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The COVID-19 pandemic has presented small-to-medium size enterprises (SMEs) with a massive and unexpected challenge that has caused many to adjust their operational standards. Perhaps the biggest change has been the shift to remote work and away from traditional office spaces. Thus, this study aims to explore the implications of this shift within the context of customer participation in the new product development (NPD) process.

Our study surveys 218 small-to-medium size business-to-business firms in the USA on a variety of questions revolving around their NPD processes, customer collaboration and the shift to remote work. The authors use structural equation modeling in the AMOS program to analyze the data.

The findings indicate that both customer participation breadth and customer participation depth positively impact new product performance. Furthermore, these relationships are found to be contingent upon whether firms rely on remote work during the collaboration process. The results show that accessing a broader variety of explicit customer insights (i.e., breadth) has become easier in the increasingly remote collaboration environment. However, as face-to-face customer participation in NPD has decreased, the prospect of gaining deep, tacit customer knowledge relevant to product development (i.e., depth) has become more challenging.

This study contributes to the knowledge-based view of the firm and the customer participation literature, and it also has implications for managers adjusting to the shift to remote work following the COVID-19 pandemic. The findings provide additional evidence that customer participation is an effective strategy for SMEs (Morgan et al., 2018), but remote work has both positive and negative implications regarding the type of external knowledge that is acquired during customer participation in NPD.

Owing to the significance of a healthy lifestyle, we investigate the antecedents of the healthy lifestyle of young consumers in Sri Lanka. 658 structured questionnaires were collected from young consumers in Sri Lanka as part of the survey procedure. The judgmental sampling method is used to choose the respondents. The analysis makes use of both descriptive and inferential statistics. The findings disclose a high degree of healthy lifestyle among young consumers in Sri Lanka. Further findings revealed that health consciousness, collective esteem, and neighborhood environment are the antecedents for a healthy lifestyle. As young consumers are more concerned about a healthy lifestyle, managers in certain industries such as food and beverages, hotels, and restaurants should adopt their products and services in line with a healthy lifestyle.

This paper aims to provide a historical overview of AA, its purpose and benefits, the legal rationale for the SCOTUS ruling and what it means for colleges and the workplace regarding equitable opportunities for minority groups (which include women, Blacks, Hispanics, Asians and other low-income populations), as they aim for the “American dream”.

SCOTUS decision and rationale, along with literature.

The race-based affirmative action (AA) precedent was recently overturned by the Supreme Court of the United States (SCOTUS) in the case of Students for Fair Admission (SFFA), Inc. vs President and Fellows of Harvard College/University of North Carolina. SCOTUS ruled that race cannot be a specific basis for college admission. In other words, public and private colleges and universities will no longer be able to consider “race” as a factor in deciding which qualified applicants should be admitted to enhance the diversity of their student body.

This is an original analysis.

This research is designed to explore the relationship between social media addiction, student engagement and student retention. Social media addiction is dealt with as an independent variable student engagement acts as a mediating variable and student retention as a dependent variable.

This is a cross-sectional and quantitative research. Primary data are collected from 600 respondents (university students) with the help of a structured questionnaire. Multistage sampling techniques, i.e. simple random sampling and judgment sampling, are used for the selection of respondents.

Results indicate that for direct relationships, social media addiction has a significant positive impact on student engagement and student retention, respectively, while student engagement is partially mediating the relationship between social media addiction with student retention.

In the future, these kinds of research may also be conducted on students of different universities in Pakistan, which are located in other cities of Pakistan besides Lahore. This research provides a practical framework for the higher authorities of the universities of Pakistan and explains how the use of media positively fosters the levels of student retention directly and indirectly through the path of student engagement. It is commonly believed that media addiction is bad but the result of this research indicates that anything is not dangerous but depends upon its use, media addiction itself is not bad but if someone uses this for a good purpose in limitation then it has better outcomes. The result indicates that the media addiction of students has a positive impact on student retention. This means that if someone uses media for a positive purpose then he/she will use it as a supporting tool for success. Longitudinal research on these variables will also help to check the status after a specific interval of time.

The current study will help the practitioners or policymakers (Managers) of higher education institutions by providing practical insights into the positive use of media by students for increasing their knowledge and grades. This research can also help practitioners or policymakers to focus their students on the positive use of social media for fostering the levels of student retention.

To the best of the researcher’s knowledge, no previous study has been done to incorporate social media addiction and student engagement in a single model in the Pakistani cultural context. Similarly, the relationship of variables social media addiction with student engagement is rarely checked empirically because the research of Wang et al. (2011) proposed that social media addiction has a relationship with student engagement so that is why this is the rationale of the research is to check this empirically. Moreover, this study is an initial effort to check the mediating effect of student engagement in the relationship between social media addiction and student retention. This research is also proposing the framework of social media addiction, student engagement and student retention based on the social exchange theory (SET).

Sarcasm is a linguistic expression that usually carries the opposite meaning of what is being said by words, thus making it difficult for machines to discover the actual meaning. It is mainly distinguished by the inflection with which it is spoken, with an undercurrent of irony, and is largely dependent on context, which makes it a difficult task for computational analysis. Moreover, sarcasm expresses negative sentiments using positive words, allowing it to easily confuse sentiment analysis models. This paper aims to demonstrate the task of sarcasm detection using the approach of machine and deep learning.

For the purpose of sarcasm detection, machine and deep learning models were used on a data set consisting of 1.3 million social media comments, including both sarcastic and non-sarcastic comments. The data set was pre-processed using natural language processing methods, and additional features were extracted and analysed. Several machine learning models, including logistic regression, ridge regression, linear support vector and support vector machines, along with two deep learning models based on bidirectional long short-term memory and one bidirectional encoder representations from transformers (BERT)-based model, were implemented, evaluated and compared.

The performance of machine and deep learning models was compared in the task of sarcasm detection, and possible ways of improvement were discussed. Deep learning models showed more promise, performance-wise, for this type of task. Specifically, a state-of-the-art model in natural language processing, namely, BERT-based model, outperformed other machine and deep learning models.

This study compared the performance of the various machine and deep learning models in the task of sarcasm detection using the data set of 1.3 million comments from social media.