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This article aims to analyze the strategic work of Polish entrepreneurs in the furniture industry following the political changes in 1989. The authors examined how these entrepreneurs transitioned from local craftsmen or importers into leaders of international manufacturing companies and how their strategizing contributed to the unprecedented growth of the Polish furniture sector.

The authors examined extant data, specifically biographical interviews conducted with 11 prominent leaders in the Polish furniture industry (Hryniewicki, 2015, 2018). They analyzed within a theoretical framework that integrates J.C. Spender’s theory of strategic management with Barry Johnson’s concept of polarity management. Polarity is a way of understanding and managing interdependent, opposing pairs of values or perspectives that give rise to conflict.

The analysis reveals key patterns of strategic challenges at the level of human agency, history and sense-making. The authors identified four key polarities: life and business, knowledge presence and absence, concordance and discordance, and instrumental and non-instrumental sense-making.

The polarity concept illuminates the interplay of agency and determinism in strategic decision-making, offering valuable insights for methodology and a deeper understanding of Poland’s furniture industry.

This paper aims to discuss the work of non-executive directors (NEDs) as inherently paradoxical. Paradox refers to the presence of persistent contradictions between interdependent forces. Those persistent tensions are explored, and approaches are indicated to stimulate the adaptive use of paradoxes as forces of innovation and renewal.

This conceptual approach can be read as an invitation for corporate governance scholars to embrace the logic of paradox to expand the understanding of this topic. Paradox is not conceptualized as an alternative to dominant structural views, including board composition, but as a complementary conceptual perspective, a meta-theoretical lens to shed light on the tensions inherent to governance.

The authors propose that paradox theory offers a fresh conceptual lens to study the role of NEDs. This approach may help NEDs to turn tensions and paradoxes visible to develop a rich understanding of their work, as well as helping them navigate the complexities of organizing, a process rich in inherent paradoxicality.

Organizational paradox theory is a bourgeoning field of study, but the conceptual lens of paradox has still been underexplored in the study of corporate governance.

The COVID-19 outbreak disrupted the business environment and severely affected the morale and performance of the employees. Further, the Indian automobile industry witnessed major setbacks and drastically impacted sector in COVID-19. Talent agility is an emerging concept in the field of HRM that will foster innovations and productivity in the automobile industry. Thus, this study aims to explore the barriers to building in-house agile talents in the Indian automobile industry in the new normal.

The barriers of talent agility were identified through a literature review and validated through experts’ opinions. This study used a hybrid approach, which combines Interpretive Structural Modelling-Polarity (ISM-P) and decision-making trial and evaluation laboratory (DEMATEL) to develop a hierarchical structural model of the barriers, followed by classification into cause and effect groups.

The result of the multi-method approach identified that shortage of skills and competencies, lack of IT infrastructure, lack of ambidextrous leaders, lack of smart HRM technologies and practices, lack of attractive reward system/career management, poor advanced T&D, poor industry, institute interface and financial constraints are the critical barriers.

It can provide a strategic roadmap for automobile manufacturers to promote talent agility in the current wave of digitalization (Industry 4.0). This study can help the managers to address and overcome the barrier and hurdles in building talent agility.

This study is unique in that it addresses the contemporary issues related to talent agility in the context of the Indian automobile industry in the current rapidly changing environment. This study developed a holistic integrated ISM(P)-DEMATEL hierarchical framework on the barriers of talent agility indicating inner dependency weights, i.e., the strength of interrelationship between the barriers.

With the rapid development of technology, 360° panorama on mobile as a very convenient way to present virtual reality has brought a new shopping experience to consumers. Usually, consumers get product information through virtual annotations in 360° panorama and then make a series of shopping behaviors. The visual design of virtual annotation significantly influences users' online visual search for product information. This study aims to investigate the influence of the visual design of virtual annotation on consumers' shopping experience in the online shopping interface of 360° panorama.

A 2 × 3 between-subject design was planned to help explore whether different display model of annotation (i.e. negative polarity and positive polarity) and different background transparency of annotation (i.e. 0% transparency, 25% transparency and 50% transparency) may affect users' task performance and their subjective evaluations.

