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This methodological paper demonstrates how service firms can use digital technologies to quantify and predict customer evaluations of their interactions with the firm using unstructured, qualitative data. To harness the power of unstructured data and enhance the customer-firm relationship, the use of computerized text analysis is proposed.

Three empirical studies were conducted to exemplify the use of the computerized text analysis tool. A secondary data analysis of online customer reviews (n = 2,878) in a service industry was used. LIWC was used to conduct the text analysis, and thereafter SPSS was used to examine the predictive capability of the model for the evaluation of customer-firm interactions.

A lexical analysis of online customer reviews was able to predict evaluations of customer-firm interactions across the three empirical studies. The authenticity and emotional tone present in the reviews served as the best predictors of customer evaluations of their service interactions with the firm.

Computerized text analysis is an inexpensive digital tool which, to date, has been sparsely used to analyze customer-firm interactions based on customers' online reviews. From a methodological perspective, the use of this tool to gain insights from unstructured data provides the ability to gain an understanding of customers' real-time evaluations of their service interactions with a firm without collecting primary data.

This research contributes to the growing body of knowledge regarding the use of computerized lexical analysis to assess unstructured, online customer reviews to predict customers' evaluations of a service interaction. The results offer service firms an inexpensive and user-friendly methodology to assess real-time, readily available reviews, complementing traditional customer research. A tool has been used to transform unstructured data into a numerical format, quantifying customer evaluations of service interactions.

This research explores the impacts of content-generating devices (mobile phones versus personal computers) and content features (social content and achievement content) on consumer engagement with marketer-generated content (MGC) on social media. It also examines these factors' interaction effects on consumer engagement.

The study analyzed MGC that 210 companies had posted to Sina Weibo over three years, testing the study’s proposed model with negative binomial regression analysis.

The study's results show that MGC generated via mobile phones attracts more consumer engagement than MGC generated via personal computers. MGC with more social features attracts more consumer engagement, whereas MGC with more achievement features reduces consumer engagement. The authors also found that MGC with more social features generated via mobile phones and MGC with more achievement features generated via personal computers lead to more consumer engagement due to the congruency of the construal level of psychological distance.

This research enriches the literature by exploring the effects of content-generating devices and content features on consumer engagement in the MGC context, which extends the research on consumer engagement with social media from the context of user-generated content to the MGC.

Strategy scholars have widely recognized the central role that narratives play in the construction of organizational identities. Moreover, storytelling is an important strategic asset that firms can leverage to inspire employees, excite investors and engage customers' attention. This chapter illustrates how advancements in computational linguistic may offer opportunities to analyze the stylistic elements that make a story more convincing. Specifically, we use a topic model to examine how narrative conventionality influences the performance of 78,758 craftsmen selling their handmade items in the digital marketplace of Etsy. Our findings provide empirical evidence that effective narratives display enough conventional features to align with audience expectations, yet preserve some uniqueness to pique audience interest. By elucidating our approach, we hope to stimulate further research at the interface of style, language and strategy.

This paper investigates how two characteristics of language, abstractness vs concreteness and narrativity, influence user engagement in communication exercises on innovation targeted to the general audience. The proposed conceptual model suggests that innovation fits well with more abstract language because of the association of innovation with imagination and distal construal. Moreover, communication of innovation may benefit from greater adherence to the narrativity arc, that is, early staging, increasing plot progression and climax optimal point. These effects are moderated by content variety and emotional tone, respectively.

Based on a Latent Dirichlet allocation (LDA) application on a sample of 3225 TED Talks transcripts, the authors identify 287 TED Talks on innovation, and then applied econometric analyses to test the hypotheses on the effects of abstractness vs concreteness and narrativity on engagement, and on the moderation effects of content variety and emotional tone.

The authors found that abstractness (vs concreteness) and narrativity have positive effects on engagement. These two effects are stronger with higher content variety and more positive emotional tone, respectively.

This paper extends the literature on communication of innovation, linguistics and text analysis by evaluating the roles of abstractness vs concreteness and narrativity in shaping appreciation of innovation.

This paper reports conceptual and empirical analyses on innovation dissemination through a popular medium – TED Talks – and applies modern text analysis algorithms to test hypotheses on the effects of two pivotal dimensions of language on user engagement.

Drawing on the dual process theory and the cultural dimension of power distance, the current research investigates the impact of a specific service clue—the linguistic style of address forms (salutation) in hotel manager letters to guests—on customer satisfaction in a hotel context in Ecuador.

Following an experimental design research approach, this research conducted a series of two studies to examine how customers' cultural values (high vs low power distance), linguistic style of address forms (formal vs casual) and service valence (service success vs service failure) together influenced customer satisfaction. Specifically, Study 1 examined the service success condition, and Study 2 investigated the service failure condition.

The research results show that, in the service success condition, customers follow their distinct cultural orientations (high vs low power distance) when responding to the different linguistic styles (formal vs casual). On the other hand, in the service failure situation, as customers desire for expressions of respect that can be reflected in a formal address form, the level of satisfaction is lower when the casual address form is used in guest communications, regardless of customers' cultural orientations in power distance.

This research adds to existing cross-cultural service research, particularly in terms of service valence, and provides practical implications for enhancing service providers' cultural awareness and sociolinguistic competence to effectively communicate with customers from diverse cultural backgrounds.

