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For Bangladeshi small and medium enterprises (SMEs), the COVID-19 economic shock is remarkable in both its complexity and intensity. SMEs need systemic inspiration to solve the crisis, aligned with a moral and authentic approach that serves both the leader and the follower’s interests. This study aims to conceptualize the innovation-focused success method of SMEs before and after the pandemic to manage the crisis by establishing spiritual leadership based on Islamic perspectives.

To discuss the impact of spiritual leadership on innovation-focused SME performance configuration through the lens of a crisis, a comprehensive literature study has been carried out in which over 360 articles are read and reviewed by the authors. It has also established the reliability and validity of literature analysis. Also, a qualitative investigation has been used to support the direction of the study.

For a subsequent process of scientific deployment and evaluation of its execution, a new applied strategic innovation-focused SME success configuration through spiritual leadership development is made available. The primary value of this paradigm is the potential to calculate and treat the aspects of spiritual leadership obtained from Islamic ideas.

Prior analytical or empirical attempts from multiple viewpoints are subsequently needed to inquire about the proposed conceptualization.

Among the realistic consequences of this analysis is that while a number of leadership paradigms have been embraced by a broad body of leadership studies, the findings indicate that this paper should pay heed to the influential spiritual style of leadership, taking into account Islamic perspectives on the context of crisis. Therefore, Bangladeshi SMEs need to develop and run leadership training programs focused on the Islamic viewpoint of spirituality to encourage the actions of leaders during and after crises.

The legal and moral values of the society would ultimately be upgraded from this conceptualization. Moreover, less corruption in corporate activities would improve the economic prosperity of a nation. It would also contribute to the cross-cultural portrayal of the positive picture of Islam.

This holistic conceptualization describes the mediating role of strategic innovation practices based on theoretical foundations, which have seldom been done in previous research, between the Islamic model of spiritual leadership and SME success during and after a crisis.

Using a newly compiled corpus module consisting of utterances from Asian learners during L2 English interviews, this study examined how Asian EFL learners' L1s (Chinese, Indonesian, Japanese, Korean, Taiwanese and Thai), their L2 proficiency levels (A2, B1 low, B1 upper and B2+) and speech task types (picture descriptions, roleplays and QA-based conversations) affected four aspects of vocabulary usage (number of tokens, standardized type/token ratio, mean word length and mean sentence length).

Four aspects concern speech fluency, lexical richness, lexical complexity and structural complexity, respectively.

Subsequent corpus-based quantitative data analyses revealed that (1) learner/native speaker differences existed during the conversation and roleplay tasks in terms of the number of tokens, type/token ratio and sentence length; (2) an L1 group effect existed in all three task types in terms of the number of tokens and sentence length; (3) an L2 proficiency effect existed in all three task types in terms of the number of tokens, type-token ratio and sentence length; and (4) the usage of high-frequency vocabulary was influenced more strongly by the task type and it was classified into four types: Type A vocabulary for grammar control, Type B vocabulary for speech maintenance, Type C vocabulary for negotiation and persuasion and Type D vocabulary for novice learners.

These findings provide clues for better understanding L2 English vocabulary usage among Asian learners during speech.

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.

This study aims to examine the influence of the derived attributes of embedded artificial intelligence-mobile smart speech recognition (AI-MSSR) technology, namely perceived usefulness, perceived ease of use (PEOU) and perceived enjoyment (PE) on consumer purchase intention (PI) through the chain relationships of attitudes to AI and consumer smart experience, with the moderating effect of consumer innovativeness and Generation (Gen) X and Gen Y in fashion retail.

The study employed a quantitative survey strategy, drawing a sample of 836 respondents from Sri Lanka and India representing Gen X and Gen Y. The data analysis was carried out using smart partial least squares structural equation modelling (PLS-SEM).

The findings show a positive relationship between the perceived attributes of MSSR and consumer PI via attitudes towards AI (AAI) and smart consumer experiences. In addition, consumer innovativeness and Generations X and Y have a moderating impact on the aforementioned relationship. The theoretical and managerial implications of the study are discussed with a note on the research limitations and further research directions.

To multiply the effects of embedded AI-MSSR and consumer PI in fashion retail marketing, managers can develop strategies that strengthen the links between awareness, knowledge of the derived attributes of embedded AI-MSSR and PI by encouraging innovative consumers, especially Gen Y consumers, to engage with embedded AI-MSSR.

