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Effective safety communication facilitates the sharing of relevant knowledge that helps to improve safety behaviours, such as superior hazard identification and compliance. This study aims to explore channels by which construction companies can effectively communicate health and safety (H&S) among communities of their operations.

Based on a quantitative research approach, this study addressed the knowledge gap through a cross-sectional survey of 250 contractors (comprising 155 building and 95 road contractors) involved in various projects in the Ghanaian construction industry. These contractors were selected by using a stratified simple random sampling technique. Data obtained from the survey was analysed through descriptive (i.e. frequencies, mean and standard deviation) and inferential (i.e. exploratory factor analysis) statistical analyses.

The findings from the mean scores revealed that all the 12 communication channels identified in the literature, confirmed through piloting and examined by the respondents, were important channels through which construction companies can effectively communicate H&S amongst communities of their operations. The exploratory factor analysis revealed a clustering of the 12 channels of communication into 5 components: “safety demonstration in the community”; “social media”; “mass media”; “community engagement”; and “opinion leaders”.

This study offers construction project managers the means of managing one of the major stakeholders of a construction project (the community). It provides an actionable opportunity that can be leveraged strategically to integrate community members into projects to promote synergy and local content inclusion while gaining a peaceful atmosphere to achieve their project goals.

Practically, this study provides construction project managers with a means of managing one of the major stakeholders of a construction project (the community) and also demonstrates the integration of community members into projects to promote synergy and local content inclusion. This would give construction organisations a peaceful atmosphere to accomplish their project objectives.

The social implication of this study is that the study offers society a means of creating safer Ghanaian communities by offering them the knowledge of identifying hazards and avoiding risky behaviours, creating a good safety atmosphere in these communities.

This study presents construction organisations with a unique opportunity to transfer and share novel external knowledge within a different social system (the community). It contributes to the state-of-the-art knowledge in H&S communication by providing channels through which H&S can be communicated in a developing country such as Ghana.

Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.

This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.

Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.

The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.

The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.

The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.

The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.