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This paper aims to present a highly accessible and affordable tracking model for earthmoving operations in an attempt to overcome some of the limitations of current tracking models.

The proposed methodology involves four main processes: acquiring onsite terrestrial images, processing the images into 3D scaled cloud data, extracting volumetric measurements and crew productivity estimations from multiple point clouds using Delaunay triangulation and conducting earned value/schedule analysis and forecasting the remaining scope of work based on the estimated performance. For validation, the tracking model was compared with an observation-based tracking approach for a backfilling site. It was also used for tracking a coarse base aggregate inventory for a road construction project.

The presented model has proved to be a practical and accurate tracking approach that algorithmically estimates and forecasts all performance parameters from the captured data.

The proposed model is unique in extracting accurate volumetric measurements directly from multiple point clouds in a developed code using Delaunay triangulation instead of extracting them from textured models in modelling software which is neither automated nor time-effective. Furthermore, the presented model uses a self-calibration approach aiming to eliminate the pre-calibration procedure required before image capturing for each camera intended to be used. Thus, any worker onsite can directly capture the required images with an easily accessible camera (e.g. handheld camera or a smartphone) and can be sent to any processing device via e-mail, cloud-based storage or any communication application (e.g. WhatsApp).

This study explores the causes of delayed mandatory annual report filings of private companies in Estonia.

The authors use an online survey targeting companies that had submitted annual reports for 2017 late (late-filers) or failed to submit these by July 2020 (non-filers). The responses of 492 late-filers and 122 non-filers are analysed with exploratory factor analysis, Mann–Whitney U-Test and logistic regression.

Annual report filing decisions of both, late-filers and non-filers, are strongly driven by administrative costs attached to the preparation and submission of reports with non-filers perceiving these to be significantly greater. The relevance of other disclosure-related costs and benefits remains similar for both late-filers and non-filers. While proprietary and privacy concerns remain rather unimportant, benefits of timely disclosure, in the form of access to financing and possibilities to continue ordinary business activities, remain important disclosure timing drivers.

Policy interventions should focus on preventive measures that hinder companies' ordinary business activities in case of non-compliance to reporting deadlines. Monetary sanctions can be used to strengthen the desired behaviour alongside broader clarification of the purpose of mandatory reporting and available exemptions.

The authors propose an empirically testable comprehensive one-period model of disclosure timing decisions of private companies differentiating late-filers and non-filers. The authors address the limitations of previous studies through a survey that allows the authors to draw direct inferences about the trade-offs between different decision drivers and the motivations behind managers' disclosure timing decisions.

Upon the premises of social exchange theory (SET), this study aimed at hypothesizing and examining a serial mediation model that investigated the underlying mechanism through which a high-performance work system (HPWS) affects individuals’ future time perspective (FTP).

The hypothesized relationships were examined using responses collected from 275 employees from 15 local private banks and 40 established branches through a proportionate stratified sampling technique. The statistical package for social sciences (SPSS) PROCESS macro 3.0 and analysis of moment’s structure (AMOS) 24.0 were employed for data analysis purposes.

The study revealed that HPWS is indirectly related to the individuals’ FTP through workplace social courage (WSC) and employee well-being (EWB) sequentially. Prescriptions for theoretical and managerial implications were discussed, and future research viewpoints with limitations were acknowledged.

This study illuminated the underlying mechanism and theoretical logic linking HPWS and individuals’ FTP by proposing the serial mediating effect of WSC and EWB.

Preventative public health policies often rely on objective criteria to identify people in vulnerable situations. Age is one of the criteria when investigating the risk of malnutrition associated with ageing. By looking at changes in the food practices of individuals as they age, this study aims to seek to contribute to more precise targeting of older adults in view of the dynamic nature of ageing.

From a theoretical perspective, this research is based on the life course paradigm. Data were collected from 42 semi-structured interviews with retirees aged 60 and over.

The results show that some ageing events lead to immense reconfigurations of individuals’ lives, implying changes as prior food practices are extensively replaced by new ones (“rebuilding after a tsunami”). Other more diffuse and gradual effects of ageing lead to adaptations of previous food practices and, in a more localised way, areas that can be targeted by healthy eating campaigns (“plugging the gaps”). Lastly, this study shows that a normative perspective can help endorse a new social role (“getting into character”), and that relying on their human capital (“it’s a matter of perspective”) can help people cope with new age-related mindsets.

The sample did not include individuals with serious physical or mental health problems at the time of the interviews.

The study identifies social, biological and psychological events related to ageing that have an impact on food practices, as well as moments and milestones for developing public policy campaigns to promote healthy eating.

The study gives insights into the place of food in the process of coping with ageing, showing that food can be either a problem to solve or a resource to help cope with social or psychological imbalances. The holistic view adopted contributes to identifying other events that impact food practices (and consequently health) during the ageing process.

The topic of artificial intelligence (AI) has been expanding rapidly in recent years, gaining the attention of academics and practitioners. This study provides a structured literature review (SLR) on AI and management decisions (MDs) by analysing the scientific output and defining new research topics.

