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Despite the availability of many metrics and tools for marketing performance measurement, the way in which firms use their marketing metrics remains underexplored. This study aims to address this gap by empirically establishing the differing effects of the diagnostic and interactive uses of marketing metrics on firms’ market-sensing capability, contingent on competitive intensity and focus on market-related metrics.

This study builds on survey data collected from 210 Irish-based firms, complemented by 21 in-depth interviews with business managers. Survey data are analysed using regression analysis.

This study finds that firms using marketing metrics interactively to communicate organizational focus are better able to sense their markets, especially under high competition. The authors observe a positive impact of the interactive use of metrics on market-sensing capability, but a U-shaped impact of their diagnostic use, the magnitudes of which further depend on competitive intensity and firms’ focus on market-related metrics.

This study provides a nuanced view of marketing performance measurement (MPM) practices within firms, particularly focussing on diagnostic versus interactive uses of marketing metrics. It also sheds further light on how two diverse uses of marketing metrics – diagnostic and interactive uses – influence a firm’s market-sensing capability. Moreover, the identification of boundary conditions also contributes to the discussion of contextuality in MPM, highlighting the importance of aligning a firm’s uses of marketing metrics with its business environment.

This study provides novel insights into how diverse uses of marketing metrics may benefit firms. The differing effects of diagnostic and interactive uses of marketing metrics on market sensing highlight a primary need for developing the latter and for using the former only with caution. It establishes that all firms would equally benefit from an interactive use of marketing metrics that is pivotal to improving their ability to anticipate, detect and sense market changes.

This study provides novel understanding of the role of marketing metric uses in firms’ market-sensing capability and contributes to the discussion of contextuality in marketing performance measurement. It highlights the importance of aligning a firm’s use of marketing metrics with its business environment.

This paper presents a follow-on study that quantifies geolingual markers and their apparent connection with authorship collaboration patterns in canonical Digital Humanities (DH) journals. In particular, it seeks to detect patterns in authors' countries of work and languages in co-authorship networks.

Through an in-depth co-authorship network analysis, this study analysed bibliometric data from three canonical DH journals over a range of 52 years (1966–2017). The results are presented as visualised networks with centrality calculations.

The results suggest that while DH scholars may not collaborate as frequently as those in other disciplines, when they do so their collaborations tend to be more international than in many Science and Engineering, and Social Sciences disciplines. DH authors in some countries (e.g. Spain, Finland, Australia, Canada, and the UK) have the highest international co-author rates, while others have high national co-author rates but low international rates (e.g. Japan, the USA, and France).

This study is the first DH co-authorship network study that explores the apparent connection between language and collaboration patterns in DH. It contributes to ongoing debates about diversity, representation, and multilingualism in DH and academic publishing more widely.

This paper presents a co-authorship study of authors who published in Digital Humanities journals and examines the apparent influence of gender, or more specifically, the quantitatively detectable influence of gender in the networks they form.

This study applied co-authorship network analysis. Data has been collected from three canonical Digital Humanities journals over 52 years (1966–2017) and analysed.

The results are presented as visualised networks and suggest that female scholars in Digital Humanities play more central roles and act as the main bridges of collaborative networks even though overall female authors are fewer in number than male authors in the network.

This is the first co-authorship network study in Digital Humanities to examine the role that gender appears to play in these co-authorship networks using statistical analysis and visualisation.

In North America, the profession known as ‘research management’ elsewhere across the globe, is often known as ‘research administration’ and encompasses the activities and work associated with developing, administering, accounting for and complying with sponsor requirements, guidelines, procedures, and laws relating to externally funded projects. In the United States and Canada, the expansion of respective federal government agencies and programmes was the major factor for the need and growth of the research administration profession. Initially, administrative and business staff often administered research funding, however over the decades, a fully-fledged profession has evolved with distinct specialisations. Both the United States and Canada now have maturing professions and professional societies to organise and advance research administration. This chapter outlines the chronological origins, growth, and professionalisation of research administration in North America, with a focus on the United States and Canada. Mexico has not yet evolved a formalised research administration infrastructure.

This paper aims to define the efficiency achieved by urban transport companies in Spain concerning the resources they use, considering the type of management used for implementation, public-private, and size.

