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Movies critics believe that the diversity of Iranian cinematic genres has decreased over time. The paper aims to answer the following questions: What is the impact of the continuous cooperation between the key nodes on the audience's taste, uniformity of the cinematic genres and the box office? Is there any relationship between the importance of actors in the actors' network and their popularity?

In the artistic world, artists' relationships lead to a network that affects individuals' commercial or artistic success and defines the artwork's value. To study the issue that the diversity of Iranian cinematic genres has decreased over time, the authors utilized social network analysis (SNA), in which every actor is considered a node, and its collaboration with others in the same movies is depicted via edges. After preparing the desired dataset, networks were generated, and metrics were calculated. First, the authors compared the structure of the network with the box office. The results illustrated that the network density growth negatively affects box office. Second, network key nodes were identified, their relationships with other actors were inspected using the Apriori algorithm to examine the density cause and the cinematic genre of key nodes, and their followers were investigated. Finally, the relationship between the actors' Instagram follower count and their importance in the network structure was analyzed to answer whether the generated network is acceptable in society.

The social problem genre has stabilized due to continuous cooperation between the core nodes because network density negatively impacts the box office. As well as, the generated network in the cinema is acceptable by the audience because there is a positive correlation between the importance of actors in the network and their popularity.

The novelty of this paper is investigating the issue raised in the cinema industry and trying to inspect its aspects by utilizing the SNA to deepen the cinematic research and fill the gaps. This study demonstrates a positive correlation between the actors' Instagram follower count and their importance in the network structure, indicating that people follow those central in the actors' network. As well as investigating the network key nodes with a heuristic algorithm using coreness centrality and analyzing their relationships with others through the Apriori algorithm. The authors situated the analysis using a novel and original dataset from the Iranian actors who participated in the Fajr Film Festival from 1998 to 2020.

Anecdotal evidence suggests that casting actors with similar facial features in a movie can pose challenges in foreign markets, hindering the audience's ability to recognize and remember characters. Extending developments in the literature on the cross-race effect, we hypothesize that facial similarity – the extent to which the actors starring in a movie share similar facial features – will reduce the country-level box-office performance of US movies in East and South-East Asia (ESEA) countries.

We assembled data from various secondary data sources on US non-animation movies (2012–2021) and their releases in ESEA countries. Combining the data resulted in a cross-section of 2,616 movie-country observations.

Actors' facial similarity in a US movie's cast reduces its box-office performance in ESEA countries. This effect is weakened as immigration in the country, internet penetration in the country and star power increase and strengthened as cast size increases.

This first study on the effects of cast's facial similarity on box-office performance represents a novel extension to the growing literature on the antecedents of movies' box-office performance by being at the intersection of the two literature streams on (1) the box-office effects of cast characteristics and (2) the antecedents, in general, of box-office performance in the ESEA region.

This study explores the direct relationship between social media user-generated content (UGC), online search traffic and offline light vehicle sales of different models.

The long-run equilibrium relationship and short-run dynamic effects between the valence and volume of UGC, online search traffic and offline car sales are analyzed by applying the autoregressive distribution lag (ARDL) model.

The study found the following. (1) In the long-run relationship, the valence of online reviews on social media platforms is significantly negatively correlated with the sales of all models. However, in the short-run, the valence of online reviews has a significant positive correlation with all models in different lag periods. (2) The volume of online reviews is significantly positively correlated with the sales of all models in the long run. However, in the short run, the relationship between the volume of online reviews and the sales of lower-sales-volume cars is uncertain. There is a significant positive correlation between the volume of reviews and the sales of higher-sales-volume cars. (3) Online search traffic has a significantly negative correlation with the sales of all models in the long run. However, in the short run, there is no consistent conclusion on the relationship between online search traffic and car sales.

This study provides a reference for managers to use in their efforts to improve offline high-involvement product sales using online information.

While there has been extensive research on understanding the effects of online reviews on product sales, there is not enough investigation of the inter-relationships between online reviews, online search and product sales. The study attempts to address this gap in the context of the Indian car market.

The research uses text mining and considers six important review features volume, valence, length, deviation of valence, sentiment and readability within the heuristic and systematic model of information processing. Panel data regression is used along with mediation analysis to study the inter-relationships between features of reviews, online search and sales.

The study finds that numerical heuristic features significantly affect sales and online search, numerical systematic feature affects sales and the textual heuristic and systematic features do not affect sales or online search in the Indian car market. Further, online search mediates the association between features of reviews and sales of cars.

Although only car sales data from India is considered in this research, similar relationships between review features, online search and sales could exist for the car market of other countries as well.

This research uncovers the unique role of online search as a mediator between review features and sales, whereas prior literature has considered review features and online search as independent variables that affect sales.

The purpose of this paper is to analyze to what extent and in what ways signaling theory has been explored within the field of international marketing. This paper systematically reviews the use of signaling theory in the field of international marketing. Communication is a core aspect of the international marketing process. Research in this field has explored effective and unique ways of improving the communication flow to reduce the asymmetry of information between international consumers and the firm. This notion is adopted, enhanced and strengthened by signaling theory. Signaling theory has recently received the attention of international marketing scholars.

The systematic review methodology was applied for the purpose of identifying the relevant studies. We extracted academic articles over the last 23 years from the domain of international marketing that directly contribute to signaling theory based on 57 journal articles extracted through the systematic review process.

Based on systematic research the results reveal that the topic has grown and continues to expand within the broader international marketing field. We offer a theoretical conceptual framework to better understand signaling theory in the context of international marketing.

The authors map and critically evaluate the use of signaling theory in international marketing. Relevance of signaling theory in international marketing is growing and authors present an integrative framework that organizes the existing literature, and provides scholars to further expand on emerging themes of the domain. The paper offers some useful future research directions.

