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This paper aims to examine whether Twitter messaging can help mitigate the harm corporations suffer in the aftermath of ethical scandals.

This paper applies Web Application Programming Interfaces (API) on the Guardian and New York Times news archives to find corporations that suffered scandals between 2014 and 2019, revealing 92 publicly listed companies in the UK. Using Twitter API and the Python library, Getoldtweets, this paper extracts historical, pre-scandal – i.e. pre-2014 – tweets of the 92 firms. The paper topic-models the tweets data using Latent Dirichlet Allocation (LDA). This paper then subjects the topics to multidimensional scaling (MDS) to examine commonalities among them.

LDA reveals 10 topics, which group under 5 themes; these are product marketing, urgent signalling of “greenness”, customer relationship management, corporate strategy and news feeds. MDS suggests that the topics further congregate into two meta-themes of future-oriented versus immediate and individual versus global.

Provided they are sincere and legitimate, corporations’ tweets on global issues with a green agenda should help cushion the impact of ethical scandals. Overall, however, the findings suggest that Twitter messaging could be a double-edged sword, and underscore the importance of strategy.

The paper offers a first exploration of the relevance of corporate Twitter messaging in mitigating ethical scandals.

The purpose of this study is to examine whether individual demographic and socio-cultural factors affect actions taken by consumers in relation to ethical violations and failure (or perceived ethical violations and failure) by service operations firms.

Data collection was undertaken over a two-year period, from 2011 to 2013, and involved sampling 3,155 respondents from 19 countries. Data analysis was undertaken utilizing hierarchical linear modelling (HLM).

Findings suggest that although both individual demographic factors (age and gender) and societal differences do affect ethical actions taken by service consumers, inter-societal cluster variations have a more significant effect on the ethical action than individual demographic differences do.

For service operations firms, the study findings offer evidence on the need for constant readjustment of service attributes in line with the ethical dispositions of the different demographic and socio-cultural clusters within the consumer base.

Real operational data are used to optimise the performance measurement of air cargo capacity demand management at Virgin Atlantic Cargo by identifying the best KPIs from the range of outcome-based KPIs in current use.

Intelligent fuzzy multi-criteria methods are used to generate a ranking order of key outcome-based performance indicators. More specifically, KPIs used by Virgin Atlantic Cargo are evaluated by experts against various output criteria. Intelligent fuzzy multi-criteria group making decision-making methodology is then applied to produce rankings.

A useful ranking order emerges from the study albeit with the important limitation that the paper looked solely at indices focussing exclusively on outcomes while ignoring behavioural complexity in the production of outcomes.

This paper offers a practical overview of the development of performance measures useful for air cargo capacity demand management.

This paper seeks to set out opportunities for the development of a UK‐focused feasibility and sizing model utilising linear programming.

Optimisation of the model is conducted using integer linear programming developed using Excel Solver.

When compared with comparable alternatives, the model is shown to be particularly useful as its functionality is embedded in resource intensive prime mover specifications obtained from seven real industrial cases.

The study acknowledges the limitation of utilising sizing data primarily obtained from secondary sources to develop the model.

The practical usefulness of this model is that it has been built using “real”, as opposed to simulated‐data. When compared with comparable alternatives, the model is shown to be articularly useful as its functionality is embedded in resource intensive prime mover specifications obtained from seven real industrial cases.

Current research in the area of risk management within small and medium-sized enterprises (SMEs) appears predisposed towards risk, predominantly dealing with the willingness of SMEs to take on losses. However, in this pilot study, the authors aim to focus on a different aspect of risk management in SMEs, namely the risk preferences. Risk preferences in this case are regarded as the willingness of SME proprietors to take on risks that are likely to lead to investment gains.

Data is gathered via a combination of a survey questionnaire and a probability scenario toolset. The authors sampled a total of 150 SME proprietors operating in Greece. The data was analysed using a combination of regression models and binomial tests.

The results suggest that we cannot, as previous literature suggests, conclude that SME proprietors generally exhibit a negative risk preference.

In light of Greece's recent economic difficulty, and in acknowledgement of the critical role played by SMEs in the Greek economy, this study addresses a topical subject in entrepreneurship research: what are the factors determining investment risk preferences?

Virgin Atlantic Cargo is one of the largest air freight operators in the world. As part of a wider strategic development initiative, the company has identified forecasting accuracy as of strategic importance to its operational efficiency. This is because accurate forecast enables the company to have the right resources available at the right place and time. The purpose of this paper is to undertake an evaluation of current month-to-date forecasting utilized by Virgin Atlantic Cargo. The study employed demand patterns drawn from historical data on chargeable weight over a seven-year-period covering six of the company's routes.

