Search results

Diverse organizations, including corporations, increasingly advocate for sociopolitical issues to engage their publics. These issues often revolve around the interests, identities and dynamics of specific social groups, necessitating a deeper examination of publics’ social identities. Thus, this study aims to explore how individuals’ social identities, such as group memberships and ingroup identification, drive individuals to become active publics.

Drawing from the social identity approach and the situational theory of problem solving (STOPS), an online survey of 965 participants from two racial groups was conducted to examine the impacts of social group memberships and ingroup identification on problem recognition, involvement recognition, constraint recognition, referent criterion, situational motivation and attitude toward race relations and the Black Lives Matter movement.

The results of multigroup confirmatory factor analysis (CFA) and structural equation modeling analyses revealed that social group membership and ingroup identification are crucial in shaping publics’ situational perceptions of sociopolitical issues. The effects of ingroup identification on situational, motivational and attitudinal perceptions significantly differed between African American and White American groups, highlighting the importance of considering group dynamics.

This study underscores the value of integrating the social identity approach into public relations and corporate communication research to elucidate diverse publics’ socio-psychological processes in response to sociopolitical issues. The findings equip practitioners with deeper insights into social group dynamics, allowing them to adapt their communication efforts to better engage and mobilize different publics, ultimately enhancing the impact of their advocacy efforts.

This study investigates the processes that the US universities and colleges used to learn during the COVID-19 pandemic and the factors that facilitated and impeded their learning processes.

To address this study’s research questions, this study used a crisis communication and learning lens to interview crisis response team members from 30 US higher education institutions in May 2020 (the first pandemic semester). In October 2020 (the second pandemic semester), this study conducted follow-up interviews with 25 of the original interviewees. Overall, this study conducted 55 interviews.

Learning during the COVID-19 pandemic is facilitated by a recognition of a serious deficiency in the current system and impeded by the need to act quickly. The findings demonstrate the process by which decisions, actions and strategies emerged during crises.

This investigation illustrates how crises can prompt organizational learning while demonstrating the critical role of internal and external resources in the learning process.

Drawing on the elaboration likelihood model, this study aims to examine how employee volunteer program (EVP) type, corporate visual identity, and issue involvement affect external publics' attributions of EVPs and attitudes toward the company.

A 2 (EVP type: skills-based vs. not-skills-based) × 2 (salience of corporate visual identity: high vs. low) randomized experiment was conducted with 157 participants from Amazon's Mechanical Turk. A fictitious company and its EVP messages were created to control for the company's existing reputation and participants' past experiences with the company.

Participants responded positively toward EVP messages regardless of the EVP type. However, salient corporate visual identity significantly aroused participants' more attributed firm-serving motives, which led to more negative attitudes toward the company. Next, issue involvement presented its value in EVP communication as highly involved participants displayed favorable attributions of EVPs and positive attitudes toward the company.

This study approaches EVPs as an important CSR practice and expands the discussion on strategic CSR communication by uncovering the roles of central and peripheral cues in public responses to EVP messages. Moreover, the findings highlight the importance of fostering and enhancing publics' issue involvement in achieving successful EVP communication.

Purpose: This chapter aims to examine machine learning (ML) models for predicting credit card fraud (CCF).

Need for the study: With the advance of technology, the world is increasingly relying on credit cards rather than cash in daily life. This creates a slew of new opportunities for fraudulent individuals to abuse these cards. As of December 2020, global card losses reached $28.65billion, up 2.9% from $27.85 billion in 2018, according to the Nilson 2019 research. To safeguard the safety of credit card users, the credit card issuer should include a service that protects customers from potential risks. CCF has become a severe threat as internet buying has grown. To this goal, various studies in the field of automatic and real-time fraud detection are required. Due to their advantageous properties, the most recent ones employ a variety of ML algorithms and techniques to construct a well-fitting model to detect fraudulent transactions. When it comes to recognising credit card risk is huge and high-dimensional data, feature selection (FS) is critical for improving classification accuracy and fraud detection.

Methodology/design/approach: The objectives of this chapter are to construct a new model for credit card fraud detection (CCFD) based on principal component analysis (PCA) for FS and using supervised ML techniques such as K-nearest neighbour (KNN), ridge classifier, gradient boosting, quadratic discriminant analysis, AdaBoost, and random forest for classification of fraudulent and legitimate transactions. When compared to earlier experiments, the suggested approach demonstrates a high capacity for detecting fraudulent transactions. To be more precise, our model’s resilience is constructed by integrating the power of PCA for determining the most useful predictive features. The experimental analysis was performed on German credit card and Taiwan credit card data sets.