(1) Virtual annotations with different background transparency affect user performance, and transparency has better visual search performance. (2) Virtual annotation background display mode may affect the user operation performance; the positive polarity of the virtual annotation is more convenient for the users' visual searching for product information. (3) When the annotation background transparency is opaque or semi-transparent, the negative polarity display is more favorable to the users' visual search. However, this situation is reversed when the annotation background transparency is 25%. (4) Participants preferred the presentation of positive polarity virtual annotations. (5) Regarding the degree of willingness to use and ease of understanding, participants preferred the negative polarity display for 0% background transparency or 50% background transparency. However, the opposite result was obtained for 25% background transparency.

The findings generated from the research can be a good reference for the development of virtual annotation visual design for mobile shopping applications.

1. Virtual annotation background transparency and background display mode are two essential attributes of 360° panoramas.

2. This study examined how virtual annotation background transparency and background display mode influence user performance and experience.

3. It is recommended to use a translucent or opaque annotation background with a negative polarity display.

4. Virtual annotation presentation with 25% background transparency facilitates consumer searching and comparison of product information.

5. Users prefer a positive polarity annotation display.

Virtual annotation background transparency and background display mode are two essential attributes of 360° panoramas.

This study examined how virtual annotation background transparency and background display mode influence user performance and experience.

It is recommended to use a translucent or opaque annotation background with a negative polarity display.

Virtual annotation presentation with 25% background transparency facilitates consumer searching and comparison of product information.

Users prefer a positive polarity annotation display.

The coronavirus disease 2019 (COVID-19) pandemic caused significant disruption to the global labor market, resulting in a rapid transition toward remote work, e-commerce and workforce automation. This shift has sparked a considerable amount of public discussion. This study aims to explore the online public's sentiment toward remote work amid the pandemic.

Based on justice theory, this paper examines user-generated content on social media platforms, particularly Twitter, to gain insight into public opinion and discourse surrounding remote work during the COVID-19 pandemic. Employing content analysis techniques such as sentiment analysis, text clustering and evolutionary analysis, this study aims to identify prevalent topics, temporal patterns and instances of sentiment polarization in tweets.

Results show that people with positive opinions focus mainly on personal interests, while others focus on the interests of the company and society; people's subjectivities are higher when they express extremely negative or extremely positive emotions. Distributive justice and interactional justice are distinguishable with a high degree of differentiation in the cluster map.

Previous research has inadequately addressed public apprehensions about remote work during emergencies, particularly from a justice-based perspective. This study seeks to fill this gap by examining how justice theory can shed light on the public's views regarding corporate policy-making during emergencies. The results of this study provide valuable insights and guidance for managing public opinion during such events.

Aspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect, and therefore it is impossible to draw conclusions on their own. ASA is challenging because a given sentence can reveal complicated feelings about multiple aspects.

This research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and classification. Aspect extraction is done using a double-layered convolutional neural network (DL-CNN). The optimized aspect and self-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative.

To detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM) model. The results of the proposed method revealed that it achieves a high accuracy of 95.3 per cent for the restaurant dataset and 96.7 per cent for the laptop dataset.

The novelty of the research work is the addition of two effective attention layers in the network model, loss function reduction and accuracy enhancement, using a recent efficient optimization algorithm. The loss function in OAS-LSTM is minimized using the adaptive pelican optimization algorithm, thus increasing the accuracy rate. The performance of the proposed method is validated on four real-time datasets, Rest14, Lap14, Rest15 and Rest16, for various performance metrics.

Social media has played a pivotal role in polarizing views on Russia–Ukraine conflict. The effects of polarization in online interactions have been extensively studied in many contexts. This research aims to examine how multiple social media sources may act as an integrator of information and act as a platform for depolarizing behaviors.

This study analyzes the communications of 6,662 tweets related to the sanctions imposed on Russia by using textual analytics and predictive modeling.

