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The paper shows how small firms perceive the pathways through which access to and adoption of superfast broadband-enabled resources strengthen business performance. Improvements to broadband infrastructure do not automatically lead to adoption of opportunities made available through the broadband resource. Then, interventions can be used to alert small firms to new opportunities. However, the quality of interventions in terms of education and digital audits can be better targeted with information available on how small firms perceive the benefits from broadband access and whether these perceptions are reflected in business performance outcomes.

Data are used from the Digital Maturity Survey from Wales. The study uses principal component analysis and a dual stage cluster approach to show how SMEs believe they are benefitting from broadband access. These belief-based perceptions of broadband inferred business benefits are tested against business performance variables.

The analysis shows variation in SME perceptions of the benefits of broadband-enabled services. This study reveals a cluster of firms which perceived routes to business value in terms of variables linked to security and risk management, and then more commonly held notions linked to communication, competition enhancement and productivity.

While the research literature points to Information and Communication Technology (ICT) resources (ICT investment and skills) and use (digital applications), leading to new to business value improvements, this study suggests less work has sought to identify the critical themes identified by business owners in explaining how ICT resources and use tie to observed business performance. The study identifies these critical themes. The analysis suggests that these critical themes in terms of business value benefits as perceived by business owners can be summarised in terms of communication and competition benefits, and security and risk related benefits. The findings have a series of implications for interventions in the space.

The theory of double jeopardy (DJ) is shown to hold across broad ranging geographies and physical product categories. However, there is very little research appertaining to the subject within an online environment. In particular, studies that investigate the presence of DJ and the contrasting view point to DJ, namely, that of negative double jeopardy (NDJ), are lacking. This study aims to contribute to this identified research gap and examines the presence of DJ and NDJ within a product category, utilising data from Twitter.

A total of 354,676 tweets are scraped from Twitter and their sentiment analysed and allocated into positive, negative and no-opinion clusters using fuzzy c-means clustering. The sentiment is then compared to the market share of brands within the beer product category to establish whether a DJ or NDJ effect is present.

Data reveal an NDJ effect with regards to original tweets (i.e. tweets which have not been retweeted). That is, when analysing tweets relating to brands within a defined beer category, the authors find that larger brands suffer by having an increased negativity amongst the larger proportion of tweets associated with them.

The clustering approach to analyse sentiment in Twitter data brings a new direction to analysis of such sentiment. Future consideration of different numbers of clusters may further the insights this form of analysis can bring to the DJ/NDJ phenomenon. Managerial implications discuss the uncovered practitioner’s paradox of NDJ and strategies for dealing with DJ and NDJ effects.

This study is the first to explore the presence of DJ and NDJ through the utilisation of sentiment analysis-derived data and fuzzy clustering. DJ and NDJ are under-explored constructs in the online environment. Typically, past research examines DJ and NDJ in separate and detached fashions. Thus, the study is of theoretical value because it outlines boundaries to the DJ and NDJ conditions. Second, this research is the first study to analyse the sentiment of consumer-authored tweets to explore DJ and NDJ effects. Finally, the current study offers valuable insight into the DJ and NDJ effects for practicing marketing managers.

The purpose of this paper is to analyse the determinants of small and medium-sized enterprises’ (SMEs) propensity to export using data from a North African country, namely Algeria. Drawing on the extended resource-based view, the study examines the role of firms’ resources and capabilities in explaining the probability to export.

The study employs the nascent fuzzy c-means clustering technique to analyse a sample of 208 Algerian SMEs. The sample included both established and potential exporters operating across various sectors. A combination of online and face-to-face methods was used to collect the data.

While a preliminary analysis established the existence of five clusters exhibiting different levels of resources and capabilities, further discernment of these clusters has shown significant variances in relation to export propensity. In short, clusters exhibiting combinations that include higher levels of export-oriented managerial resources showed greater export propensity, whereas clusters lacking such assets were less likely to display high export propensity, despite superior capabilities in marketing and innovation.

The findings provide a more comprehensive insight on the critical resources shaping SMEs’ internationalisation in the North African context. The paper holds important implications for export promotion policy in this area.

The study makes a twofold contribution. First, the use of the fuzzy c-means clustering technique to capture the joint influence of discrete resources and capabilities on SMEs’ export propensity constitutes a methodological contribution. Second, being the first study bringing evidence on SMEs’ internationalisation from the largest country in the African continent, in terms of landmass, constitutes an important contextual contribution.

The purpose of this paper is to investigate student motivation for undertaking an entrepreneurship education programme and their ultimate employment aspirations through a novel data mining technique. The study considered what relationship certain motivation characteristics have to students’ aspirations, specifically in terms of their intention to be self-employed or employed.

The study examined enrolment data of 720 students on an entrepreneurial education programme, with work statuses of full-time, part-time or unemployed and have known aspirations to either employment or self-employment. The Classification and Ranking Belief Simplex (CaRBS) technique is employed in the classification analyses undertaken, which offers an uncertain reasoning based visual approach to the exposition of findings.