Findings: The experimental findings revealed that the KNN achieved an accuracy of 96.29%, recall of 100%, and precision of 96.29%, which is the best performing model on the German data set. While the ridge classifier was the best performing model on Taiwan Credit data with an accuracy of 81.75%, recall of 34.89, and precision of 66.61%.

Practical implications: The poor performance of the models on the Taiwan data revealed that it is an imbalanced credit card data set. The comparison of our proposed models with state-of-the-art credit card ML models showed that our results were competitive.

This study aims to explore the role of green motive (GM) and green dynamic capabilities (GDC) in green innovation (GI) through green value co-creation (GVC). Moreover, this study investigates the moderation of top management support (TMS) to strengthen the mediation of specific constructs; GM, GDC, green value co-creation (GVC) and green innovation (GI).

In total, 337 respondents (executive level/chief executive officer (CEO)) of service organizations were approached using a convenience sampling technique to collect the data through the survey method. Of these, 294 (87% response rate) duly filled responses were used in the final data analysis. In SPSS (Statistical Package for Social Sciences) v-23, the Process Macro-Hayes was used to evaluate the study's conceptual framework empirically.

The study revealed that TMS strengthened the mediation framework of GM, GDC, GVC and GI. Moreover, all hypotheses related to direct and indirect associations of specific constructs used in the theoretical framework were statistically significant and proved.

The comprehensive framework for GI of service organizations, primarily in the context of developing countries like Pakistan, is deficient in literature. This study helps service organizations by providing a comprehensive GI model to put a central focus on the transformation of management philosophy and working approach for achieving GI in the services structure.

The case focuses on the establishment and development of LYFEN, a Chinese leisure food brand. LYFEN created a business model of “small packaging + store.” Through accurate analysis of consumer habits, it quickly became one of the major brands in China's leisure food industry. In the process of entrepreneurship, it grasped the economic opportunity during SARS and quickly bought a large number of stores at low prices, laying the foundation for the rapid development of the follow-up. At the same time, its active practice of introducing information construction also further improved the business's competitiveness. Case B mainly focuses on the external and internal environment of LYFEN after 2015. According to estimates made in 2014, LYFEN's online sales were supposed to double, but LYFEN was gradually falling behind the competition.

The purpose of this study is to provide a nuanced understanding of the marketing placebo effect (MPE) of products with reduced sugar labeling and how it forms certain perceptual underpins (perceived healthiness (PH) and perceived tastiness (PT)), with the potential effect of product category and social class in consideration.

The proposed model is tested using a sample of 822 participants by employing partial least squares structural equation modeling (PLS-SEM). Hypothetical relationships among MPE, PH, PT, purchase intention (PI) and social class are examined for both hedonic and utilitarian products.

The results highlight the positive role of MPE in leveraging consumer PI through the parallel mediation of PH and PT. The positive effect of MPE on PH and PT was more pronounced for the utilitarian product. In addition, social class negatively moderated the relationship between PH and PI only in the case of the utilitarian product.

This paper contributed to the MPE literature in the food industry by challenging the conventional intuition of “Unhealthy = Tasty” and highlighting the potential of perceived food healthiness to positively influence perceived food tastiness under the effect of MPE. An upper social class would attenuate the positive effect of perceived food healthiness on PI.

In the North Hiding of Yorkshire a number of samples of lardine were examined during the second quarter. Four of these samples contained water to the extent of 24, 25, and 26 per cent. respectively. One case was taken into court, but the magistrates dismissed the information. They were of opinion that there being no statutory standard for lardine they could not fix the percentage of water that was permissible. An appeal was made to the High Court, and eventually the case was referred back to the magistrates to determine whether or not there was adulteration.

Despite the growing body of empirical research on leader anger expressions, the issue of how and when leader anger expressions shape newcomers’ proactive career behavior and work alienation in the construction industry has been largely overlooked. Building upon social information processing theory, this research identifies newcomers’ organization-based self-esteem as a mediator, and suggests that newcomers’ performance goal orientation could moderate the relationship.

A questionnaire study was conducted on the construction industry in China, and the PROCESS program developed by Hayes was used to test the hypothetical model with 215 valid cases.

The results suggest that leader anger expressions are negatively associated with newcomers’ organization-based self-esteem, and organization-based self-esteem mediated the link between leader anger expressions and newcomers’ proactive career behavior and work alienation. Furthermore, the newcomers’ performance goal orientation moderates the negative impact of leader anger expressions on newcomers’ organization-based self-esteem.

Due to the cross-sectional nature of this study, causal implications are difficult to draw. Moreover, all data we received was based on participant self-reports, which may raise concerns about common method variance.

In this paper, we contribute to a deeper understanding of the mediating mechanisms and boundary conditions by which leader anger expressions influence newcomers’ proactive career behavior and work alienation from social information processing perspective, in addition to providing valuable insights for management of newcomers in the construction industry.