A case study is carried out, where a comparison between forecasting models is undertaken using error accuracy measures. Data in the form of historical chargeable weight over a seven-year-period covering six of the company's most profitable routes are employed in the study. For propriety and privacy reasons, data provided by the company have been sanitized.

Preliminary analysis of the time series shows that the air cargo chargeable weight could be difficult to forecast due to demand fluctuations which appear extremely sensitive to external market and economic factors.

The study contributes to existing literature on air cargo forecasting and is therefore of interest to scholars examining the problems of overbooking. Overbooking which is employed by air cargo operators to hedge against “no-show” bookings. However, the inability of air cargo operators to accurately predict cargo capacity unlikely to be used implies that operators are unable to establish with an aspect of certainty their revenue streams. The research methodology adopted is also predominantly discursive in that it employs a synthesis of existing forecasting literature and real-life data for accuracy analysis.

The way and manner in which energy is produced is known to have a significant impact on emissions. For this reason, the UK government has sought to enhance the efficiency of energy production/conversion by focusing on a number of energy production approaches, including Combined Heat and Power (CHP). The purpose of this paper is to describe a practical approach for assessing the feasibility of CHP.

The authors provide an overview of Combined Heat and Power (CHP); describe a new and easy‐to‐implement feasibility and optimisation model to aid in the installation of CHP; and discuss the practical feasibility issues of CHP through an analysis of existing case studies using the proposed model. The modelling utilises regression models which are created using historical data obtained from public sources.

Compared against alternatives, the model is shown to be particularly useful, as its functionality is embedded in resource‐intensive prime mover specifications obtained from seven real industrial cases.

The need for such a practical and easy‐to‐use model is driven by the existence of numerous models, which are mainly complex and not necessarily “user‐friendly”. The proposed model is set to provide a practical and user‐friendly model for CHP appraisal that is easy to understand and assess in terms of prime movers such as capital cost, payback, annual financial and CO₂ savings.

This paper aims to present a smart, sustainable supply chain practices structure on the basis of the relational view.

A method based on fuzzy cognitive map was applied to construct a relational map to introduce and implement such relational methods. Considering this relational map as a guideline, observations into particular methods and ways of applying relational methods to attain sustainable development goals across organizations has been introduced.

Primary outcomes provided a series of relational methods for the purpose of giving advice to those organizations and their suppliers for smart, sustainable supply chain. Reliance between relational methods were examined and assessed under seven meaningful groups: economic internet of things (IoT), green internet of things, social internet of things, economic supply chain, green supply chain, social supply chain and other variables.

This study guides managers toward an improved perception of the connection among IoT instances and sustainable supply to modeling smart, sustainable supply chain. Managers can determine the practices that need more focus along with the practices that are less relevant. Thus, this will help managers in the decision-making process and to organize their decisions by planning and calculating the relative importance and influence of smart, sustainable practices on each other and on the company’s smart, sustainable program.

To the best of the authors’ knowledge, this is the first approach that promptly examines and determines the interdependencies between relational methods and constructs a relational map for the purpose to introduce and analyze smart, sustainable supply chain.

This paper examines the dividend smoothing (DS) behaviour in the Gulf Cooperation Council (GCC) countries in emerging markets where the response to news and the economic environment are different from those of developed countries.

The authors examine the effect of share price informativeness on DS in the GCC markets using unbalanced panel data for a sample of 628 GCC-listed firms during 1994–2016. For the regression analysis, the hypotheses are tested using panel regressions and generalised method of moments (GMM) estimation.

First, the Lintner model shows that the DS degree in GCC firms is comparable to that of a developed market. Second, and importantly, the results reveal that the DS in GCC firms is sensitive to private information of share prices. Finally, the findings indicate that information asymmetry (IA) and agency-based models affect the tendency to smooth dividends in the GCC markets.

This study is the first study to measure the degree of DS using data for all GCC countries. The authors also identify other determinants of DS behaviour and test the agency and IA explanations for DS in GCC-listed firms. The findings are highly recommended to financial managers and analysts dealing with the GCC markets. This study helps financial analysts to use the share price informativeness as an indicator for the presence of the IA. The study results are beneficial to researchers in understanding the relationship between DS and share price informativeness.