內市場營運的主要航空公司272名乘客的樣本，我們亦以多組分析，把參與作為調節變數，合併在一起。

研究結果顯示，顧客契合的認知維度對情緒維度 (以熱忱來計量) 有直接和積極的影響。研究結果亦顯示，若要在離線、以及在網絡環境下維持乘客的忠誠，最重要的是公司需取得他們的注意，特別是當他們談及品牌時是卑微的。

本研究為瞭解在一個特定服務業的背景裡，顧客契合、作為多元的構建會帶來的結果，建議並測試了一個模型。

研究人員使用態度形成的理念和廣告的階層效果理論，去探討顧客契合的認知維度與情緒維度之間的關係的動態。研究的貢獻、在於視顧客契合為一個過程，而這個過程，包括了消費者若要與品牌齧合所需經過的階段。另外，研究結果為每個顧客契合，在離線和社交媒體兩個環境裡，對忠誠所產生特定的影響提供了證據。

Bullying negatively affects victims’ mental health and has been shown to be associated with anxiety and depression. Moreover, many studies have reported the prevalence of mistreatment among medical students (MS), interns and junior physicians. However, there are limited studies on bullying in the Middle East, and no studies on higher education in Bahrain. The authors’ aim was to investigate and compare the prevalence of bullying between MS at a major public university and non-medical students (NMS) at a private university in Bahrain. The authors also sought to explore the associations between bullying, depression and anxiety.

The authors conducted a survey-based cross-sectional study from October 1 to December 31, 2018, using convenient sampling method. A total of 300 students (150 MS and 150 NMS) participated in the study by answering a questionnaire that explored exposure to different types of bullying. In addition, standard anxiety and depression instruments were distributed to students.

The results showed that MS were more exposed to bullying than NMS, with a significant number (41%) reporting mistreatment from multiple sources, including teachers, consultants and peers. Furthermore, the authors found that bullying was significantly correlated with psychological health, anxiety and depression.

There are limited studies on bullying in the Middle East and no studies on the topic of higher education in Bahrain. Furthermore, the learning environment of tertiary education institutions can be improved by addressing the concerns associated with bullying identified in this study.

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.