This study consisted of an analysis of the efficiency of 229 public-private urban transport operators during the period 2012–2021 using Data Envelopment Analysis, the Malmquist Index and inference estimators to determine productivity, efficiency change into Pure Technical Efficiency Change (PTECH), and scale efficiency change.

Based on the efficiency analysis, the authors concluded that of the 229 companies studied, more than 35 were inefficient in all analysed periods. Considering the sample used, direct management is considered significantly more efficient. It cannot be concluded that the size of these companies influences their efficiency, as the data show unequal development behaviours in the studied years.

This study provides arguments on whether there is a significant difference between the two types of management in the urban transport sector. It also includes firm size as a study variable, which has not been previously considered in other studies related to urban transport efficiency. Efficiency should be a crucial factor in determining funding allocation in this sector, as it encourages operators to optimize and improve their services.

India is a fast-growing economy, that has a majority share in the global information technology industry (IT). Rapid urbanisation and modernisation in India have strained its energy sector, which is being reformed to cope. Despite being the global IT heart and having above average research output in the field of artificial intelligence (AI), India has not yet managed to leverage its benefits to the full. This study aims to address the role of AI and information management (IM) in India’s energy transition to highlight the challenges and barriers to its development and use in the energy sector.

The study, through analysis of proposed strategies, current policies, available literature and reports, discusses the role of AI and IM in the energy transition in India, highlighting the current situation and challenges.

The results show dispersed research and development incentives for IT in the Indian energy sector; however, the needed holistic top-down approach is lacking, calling for due attention in this matter. Adaptive and swift actions from policymakers towards AI and IM are warranted in India.

The ongoing transition of the Indian energy sector with the integration of smart technologies would result in increased access to big data. Extracting the maximum benefits from this would require a comprehensive AI and IM policy.

The revolution in AI and robotics must be carried out in line with sustainable development goals, to support climate action and to consider privacy issues – both areas in India must be strengthened.

The paper offers an original discussion on certain applicable solutions regarding the energy transition of AI coming from the Global South; they are based on lessons learned from the Indian case studies presented in this study.

This study examines voluntary cybersecurity risk disclosures (VCRD) by listed Indian companies. It also investigates how it relates to firm-specific characteristics such as size, leverage, profitability, liquidity, beta, market growth and industry.

The extent of VCRD was measured by assessing the cumulative occurrence of cybersecurity risk keywords in the annual report of 100 listed Indian non-financial companies. Keyword extraction and occurrence counts were performed using Python software. A multiple regression analysis was applied to predict the characteristics of VCRD.

The results showed that the theoretical frameworks underpinned by agency and signalling theories continued to provide a valid explanation of VCRD by Indian companies. Specifically, the findings emphasized the importance of firm size, leverage, and beta as significant VCRD determinants. Additionally, the study found that knowledge-intensive industries had a favourable impact on the extent of VCRD.

This study is relevant because it informs company management, regulators and investors about the nature and characteristics of companies that satisfy stakeholder demands to prevent cyber breaches.

Understanding disclosure characteristics is crucial from policy and regulatory perspectives. Studies on cybersecurity disclosures are related to developed economies such as the United States of America and Canada. This is the first study to explore this issue in a developing nation, in general, and in India, in particular, where cybersecurity risk disclosure has yet to be recognized.

This study aims to understand the perceived emotions of human–artificial intelligence (AI) interactions in the private sector. Moreover, this research discusses the transferability of these lessons to the public sector.

This research analysed the comments posted between June 2022 and June 2023 in the global open Reddit online community. A data mining approach was conducted, including a sentiment analysis technique and a qualitative approach.

The results show a prevalence of positive emotions. In addition, a pertinent percentage of negative emotions were found, such as hate, anger and frustration, due to human–AI interactions.

The insights from human–AI interactions in the private sector can be transferred to the governmental sector to leverage organisational performance, governmental decision-making, public service delivery and the creation of economic and social value.

Beyond the positive impacts of AI in government strategies, implementing AI can elicit negative emotions in users and potentially negatively impact the brand of private and government organisations. To the best of the authors’ knowledge, this is the first research bridging the gap by identifying the predominant negative emotions after a human–AI interaction.