This case was written using primary data through various channels, including in-depth structured interviews with the CEO and other individuals at the Crisis Center of Tampa Bay (CCTB), as well as exchanging email messages and phone conversations with employees at CCTB. All interviews were recorded and transcribed. In addition, one of the authors took a tour of the main offices of CCTB and took notes on the physical facilities as well as the information provided by the tour guide. Public information from CCTB was used to enhance the information and provide background. All accounts presented in this case are real, and no information was altered or fabricated.

Clara Reynolds had been CEO of CCTB for over eight years. The agency had almost tripled its budget in the time she had been there. Her leadership style had positively impacted the culture of the organization. Employees valued her open and transparent leadership style. Employees saw her commitment to training employees, creating work–life balance and helping employees be exceptional at their jobs. There was an issue, however, with Transcare, the organization’s ambulatory service. The performance of the business was declining, and Clara wanted to update the board within 60 days at the next quarterly board meeting. She was not sure what she could do to increase engagement with Transcare’s staff, which would show the board that the staff was fully willing to do what was necessary.

This case is appropriate for teaching undergraduate or graduate-level courses in leadership, organizational behavior or principles of management. It is designed to be discussed during one class period. It will save time and improve the flow if the students read the case before class and are prepared when they arrive. Any information needed for the case discussion has been presented in the case; no further research by the students is necessary. Students should think about the role of leadership in a nonprofit. They should put themselves in the protagonist’s shoes throughout the reading of the case.

The hygiene factor is always imperative when customers consider a certain restaurant, and the information contained in customer reviews can be an efficient approach to gauge a restaurant's hygiene during gaps in the official inspection. Therefore, the purpose of this study was to investigate whether information obtained from online reviews could predict the upcoming hygiene rating, specifically, evaluating the impact of both qualitative and quantitative content of reviews on the restaurant hygiene rating.

The quantitative research method with big data analytic techniques was applied in this study. In total, 127,656 pieces of data collected from 1,710 restaurants in four major cities in the USA were used in the analysis. Both quantitative factors (i.e. reviewer's numerical rating, days to review, readability, useful/funny/cool) and qualitative factors (i.e. eight emotional dimensions of textual reviews) were analyzed from the online customer reviews and considered in predicting the restaurant's hygiene rating.

Six out of eight emotional dimensions including anger, disgust, fear, sadness, surprise and trust were identified as having significant influences on the restaurant hygiene ratings. While three quantitative variables including days to review, readability and usefulness were identified with significant impacts on the dependent variable of restaurant hygiene rating.

This study opens an avenue for innovative research that establishes a connection between customers' reviews and restaurants' inspection systems. The results allow restaurants to predict an impending hygiene inspection rating upon dynamic assessment of review content and aid in adjusting hygiene measures accordingly.

Starting from intention–behaviour models and building upon the growing evidence that aggregated internet search query data represent a good proxy of human interests and intentions. The purpose of this study is to demonstrate that the internet search traffic information related to the selected key terms associated with establishing new businesses, reflects well the dynamics of entrepreneurial activity in a country and can be used for predicting entrepreneurial activity at the national level.

Theoretical framework is based on intention–behaviour models and supported by the knowledge spillover theory of entrepreneurship. Monthly data on new business registration from 2018 to 2021 is derived from the open database of the Russian Federal Tax Service. Terms of internet search interest are identified through interviews with the recent founders of new businesses, whereas the internet search query statistics on the identified terms are obtained from Google Trends and Yandex Wordstat.

The results suggest that aggregated data about web searches related to opening a new business in a country is positively correlated with the dynamics of entrepreneurial activity in the country and, as such, may be useful for predicting the level of that activity.

The results may serve as a starting point for a new approach to measure, monitor and predict entrepreneurial activities in a country and can help in better addressing policymaking issues related to entrepreneurship.

To the best of the authors’ knowledge, this study is original in its approach and results. Building on intention–behaviour models, this study outlines, to the best of the authors’ knowledge, the first usage of big data for analysing the intention–behaviour relationship in entrepreneurship. This study also contributes to the ongoing debate about the value of big data for entrepreneurship research by proposing and demonstrating the credibility of internet search query data as a novel source of quality data in analysing and predicting a country’s entrepreneurial activity.

This secondary source case is based mainly on legislative documents (that tracked the initiation and progress of the Parental Rights in Education bill that later became an Act), corporate documents (published by The Walt Disney Company) and news articles from publications such as The New York Times and Bloomberg. All sources are cited in the case narrative and as end notes.

In April 2022, The Walt Disney Company and its CEO, Robert Chapek, were at the center of a controversy over the company’s opposition to the State of Florida’s Parental Rights in Education bill. The bill, dubbed “Don’t Say Gay” by its critics, prohibited instruction on sexual identity and gender orientation in the state’s elementary schools. The controversy stemmed from Disney’s initial non-reaction to the bill and its later strident opposition and call for its repeal. Chapek was pressured by negative media publicity and employee disgruntlement on the one hand and adverse economic consequences for opposing the bill by the state’s Governor, Ron DeSantis. Chapek and the Board had to respond to the political threats to Disney’s economic well-being while appeasing its employees and other stakeholders who wanted the company to be a corporate champion in diversity, equity and inclusion.

The case is best suited for advanced undergraduate or graduate leadership, strategic management and marketing courses. From a leadership and strategic management perspective, the case is well-suited for demonstrating the evolving expectations of leaders and corporate social responsibility, as well as the concepts of issue framing and nonmarket management. Instructors may also leverage the case in marketing courses (e.g. brand management), as CEO activism (i.e. messaging and practice) is one characteristic of brand activism (Animation Guild, 2022).