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The term corporate social responsibility (CSR) is not new as it has its root in the past, but now it becomes more complicated today. It has been observed that CSR is the building block of social capital and globalisation is the main factor which has forced the market and business to interact with the people either they are from their own place or from different part of the country. At the time of interaction, people interact with the differences of their cultures, beliefs and they have different hopes from the companies and its place in society. This reality of interaction with many differences provides us a direction to explore the cross-cultural variations and to know how it helps in creating the social responsibility policy by the government in different countries and its implementation by business people, employees and other stakeholders. This theoretical review is aimed to explore the role of institutionalisation theory for increasing the social capital and ensuring positive CSR practices in cross-cultural scenario.

Linked data can provide unique insights into both the need for social care services and the outcomes of intervention. Crucially, these insights will be based on much more representative coverage of the population of people receiving social care than is achieved by surveys and they are not subject to the reporting bias that can arise in relation to stigmatised services.

The opportunities are expanding for linking together routine administrative data from different public services, e.g. health care, social care, education and criminal justice. These linked data can be used for research in trusted research environments which are very secure and where no researcher can identify individuals. Work is rapidly developing using children’s social care data.

Much of the data linkage research using children’s social care data is being conducted by data scientists and medical researchers without knowledge of the sector, and very few social care or social work specialists who have that knowledge are involved in these studies.

This viewpoint piece argues the need for social care and social work research to embrace data linkage. What is needed is an integration of methods expertise in linked data and substantive knowledge of children’s social care work. The arguments are illustrated with reference to some recent research in Wales.

Brands increasingly coappear in television programs while research in product placement has primarily focused on the placements of a single brand. Building on research related to product placement and cobranding, this study aims to systematically examine the roles of product competitiveness and brand competitiveness on the effectiveness of brand coappearance on television programs.

Extensive pretesting and four experimental studies were conducted. Real stimuli that had been digitally manipulated with fictitious brands were used in Study 1 (laboratory experiment involved student samples) and Study 2 (online experiment with a national sample) to examine the short- and long-term impacts of product competitiveness on brand coappearance. Real stimuli incorporated actual brands were used in Study 3 (involved advertisers’ key demographic) and Study 4 (alterative television program with a national sample) to examine the impacts of brand competitiveness and its interaction effect with product competitiveness.

The study found that coappearing with a product of high competitiveness significantly enhanced attitudes and purchase intention toward the coappearing products both in the short and long term. Product competitiveness further interacts with brand competitiveness to influence attitudes and purchase intention toward the coappearing brands suggesting a coopetition pattern for brand coappearances. The effect of brand coappearances did not vary substantially for low or high involvement products with or without character interaction.

The study develops a useful framework for explaining and understanding the potential spillover effects in brand coappearances. It contributes to the existing literature on product placement and cobranding, while also paving the way for future research opportunities.

When introducing new brands, marketers are advised to consider coappearance deals with more competitive brands in highly competitive product categories. Conversely, coappearance deals with less competitive brands in less competitive product categories should be adopted to promote well-known brands. Advertisers may also consider product or brand exclusivity arrangements with broadcasters to enhance the effectiveness of the product placement.

Although brand coappearance in media content is likely to continue to proliferate, little is known about the phenomenon and its effects. To the best of the authors’ knowledge, this research is the first to systematically examine the perceptions toward brands coappeared in television programs.

The purpose of this article is to determine the regional economic factors and bank characteristics that significantly contribute to changes in bank liquidity. We also seek to identify regions that may be most susceptible to liquidity tightening.

For this article we use data on deposits from commercial banks, Federal Reserve survey data and indicators of regional and agricultural economic conditions. We specify a panel regression with fixed effects to model how liquidity at agricultural banks has changed and to identify the most significant drivers.

Our results suggest that small banks and banks with branch networks located in areas more concentrated in agricultural production bear the greatest risk of reduced liquidity.

Prior to the pandemic and more recently, lower deposit growth, combined with strong demand for agricultural loans, has led to reductions in liquidity at agricultural banks. Lower liquidity could reduce credit availability for farm borrowers and increase risks for banks that must rely on alternative sources of funding to meet loan demand.

