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Internet use has a high environmental footprint that is often overlooked by end users. This paper contributes to limiting the negative environmental footprint of Information Technology (IT) use by understanding the relationship between environmental concerns and use of IT amongst users who are aware of the environmental footprint of IT use. Second, the paper argues that taking environmental concerns into account in technology acceptance studies is relevant, even in studies concerning ordinary IT (i.e. IT not commonly classified as “green” technology).

The authors conduct two vignette-based surveys in two different countries: the USA and Belgium. Partial least squares structural equation modeling (PLS-SEM) is used to analyse the effect of environmental concerns on the intention to use the webcam during online meetings and binary logistic regression is used to analyse the relationship between environmental concerns and reported actual use of webcams.

The higher the respondents' environmental concerns, the higher their intention to use internet systems in a more environmentally responsible way, provided the respondents are aware of the environmental footprint of internet system use. Moreover, the higher the respondents’ environmental concerns, the more likely they are to use internet systems in a more environmentally responsible way.

This study is the first to distinguish “Greening of IT Use” from “Greening of IT” and “Greening by IT” and to show that environmental concerns has an impact on the way end users (intend to) use internet systems, provided the users are aware of the environmental footprint of that use.

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

Little is known about what emotionally un(intelligent) CEOs really say to their close collaborators within the boardroom. Would the rhetoric content differ between an emotionally intelligent and an emotionally unintelligent CEO, especially during a crisis? This chapter aims to answer this question.

40 CEOs of large corporations were asked to deliver a verbal address to their board members in reaction to a vignette describing a critical situation for the company. Participants were provided with the Schutte self-report emotional intelligence (EI) test. The verbal content of CEOs' closed-door discourses was analyzed using Cognitive-Discursive Analysis (CDA) and, subsequently, Geometric Data Analysis (GDA).

The results revealed that CEOs with low EI tend to evoke unpleasant emotions, talk about competition, and often blame some – or all – of the board members for their (poor) actions in comparison to CEOs with high or medium EI. In contrast, CEOs with high EI tend to use terms in relation to decision or realization and appear to be more cooperative than those with lower EI and were also ready to make decisions on behalf of team.

Previous research has mainly focused on CEOs' public speeches. But the content of CEOs' speeches within the boardroom might noticeably differ from what they would say in a public address. The results of our exploratory study can serve CEOs as a basis toward improving their closed-door rhetoric during a crisis.

It would be interesting to enlarge the size of our population in order to strengthen our statistical analyses as well as explore other cultural and linguistic environments and other channels through which emotions can be expressed (e.g., human face, gesture, vocal tone).