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This paper aims to explore the stages and processes of work identity reconstruction following a major health-related career shock.

In-depth case study and interpretive phenomenological methods are used to allow for deep reflective self-analysis of post-career shock stages. The paper explores the identity processes and stages a Chief of Human Resources of a multinational firm experienced after being deprived from his main working abilities as a result of a brain stroke.

Post-shock work identity stages and processes are identified, as long as the importance of identity threat, liminality, identity internalization and relational recognition in the reconstruction process. The findings propose new coping responses that may allow individuals to escape a diminished work identity: identity shedding and identity implanting.

While career shocks play a significant role in career development, there is currently little understanding of how career shocks may affect individuals' work identity or sense of self, particularly over time. The paper provides a nuanced understanding of this phenomenon, through process data collected at several points in time over a period of 14 years.

Artificial intelligence (AI) has gained significant momentum in recent years. Among AI-infused systems, one prominent application is context-aware systems. Although the fusion of AI and context awareness has given birth to personalized and timely AI-powered context-aware systems, several challenges still remain. Given the “black box” nature of AI, the authors propose that human–AI collaboration is essential for AI-powered context-aware services to eliminate uncertainty and evolve. To this end, this study aims to advance a research agenda for facilitators and outcomes of human–AI collaboration in AI-powered context-aware services.

Synthesizing the extant literature on AI and context awareness, the authors advance a theoretical framework that not only differentiates among the three phases of AI-powered context-aware services (i.e. context acquisition, context interpretation and context application) but also outlines plausible research directions for each stage.

The authors delve into the role of human–AI collaboration and derive future research questions from two directions, namely, the effects of AI-powered context-aware services design on human–AI collaboration and the impact of human–AI collaboration.

This study contributes to the extant literature by identifying knowledge gaps in human–AI collaboration for AI-powered context-aware services and putting forth research directions accordingly. In turn, their proposed framework yields actionable guidance for AI-powered context-aware service designers and practitioners.

Co-teaching is a foundational mentoring model used in teacher residency programs in urban classrooms throughout the United States of America. Beyond the basic understanding of co-teaching in categorizing classroom models, the purpose of this qualitative case study is to investigate the dialectical tensions manifested in mentored co-teaching activities through the lens of cultural-historical activity theory (CHAT).

Designed as a qualitative case study of 17 pairs of teaching-residents and mentor-teachers, the authors used thematic analysis to scrutinize archival interview data in an urban teacher residency program located in the largest megalopolis of the USA Northeast. The authors used CHAT-based concept coding to analyze the interview narratives from participants across different secondary school placements as they reflected on their co-teaching philosophy and the relationships they built.

The authors found that for teaching-residents and mentor-teachers to co-develop as co-teachers, they jointly must learn to resolve the dialectical tensions of unbalanced classroom ownership vs added co-working responsibilities, breaking from routine so that a partnership can grow. Furthermore, the findings suggest that the prefix co- should be understood as (1) shifts in thinking that transcend the status quo and (2) the orchestration of human capital to change norms.

This new understanding of the prefix co- allows teacher education programs to better mediate the dialectical tensions experienced by co-teachers in a mentored co-teaching activity, from individual teacher learning (e.g. a pair/dyad comprising one teaching-resident and one mentor-teacher) to collective co-learning across activity systems (e.g. partnership-based teacher education).

The authors aimed to identify the nature of customer harassment against lesbian, gay, bisexual, transgender, and queer (LGBTQ) workers.

The authors analyzed data from in-depth interviews with 30 LGBTQ service workers in the United States who had recently experienced customer harassment.

Among various forms of customer harassment LGBTQ workers reported, some showed commonalities with previously reported cases of race-based and gender-based customer harassment. However, other cases highlighted unique aspects of LGBTQ-based customer harassment—customers morally condemned their LGBTQ identities, refused their service while displaying emotional disgust, and made sexual advances while imposing sexual stereotypes and fantasies about LGBTQ people. Experiences of customer harassment varied across subgroups of workers who had specific sexual and gender identities, and LGBTQ workers of color were harassed for their LGBTQ and racial identities simultaneously.

Past research on group-based customer harassment has focused on incidents against straight, cisgender women and workers of workers of color, but the present study identified the nature of customer harassment that targeted workers' LGBTQ status.

This paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge.

We deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas.

The approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels.

This work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media.

This work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it.

In this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).

The purpose of this paper is to do a thorough literature review to assess the current status of corporate sustainability (CS) practices, issues and challenges in small and medium enterprises (SMEs) and based on literature suggest a model that can improve and strategically manage their sustainability practices in the emerging market context of Asia.