Previous research has shown that exogenous shocks from other economic sectors, such as energy, can significantly affect bank liquidity, but research is limited on how agricultural bank liquidity is affected by downturns in the agricultural economy and other regional economic factors. Another contribution is this paper’s analysis of regional disparities in bank liquidity.

Recent calls in international management (IM) research ask scholars to conduct more context-sensitive research, however; little attention has been paid to the methodological particularities that inform such context sensitivity. This paper aims to addresses this shortcoming by exploring how emic concepts implicate IM research processes during qualitative field studies.

We carried out ethnographic fieldwork in Brazilian subsidiaries of three German multinational enterprises. We relied on the researchers’ experiences and data from a larger research project including 63 semi-structured interviews, 7 focus groups, documents and field notes. Adopting a culturally sensitive and self-reflexive lens, we reflect on the researchers’ experiences in the Brazilian sociocultural context from an interpretive paradigm.

Our findings reveal how seven identified emic concepts affect four prototypical phases of the research process: securing access, collecting data, analyzing data and presenting findings. We discuss how these seven emic concepts influenced the research process and impacted research outcomes, as experienced by the researchers.

Findings are limited by our self-reflexive capabilities as foreign researchers, the limited explanatory power of emic categories, our paradigmatic positioning and the research context.

We contribute to research practice by providing eight suggestions for conducting international fieldwork and proposing avenues for future research.

This research contributes to the epistemological and methodological debate on context-sensitive research by arguing that intercultural sensitivity needs to be managed as an integral dimension for any form of international fieldwork. Findings contribute to interpretive approaches showing how emic concepts affect research practices, with implications for critical management perspectives.

This study aims to investigate the factors influencing public servants’ anxiety and general public’s opposition toward the implementation of digital participatory platforms (DPPs) and municipal digital transformation (DX) in Japan. By addressing these factors, the research seeks to provide insights for policymakers to facilitate smoother transitions to digital governance and increase public acceptance and engagement.

The study uses surveys conducted with both general public and public servants. The surveys were designed based on previous research and collected data through Web-based questionnaires. General public’s data were collected from 366 valid responses over four days in July 2022, while public servants’ data were gathered from 197 valid responses over eight days. Statistical analysis was used to identify key factors influencing anxiety and opposition.

Public servants’ anxiety is influenced by self-perceived creativity, openness to innovation, international collaborations, work-life balance and gender equality reforms. The general public’s opposition stems from dissatisfaction with current digital tools, social media use and political engagement. Both groups could benefit from targeted training, improved usability and inclusive engagement strategies.

The findings suggest that targeted training to enhance public servants’ creativity and digital literacy, fostering an innovative organizational culture and promoting work-life balance can reduce anxiety about DPPs. For general public, improving the usability of digital tools, engaging them in the design process and leveraging social media for communication and feedback can increase acceptance of digital initiatives.

Understanding the concerns of both public servants and general public regarding DX can lead to more inclusive and effective governance. By addressing these concerns, policymakers can foster greater public trust and engagement, ultimately enhancing the effectiveness and transparency of municipal governance.

This study provides a comprehensive analysis of the factors contributing to resistance to DX in public governance. By examining both public servants’ and general public’ perspectives, it offers valuable insights for designing and implementing strategies to facilitate smoother transitions to digital participatory governance.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

With the recent conclusion of the United Nations Conference of the Parties (COP) 28 in the United Arab Emirates, this study aims to investigate the tweeting behaviour of firms surrounding COP events. The authors analyse the environmental, social and governance (ESG) tweets from the COP 26 and COP 27 events, aiming to deepen the understanding of the complex relationships between social media communication, industry characteristics and financial performance. This timely analysis is critical for assessing how the latest global discussions on climate change are influencing corporate communication strategies on sustainability, offering fresh insights into the evolving dynamics of ESG engagement in the context of these pivotal international meetings.