The research findings reveal that the tweeting behavior of netizens was depolarized because of information from multiple social media sources. However, the influx of information from non-organizational sources such as trending topics and discussions has a depolarizing impact on the user’s pre-established attitude.

For policymakers, conflict mediators and observers, and members of society in general, there is a need for (1) continuous and consistent communication throughout the crisis, (2) transparency in the information being communicated and (3) public awareness of the polarized and conflicting information being provided from multiple actors that may be biased in the claims being made about the conflict crisis.

While previous research has examined Russia–Ukraine conflict from a variety of perspectives, this is the first study to examine how social media might be used to reduce attitude polarization during times of conflict.

This paper aims to (1) identify the different performance drivers (lead indicators) and outcome measures (lag indicators) investigated in the literature concerning the four balanced scorecard (BSC) perspectives in operations management (OM) contexts and (2) understand how performance drivers and outcome measures (and substantiated perspectives) are related.

We undertake a systematic literature review of the BSC literature in OM journals. From the final sample of 40 articles, performance drivers and outcome measures have been identified, and the relationships amongst them have been synthesised according to the system dynamics approach.

Findings show (1) the most relevant performance drivers and outcome measures within each BSC perspective, (2) their relationships, (3) how the perspectives are linked through the performance drivers and outcome measures and (4) how the different measures relate systemically. Accordingly, four causal loops amongst identified measures have been built, which – jointly considered – allowed for the creation of a dynamic strategy map for OM.

This study is the first one that provides a comprehensive and holistic view of how the different performance drivers and outcome measures within and between the four BSC perspectives in OM relate systemically, increasing the knowledge and understanding of scholars and practitioners.

Using sentiment analysis (SA), this study aims to examine the impact of COVID-19 on mental health and virtual learning experiences among 1,125 students at a public Argentinean faculty.

A study was conducted during the COVID-19 pandemic, surveying 1,125 students to gather their opinions. The survey data was analysed using text mining tools and SA. SA was used to extract the students’ emotions, views and feelings computationally and identify co-occurrences and patterns in related words. The study also examines educational policies implemented after the pandemic.

The prevalent emotions expressed in the comments were trust, sadness, anticipation and fear. A combination of trust and fear resulted in submission. Negative comments often included the words “virtual”, “virtual classroom”, “virtual classes” and “professor”. Two significant issues were identified: teachers’ inexperience with virtual classes and inadequate server infrastructure, leading to frequent crashes. The most effective educational policies addressed vital issues related to the “virtual classroom”.

Text mining and SA are valuable tools for decision-making during uncertain times, such as the COVID-19 pandemic. They can also provide insights to recover quality assurance processes at universities impacted by health concerns or external shocks.

The paper makes two main contributions: it conducts a SA to gain insights from comments and analyses the relationship between emotions and sentiments to identify optimal educational policies. The study pioneers exploring the link between emotions, policies and the pandemic at a public university in Argentina. This area of research still needs to be explored.

Sharing and disseminating debunking information are critical to correcting rumours and controlling disease when dealing with public health crises. This study investigates the factors that influence social media users' debunking information sharing behaviour from the perspective of persuasion. The authors examined the effects of argument adequacy, emotional polarity, and debunker's identity on debunking information sharing behaviour and investigated the moderating effects of rumour content and target.

The model was tested using 150 COVID-19-related rumours and 2,349 original debunking posts on Sina Weibo.

First, debunking information that contains adequate arguments is more likely to be reposted only when the uncertainty of the rumour content is high. Second, using neutral sentiment as a reference, debunking information containing negative sentiment is shared more often regardless of whether the government is the rumour target, and information containing positive sentiment is more likely to be shared only when the rumour target is the government. Finally, debunking information published by government-type accounts is reposted more often and is enhanced when the rumour target is the government.

The study provides a systematic framework for analysing the behaviour of sharing debunking information among social media users. Specifically, it expands the understanding of the factors that influence debunking information sharing behaviour by examining the effects of persuasive cues on debunking information sharing behaviour and the heterogeneity of these effects across various rumour contexts.