This study advances the literature on embedded AI-MSSR and consumer PI in fashion retail marketing by providing an integrated view of the technology acceptance model (TAM), the diffusion of innovation (DOI) theory and the generational cohort perspective in predicting PI.

The authors investigate how COVID-19 has influenced the amount, type or topics of abuse that UK politicians receive when engaging with the public.

This work covers the first year of COVID-19 in the UK, from March 2020 to March 2021 and analyses Twitter abuse in replies to UK MPs. The authors collected and analysed 17.9 million reply tweets to the MPs. The authors present overall abuse levels during different key moments of the pandemic, analysing reactions to MPs by gender and the relationship between online abuse and topics such as Brexit, the government’s COVID-19 response and policies, and social issues.

The authors have found that abuse levels towards UK MPs were at an all-time high in December 2020. Women (particularly those from non-White backgrounds) receive unusual amounts of abuse, targeting their credibility and capacity to do their jobs. Similar to other large events like general elections and Brexit, COVID-19 has elevated abuse levels, at least temporarily.

Previous studies analysed abuse levels towards MPs in the run-up to the 2017 and 2019 UK General Elections and during the first four months of the COVID-19 pandemic in the UK. The authors compare previous findings with those of the first year of COVID-19, as the pandemic persisted, and Brexit was forthcoming. This research not only contributes to the longitudinal comparison of abuse trends against UK politicians but also presents new findings, corroborates, further clarifies and raises questions about the previous findings.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-07-2022-0392

This study aims to highlight the dimensions of the rivalry over the regional role between two regional powers in the Middle East, and the impact of local, regional and international pressures of the Syrian crisis on the role performance of the competing forces.

The study is based on using “the role approach” as an analytical frame to benefit by the application of the theory of role. This approach allows the possibility of linking various analytical levels, both in clarifying the relationship between internal and external factors and showing the interaction between elements of perception, abilities and behavior.

The international pressures shall remain governing the frame of competition among the roles of the regional powers, through determining the course of competition and its direct impact on its results.

This study examines the phenomenon of regional rivalry between two distinct and competing regional powers, in a turbulent environment in the wake of the Arab Spring crises, which created opportunities and challenges for regional powers, especially in Syria, where it intersected with the interests and policies of major and regional powers.

This paper aims to contend that populism is damaging to both domestic and international politics; not only does it erode liberal democracy in established democracies but also fuels authoritarianism in despotic regimes and aggravates conflicts and crises in international system.

The research is divided into two main sections. First, it examines how populist mobilization affects liberal democracy, and refutes the claims that populism is beneficial and reinforcing to democracy. Second, it attempts to demonstrate how populism is damaging to domestic politics (by undermining liberal democracy and supporting authoritarianism) as well as international relations (by making interstate conflicts more likely to materialize). Theoretically, populism is assumed to be a strategy used by politicians to maximize their interest. Hence, populism is a strategy used by politicians to mobilize constituents using the main features of populist discourse.

The research argues that populism has detrimental consequences on both domestic and international politics; it undermines liberal democracy in democratic countries, upsurges authoritarianism in autocratic regimes and heightens the level of conflict and crises in international politics. Populism can lead to authoritarianism. There is one major undemocratic trait shared by all populist waves around the world, particularly democracies; that is anti-pluralism/anti-institutions. Populist leaders perceive foreign policy as the continuation of domestic politics, because they consider themselves as the only true representatives of the people. Therefore, populist actors abandon any political opposition as necessarily illegitimate, with repercussions on foreign policy.

Some scholars argue that populism reinforces democracy by underpinning its ability to include marginalized sectors of the society and to decrease voter apathy, the research refuted these arguments. Populism is destructive to world democracy; populists are reluctant to embrace the idea of full integration with other nations. Populists reject the idea of open borders, and reckon it an apparent threat to their national security. The research concludes that populists consider maximizing their national interests on the international level by following confrontational policies instead of cooperative ones.

This paper aims to explore the motivations behind women in a developing South Asian country – Sri Lanka – to embark on entrepreneurship in science, technology, engineering and mathematics (STEM) fields, which is a doubly masculine hegemony operating within a culturally nuanced gendered context.

The study employs a qualitative research approach, conducting in-depth semi-structured interviews with 15 STEM women entrepreneurs, following the theoretical lenses of push and pull motivation theory and gender role theory.