In this study, the authors embrace a grounded theory approach to gain insights into the ESG and sustainability initiatives presented by companies on social media, with an intensified focus on climate change discourse. Leveraging advanced social media analytics, this study expands its scope by conducting a thorough examination of ESG-related tweets from Standard and Poor’s (S&P) 500 companies. In addition, the authors explore the relationships between such communication efforts and financial performance, applying an advanced cumulative abnormal returns (CARs) model. This methodological enhancement enables a more sophisticated understanding of how ESG communication on Twitter correlates with, and potentially influences, a firm’s market valuation and financial health, offering invaluable insights into the strategic importance of digital sustainability discourse.

The research findings introduce four novel distinct groups – Unengaged, Catalysts, Cautious and Shapers – based on firms’ proactive or reactive sustainability communication patterns. The results explore the potential impact of COP event locations on tweeting behaviour, proposing that conferences held in different regions, such as Asia versus Europe, may elicit varied reactions from S&P 500 firms. Despite no significant inter-industry differences in tweeting habits, the authors discover a significant link between firms’ financial metrics, specifically CARs, and their categorised communication styles. The results challenge the simplistic view that higher social media engagement leads to positive financial outcomes, suggesting instead that lower financial performance may drive firms to adopt more extreme communication patterns, possibly as a strategic move to enhance corporate legitimacy.

This study offers new insights into how companies use social media during significant climate change events, namely, COP events. By classifying firms according to their ESG communication approaches, the results reveal uncharted correlations between how companies communicate on social media, namely, Twitter, and the correlation to financial performance.

City–university partnerships (CUPs) are emerging as dynamic collaborations addressing urban challenges in various cities. This chapter delves into the transformative dynamics of the CUP in Valencia, particularly within the framework of the European Union (EU) Cities Mission. Valencia, a recognized leader, achieved the EU Mission Label in 2023, showcasing the success of its collaboration with the Polytechnic University of València (UPV). The Valencian CUP functions as a multi-faceted entity, serving as a knowledge powerhouse, an innovation catalyst in urban policies, and a vital educational space. The exploration unfolds various dimensions of the CUP’s impact, emphasizing its contributions to the city’s development model, innovation in urban policies, and transdisciplinary education. Examining collaborations and transformations leading to CUP creation and effective functioning through the multi-level perspective (MLP) framework illuminates a complexity of the CUP in Valencia. It also highlights the catalytic role of the EU Cities Mission in shaping CUP creation. In conclusion, the CUP in Valencia stands as a beacon of inspiration and a blueprint for global urban centers navigating the path toward climate neutrality. This chapter contributes valuable insights to the broader discourse on the transformative potential of CUPs, acknowledging the narrative as ongoing and calling for further research to uncover their full potential in shaping resilient and sustainable urban landscapes.

This paper introduces the YouWalk-UOS mobile application, a tool that revolutionises the assessment of urban open spaces (UOS). The paper demonstrates how integrating real-time, on-ground observations with users’ reactions into a digital platform can transform the evaluation of urban open spaces. It seeks to address the existing shortcomings of traditional UOS assessment methods and underscore the need for innovative, adaptable and inclusive approaches.

Emphasizing the necessity of UOS for mental and physical health, community interaction and social and environmental resilience in cities, the methodology involves a comprehensive analysis of a number of theoretical frameworks that have historically influenced urban open space conceptualisation, design and assessment. The approach includes a critical review of traditional UOS assessment methods, contrasting them with the capabilities of the proposed YouWalk-UOS application. Building on the reviewed theoretical frameworks, the methodology articulates the application’s design, which encompasses 36 factors across three assessment domains: functional, social and perceptual and provides insights into how technology can be leveraged to offer a more holistic and participatory approach to urban space assessment.

YouWalk-UOS application represents an important advancement in urban space assessment, moving beyond the constraints of traditional methods. The application facilitates a co-assessment approach, enabling community members to actively participate in the evaluation and development of their urban environments. Findings highlight the essential role of technology in making urban space assessment more user-centred, aligning more closely with community needs and aspirations.

The originality lies in the focus on the co-assessment approach and integration of mobile technology into urban open space assessment, a relatively unexplored area in urban design literature. The application stands out as an innovative solution, offering a new perspective on engaging communities in co-assessing their environments. This research contributes to the discourse on urban design and planning by providing a fresh look at the intersection of technology, user engagement and urban space assessment.