Instructors at tertiary-level institutions in the Gulf are increasingly encouraged to reflect on their teaching practice. This article is both a reflection on my own practice and an attempt to demonstrate, through recounting a personal experience, how reflection can contribute positively to any teacher's self-knowledge and consequent performance in the classroom.

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

A total of 1,022 individuals took part in the survey; participants were asked to fill out the ten item personality measure questionnaire to capture personality traits and the dysfunctional attitude scale (DAS) to measure dysfunctional beliefs and cognitive vulnerabilities. ML classification models using participant profiling information as input were developed to predict the extent to which an individual was influenced by statements that contained different linguistic styles/persuasion techniques. Several ML algorithms were used including support vector machine, LightGBM and Auto-Sklearn to predict the effect of each technique given each individual’s profile (personality, belief system and demographic data).

The findings highlight the importance of incorporating emotion-based variables as model input in predicting the influence of textual statements with embedded persuasion techniques. Across all investigated models, the influence effect could be predicted with an accuracy ranging 53%–70%, indicating the importance of testing multiple ML algorithms in the development of a persuasive communication (PC) system. The classification ability of models was highest when predicting the response to statements using rhetorical devices and flattery persuasion techniques. Contrastingly, techniques such as authority or social proof were less predictable. Adding DAS scale features improved model performance, suggesting they may be important in modelling persuasion.

In this study, the survey was limited to English-speaking countries and largely Western society values. More work is needed to ascertain the efficacy of models for other populations, cultures and languages. Most PC efforts are targeted at groups such as users, clients, shoppers and voters with this study in the communication context of education – further research is required to explore the capability of predictive ML models in other contexts. Finally, long self-reported psychological questionnaires may not be suitable for real-world deployment and could be subject to bias, thus a simpler method needs to be devised to gather user profile data such as using a subset of the most predictive features.

The findings of this study indicate that leveraging richer profiling data in conjunction with ML approaches may assist in the development of enhanced persuasive systems. There are many applications such as online apps, digital advertising, recommendation systems, chatbots and e-commerce platforms which can benefit from integrating persuasion communication systems that tailor messaging to the individual – potentially translating into higher economic returns.

This study integrates sets of features that have heretofore not been used together in developing ML-based predictive models of PC. DAS scale data, which relate to dysfunctional beliefs and cognitive vulnerabilities, were assessed for their importance in identifying effective persuasion techniques. Additionally, the work compares a range of persuasion techniques that thus far have only been studied separately. This study also demonstrates the application of various ML methods in predicting the influence of linguistic styles/persuasion techniques within textual statements and show that a robust methodology comparing a range of ML algorithms is important in the discovery of a performant model.

Arabic Across the Curriculum is a broad language support program at Zayed University in the UAE, which has stimulated discussion on several issues concerning Arabic language support in the Arab world in general and in the Gulf area in particular. These issues can be summed up in the following questions: Why do we need to teach Arabic to native Arabic-speaking students? How will Arabic language proficiency help students in their academic and future careers? Which Arabic language skills should we teach native speakers in higher education, and how? What means of assessment and what criteria might be helpful to Arabic programs and instructors?

Cognitive computing is part of AI and cognitive applications consists of cognitive services, which are building blocks of the cognitive systems. These applications mimic the human brain functions, for example, recognize the speaker, sense the tone of the text. On this paper, we present the similarities of these with human cognitive functions. We establish a framework which gathers cognitive functions into nine intentional processes from the substructures of the human brain. The framework, underpins human cognitive functions, and categorizes cognitive computing functions into the functional hierarchy, through which we present the functional similarities between cognitive service and human cognitive functions to illustrate what kind of functions are cognitive in the computing. The results from the comparison of the functional hierarchy of cognitive functions are consistent with cognitive computing literature. Thus, the functional hierarchy allows us to find the type of cognition and reach the comparability between the applications.

Negative disclosure tone in 10-K annual reports has economic consequences, yet relatively little is known about how it is generated. Boards of directors play an important governance role with respect to mandatory disclosures and personally sign off on Form 10-K, leading us to expect directors to influence financial reporting narratives. This study investigates whether the negative tone of firms' narrative annual report disclosures is associated with the human and social capital of its board of directors.

Multivariate regression analyses of negative disclosure tone (Loughran and McDonald, 2011) on board members' average age, gender, education, financial expertise and turnover is performed. A host of supplemental tests to corroborate our primary analysis, including using Sarbanes-Oxley's financial expert mandate as an exogenous shock to board composition, impact threshold for a confounding variable, placebo analysis, portfolio tests of more and less negative disclosing firms and portfolio tests of “loud” versus “quiet” boards are conducted.

Evidence that directors' gender, education, financial expertise and board turnover are associated with more negative disclosure tone, while directors' age is associated with less negative disclosure tone is found. The study also looked within the board to differentiate whether these findings are driven by characteristics of inside directors or outside directors serving on the audit committee, or both, as these are the specific groups of directors we would expect to play a role in disclosure. It was found that negative disclosure tone is associated with a lower bid-ask spread, so this study interpreted more negative tone as containing more descriptive information.

This study helps decode the “black box” of annual report disclosure tone, which Loughran and McDonald (2011) show has important economic implications. The results help inform stakeholders such as policymakers, executives and capital market participants as to how board member traits are associated with disclosure. The findings are particularly important as this study bears witness to the increasing prominence of gender/diversity mandates (e.g. Israel, Norway, California) and financial expertise mandates (e.g. Sarbanes-Oxley).