Although the motivations of STEM women entrepreneurs cannot be exclusively categorized as either push or pull factors, the pull factors had a greater influence on the participants in motivating them to become entrepreneurs. The primary motivators for starting businesses in STEM were: inspiration from something or someone, inner calling, the identification of business opportunities, the need for flexibility, necessity and/or desire to help society. It was often difficult to identify one dominant motivator in many instances, as many factors were interlinked to motivate women to start a business. The study also revealed that gender ideologies could stifle the participants' motivation, while the inner need to break these gender ideologies implicitly stimulated their motivation.

The study contributes to and expands the knowledge of STEM women entrepreneurs in general and to the limited existing knowledge of STEM women entrepreneurs in developing countries specifically. The paper brings contextual novelty as Sri Lanka produces more female STEM graduates than men, which is unique compared to most other parts of the world.

The study investigates factors like media, tribal, religious and party politics' influence on Nigerias’ 2023 presidential election choice. It confirms dominant social media platforms and examines their influence on election polls, e-participation and political candidate choice. The main objectives of this study are to: investigate if tribal, religious and party politics affect the respondent’s choice of a presidential candidate, ascertain the respondent's most used social media platform for political engagement and determine how social media platforms influenced the election polls during the 2023 Nigerian presidential election.

A sample size of 384 registered voters was used to survey three states in Southeast Nigeria hinged on the technological acceptance model, the instrumentalist theory of ethnicity and the theory of reasoned action.

The study found that tribal politics did not influence political candidates during the 2023 Nigerian presidential election. However, religious and party politics influenced their choices as well as X (Twitter), found as the most used and most influential social media platform vital for enhancing participatory democracy and informing people at real-time.

The researchers experienced challenges such as ensuring that the respondents filled the questions appropriately to reduce the number of void questionnaires and a funding problem since they had yet to receive any grant to enhance the study.

The study commends improved Internet connectivity and accessibility among the citizens for increased political engagement on social media. It also recommends that the Nigerian government enforce the rule of law in politics to enable diverse tribes and religions to experience democratic e-participation and development without marginalisation or subjugation by incumbent power. The findings affirm that social media is apt in political communication during the 2023 presidential elections in Nigeria. The study is a contribution to knowledge, timely and original.

In time and accurate detection of cancer can save the life of the person affected. According to the World Health Organization (WHO), breast cancer occupies the most frequent incidence among all the cancers whereas breast cancer takes fifth place in the case of mortality numbers. Out of many image processing techniques, certain works have focused on convolutional neural networks (CNNs) for processing these images. However, deep learning models are to be explored well.

In this work, multivariate statistics-based kernel principal component analysis (KPCA) is used for essential features. KPCA is simultaneously helpful for denoising the data. These features are processed through a heterogeneous ensemble model that consists of three base models. The base models comprise recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU). The outcomes of these base learners are fed to fuzzy adaptive resonance theory mapping (ARTMAP) model for decision making as the nodes are added to the F_2ˆa layer if the winning criteria are fulfilled that makes the ARTMAP model more robust.

The proposed model is verified using breast histopathology image dataset publicly available at Kaggle. The model provides 99.36% training accuracy and 98.72% validation accuracy. The proposed model utilizes data processing in all aspects, i.e. image denoising to reduce the data redundancy, training by ensemble learning to provide higher results than that of single models. The final classification by a fuzzy ARTMAP model that controls the number of nodes depending upon the performance makes robust accurate classification.

Research in the field of medical applications is an ongoing method. More advanced algorithms are being developed for better classification. Still, the scope is there to design the models in terms of better performance, practicability and cost efficiency in the future. Also, the ensemble models may be chosen with different combinations and characteristics. Only signal instead of images may be verified for this proposed model. Experimental analysis shows the improved performance of the proposed model. This method needs to be verified using practical models. Also, the practical implementation will be carried out for its real-time performance and cost efficiency.

The proposed model is utilized for denoising and to reduce the data redundancy so that the feature selection is done using KPCA. Training and classification are performed using heterogeneous ensemble model designed using RNN, LSTM and GRU as base classifiers to provide higher results than that of single models. Use of adaptive fuzzy mapping model makes the final classification accurate. The effectiveness of combining these methods to a single model is analyzed in this work.