The classification findings demonstrate the level of contribution of the different motivations to the discernment of students with self-employed and employed aspirations. The most contributing aspirations were Start-Up, Interests and Qualifications. For these aspirations, further understanding is provided with respect to gender and student age (in terms of the association with aspirations towards self-employed or employed). For example, with respect to Start-Up, the older the unemployed student, the increasing association with employment rather than self-employment career aspirations.

The study identifies candidate motivation and the demographic profile for student's undertaking an entrepreneurial education programme. Knowing applicant aspirations should inform course design, pedagogy and its inherent flexibility and recognise the specific needs of certain student groups.

The study contributes to the literature examining motivations for undertaking entrepreneurship education and categorising motivating factors. These findings will be of value to both education providers and researchers.

The UK police service has a major challenge to introduce innovative ways of improving efficiency and productivity, whilst at the same time improving public opinion as to their effectiveness in the “fight against crime”. The purpose of this paper is to outline an exploratory study of the ability to cluster police forces based on their sanction detection levels over a number of different offence groups and whether these clusters have different associated public opinions towards them.

Using secondary data and the fuzzy c‐means clustering technique to exposit clusters of police forces based on sanction detection levels, relating them in a statistical analysis with public opinion on the police.

The clustering analysis shows how police forces can be considered relative to each other, based on their sanction detection levels of certain offence groups, including; burglary, fraud and forgery and criminal damage. Using the established clusters of police forces, in respect of independent variables relating to public opinion, including confidence in police; there does appear to be statistically significant differences amongst the clusters of police force.

The results demonstrate the connection between the police's attempt to fight crime and public opinion. With the public opinion measures considered post the establishing of police forces’ clusters, the results show the public does notice the level of sanction detections achieved. The identified disconnect of the public with the criminal justice system is something that can be improved on in the future.

Demonstrates that there is a significant link in the relationship between the levels of sanction detection levels of police forces and public opinion about their ability to fight crime.

This paper employs fuzzy c‐means, a modern clustering technique nascent in this area of research.

The purpose of this paper is twofold: to outline and analyse the issue of gender differences in supermarket choice; and to demonstrate the nascent CaRBS technique as an appropriate analysis tool on incomplete data.

The paper presents a CaRBS analysis of survey‐based data with emphasis on the visualisation of the evidence on the reasons for supermarket choice in discerning the gender of those making the decision of which supermarket to visit.

Using the original incomplete data, there are certain reasons, such as range of stock, that are viewed differently by male and female consumers when deciding which supermarket to choose.

CaRBS provides the ability to analyse incomplete data without the need to manage the missing values that are present, and the ability to optimise the classification of respondents based on their gender through minimising ambiguity but not the inherent ignorance in the evidence from the questionnaire‐based responses. The relevance of characteristics can be found, even though many of the response‐based data values are missing.

The paper provides a clear demonstration of the ability to analyse original incomplete data, mitigating having to interpret results from managed data. The paper also introduces the CaRBS technique as a practical analysis tool in marketing research.

The paper is set in the context of the impact of new public management (NPM) on the police service in the UK. Specifically, it aims to describe a modelling based approach to targeted police performance improvement within a specific area of measured operational policing namely sanction detection levels. It draws upon nationally available crime statistics, which have been utilised in a novel way in order to provide the police with an additional performance management technique.

The paper uses secondary data and the PROMETHEE ranking technique to exposit performance rank improvement of a police force amongst their most similar forces group.

The modelling approach is a proven tool that could be used in partnership with other police performance management techniques in their attempt to meet the public interest and Home Office demands for improvements in base sanction detection levels.

The paper presents a theoretical approach that seeks to address a complex and multifaceted operational issue affecting all police forces. The theoretical nature in itself presents a potentially idealistic scenario.

The paper demonstrates that innovative modelling has the potential to add value to techniques that are currently used in the area of police performance improvement, in this case sanction detection levels. At the fundamental level this could be viewed in terms of “Where to start first, and from there?” with respect to targeting certain types of crime.

This paper uses a modern ranking technique previously unused in this area.

The maintenance of public order and the control of crime are clearly amongst the primary objectives of global law enforcement agencies. An important antecedent to this is the consideration of public trust in their police force. The purpose of this paper is to utilise data from the fifth round European Social Survey (ESS), to investigate how public social indicators may be highlight the level of trust in a country’s police force.

The results from the ESS are analysed using fuzzy-set Qualitative Comparative Analysis (fsQCA), multiply conjunctional causal configurations of the considered social indicators are then established and analysed.

A consequence of using fsQCA, asymmetric causal configurations are identified for the relative high and low limiting levels of trust towards the police in the considered countries. The results offer novel insights into the relationship between social indicators and police trust, as well as expositing a nascent technique (fsQCA) that may offer future potential in this area.

This paper introduces a nascent technique (fsQCA) to analyse a major European data set relating to citizens perceptions of the police. The findings might prove useful for policing organisations as they develop strategies to maintain/improve the level of trust and confidence of citizens in the policing services they provide.

Rough set theory (RST) involves techniques for knowledge discovery or data mining. RST is typically applied within decision tables and offers an alternative to more conventional techniques for classification and rule induction. It is based on describing decisions or categories by means of certain approximations. Offers an overview of the basic principle through the use of a small example. Concludes with a marketing case study, dealing with the characteristics of different brands of cereal.