After setting the theoretical background on why the corporations need to shoulder the responsibility of sustainable development, the authors did a high level literature review to estimate the gap area and based on the findings formulated some research questions on why the SMEs, more specifically those in Asian emerging market, are lagging behind in terms of CS practices. Next, using the “literature survey” as the methodology the authors did a thorough in-depth literature review to answer the above research questions and organised the findings in light of those research questions. Considering the research gap and the crucial role of SMEs in a country’s economic progress the authors restricted the literature search on CS in the case of SMEs only.

CS is a well-practiced area in big organisations. However, the literature suggests that in the case of SMEs the situation is different. Social and environmental practices are grossly neglected in SMEs more specifically in emerging markets. Existing literature mentions that collaborative mode of operation, government policy and facilitation, supporting organisation culture can positively influence SME’s sustainability performance, and hence, improve their financial performance.

The conceptual model has been developed based on suggestions and recommendations in western countries context. To establish the validity of the model in the Asian business scenario it needs to be tested with a sizable sample of SMEs in the Asian market.

The current paper has practical implications both for academic research on sustainable development and also in business development. The proposed model in the current form is based on the literature survey only. Once such a model is empirically established this can be proved beneficial for providing guidance to SMEs to enhance the sustainability of their business operations. Moreover, environmental and social best practices adopted by SMEs will also have a positive influence on society and the environment in the long run.

This paper had done a country-wise comparison of sustainable practices in SMEs and based on the recommendations and suggestions of different scholars as found in the detailed literature review, the authors developed few research propositions and also presented a conceptual model. Finally, to address the gap it mentions some future research possibilities to test and validate the proposed model in the context of SMEs in emerging markets of Asia.

The purpose of this paper is to do a thorough literature review to assess the current status of corporate sustainability practices, issues and challenges in small and medium-sized enterprises (SMEs) and, based on literature, suggest a model that can improve and strategically manage their sustainability practices in the emerging market context of Asia.

The authors did a meta-analysis of the reviewed literature taken from peer-reviewed scholarly journals in the time frame of 1985-2016. Methodology used by the authors is through preferred reporting items for systematic reviews and meta-analysis.

Corporate sustainability is a well-practiced area in big organisations. However, literature suggests that in case of SMEs, the situation is different. Social and environmental practices are grossly neglected in SMEs, more specifically in emerging markets. Existing literature mentions that collaborative mode of operation, government policy and facilitation and supporting organisation culture can positively influence SME’s sustainability performance and hence improve their financial performance.

Most of the papers in corporate sustainability literature are qualitative in nature and there is no empirical evidence of establishing this relationship in SME context. This paper tries to conceptualise the existence of a possible framework relating factors of sustainability and their role in improving business performance of the SMEs. However, the findings are purely literature-based and piloting of the questionnaire is also done on 20 SMEs in a specific geography. This is a very small and also a biased sample. Future research studies based on this paper are expected to verify the proposed relationship with larger data set catering to different industry clusters and countries.

The proposed model is only directional in nature. With the content analysis, the authors have tried to answer the research questions relating to factors of sustainability and its impact on business performance. To address the research questions in a more generalised way, an empirical research needs to be conducted to establish the research propositions. The proposed model needs to be validated and optimised with further research and data analysis. Once such a model is established, this can be proved beneficial for providing guidance to SMEs to enhance the sustainability of their business operations.

The SME sector has made a significant contribution to the economic development in countries, and this sector has huge potential for growth, and their geographic spread and penetration is much higher than large organisations. Sustainable growth of this industry sector can reasonably be expected to lead to stable and sustainable development of the nation. Moreover, environmental and social best practices adopted by SMEs will also have a positive influence on society and environment in the long run.

This paper had done a geography wise analysis of sustainable practices in SMEs, and based on the recommendations and suggestions of different analyses, the authors developed few research propositions and also presented a conceptual model. Finally, to address the gap, it mentions some future research possibilities to test and validate the proposed model in the context of SMEs in emerging markets of Asia.

This study evaluates the effect of the media image content of business to business (B2B) organizations to accelerate social media engagement. It highlights the importance of strategically designing image content for business marketing strategies.

This study designed a computation extensive research model based upon the stimulus-organism-response (SOR) theory using 39,139 Facebook posts of 125 organizations selected from Fortune 500 firms. Attributes from images and text were estimated using deep learning models. Subsequently, inferential analysis was established with ordinary least squares regression. Further machine learning algorithms, like support vector regression, k-nearest neighbour, decision tree and random forest, are used to analyze the significance and robustness of the proposed model for predicting engagement metrics.

The results indicate that the social media (SM) image content of B2B firms significantly impacts their social media engagement. The visual and linguistic attributes are extracted from the image using deep learning. The distinctive effect of each feature on social media engagement (SME) is empirically verified in this study.

This research presents practical insights formulated by embedding marketing, advertising, image processing and statistical knowledge of SM analytics. The findings of this study provide evidence for the stimulating effect of image content concerning SME. Based on the theoretical implications of this study, marketing and media content practitioners can enhance the efficacy of SM posts in engaging users.