The study uses a rigorous methodological approach to summarise the state of the art of the past literature. The authors used Scopus as the database for data collection and utilised the Bibliometrix R package. In total, 204 peer-reviewed English articles were collected and analysed.

The results showed that literature in this field is emerging. Studies are focused on using AI as forecasting and classification for management decision-making, AI as a tool to improve knowledge management in organisations and extract information. The cluster analysis revealed the presence of five thematic clusters of studies on the topic.

The study’s originality lies in providing a new perspective on AI for MDs. In particular, the analysis reveals a new classification of research streams and provides fruitful research questions to continue research on the topic.

Given the fierce competition in financial services, service failure management and trust restoration tactics are becoming strategic priorities. Studies investigating trust restoration have increased over the years due to the significance of trust in services and the frequency of trust violations. Drawing on the sense-making and defensive approaches of attribution theory, this study aims to explore the effectiveness of various trust recovery tactics (e.g. apology, explanation, and investigation) in financial services considering the prevalence of service failure severity.

Based on a scenario-based survey, this study gathered data from 402 consumers of different banks in Pakistan. The study analyzed the data using ordinary least square regressions and structural equation modeling.

The study indicated that explanation is more effective in repairing character-competence and commitment-based trust, while investigation remained highly effective for inducing congruence-based trust. Interestingly, an apology was more effective for communication-based trust repairing, while context-based trust recovery was unaffected against all recovery tactics. Despite the prevalence of severe service failure, recovery actions proved fully effective for character-competence and commitment-based trust while partially effective for congruence-based trust recovery. This study also found that severe service failure undermines the effectiveness of recovery actions in repairing communication and context-based trust.

The study extends the literature on trust recovery by integrating sense-making and defensive attribution theory. The sense-making approach contributes to the existing knowledge on trust recovery by elucidating how consumers and service providers develop a shared understanding to facilitate the recovery mechanism of multidimensional trust in financial services.

This paper aims to explore the impact of brand authenticity dimensions (i.e. aesthetic, symbolism, heritage, originality, quality commitment and virtue) on consumer engagement in the context of social media. This study answers to the need of scholars to understand consumer behaviour towards family and non-family firms’ brand authenticity constructs and for practitioners to find the correct levers to increase consumer engagement.

Top 10 European family firms with a retrievable Facebook (FB) page from the Global Family Business Index have been selected. Then, the study analysed family firms’ social media consumer engagement versus their non-family business direct competitors on a sample of 21.664 FB posts over a four-year period, leveraging multi-group analysis.

The results outline that three out of six brand authenticity dimensions posted on FB are statistically arousing more interactions respect to non-authenticity-related contents when posted by family firms. However, there are no statistically significant findings when brand authenticity content is posted by the non-family competitors.

This research is helpful for practitioners and entrepreneurs who might want to strengthen their social media brand strategies. With this regard, the study provides insights on which elements of brand authenticity are perceived by consumers as more engaging and which levers to use when communicating the familiness of the company.

To the best of authors’ knowledge, this is one of the earliest studies crosscutting the family business and brand authenticity literature streams to conduct an empirical analysis based on official FB data with a data set of over 20,000 observations. Moreover, this study assesses that not every dimension of the brand authenticity construct is relevant in the context of social media and that its effectiveness depends on the firms’ familiness.

Social interaction in comment sections has become a key factor for backers' decision making in crowdfunding platforms. However, current research on the two-way social interaction in crowdfunding is insufficient, and there exist inconsistent conclusions. This study focuses on the social interaction between creators and backers and explores its influence on the successful exit of crowdfunding projects.

The extended Cox model is used for the empirical analysis of 1,988 crowdfunding projects on the Modian (www.modian.com) platform, a crowdfunding platform for cultural and creative projects in China. The two-way social interaction is reflected in comment quantity and sentiment, as well as reply rate.

Results reveal an inverted U-shaped relationship between comment quantity/sentiment and the successful exit of crowdfunding projects. This relationship is strengthened by high reply rate.

This study focuses on comment quantity and sentiment. The inverted U-shaped results reconcile previous conclusions. Replies from creators are regarded as a separate factor, and their moderating role is explained. The study research proves the importance of social interaction in crowdfunding platforms and provides suggestions for backers, creators and platform managers.

Rolling element bearings (REBs) are commonly used in rotating machinery such as pumps, motors, fans and other machineries. The REBs deteriorate over life cycle time. To know the amount of deteriorate at any time, this paper aims to present a prognostics approach based on integrating optimize health indicator (OHI) and machine learning algorithm.

Proposed optimum prediction model would be used to evaluate the remaining useful life (RUL) of REBs. Initially, signal raw data are preprocessing through mother wavelet transform; after that, the primary fault features are extracted. Further, these features process to elevate the clarity of features using the random forest algorithm. Based on variable importance of features, the best representation of fault features is selected. Optimize the selected feature by adjusting weight vector using optimization techniques such as genetic algorithm (GA), sequential quadratic optimization (SQO) and multiobjective optimization (MOO). New OHIs are determined and apply to train the network. Finally, optimum predictive models are developed by integrating OHI and artificial neural network (ANN), K-mean clustering (KMC) (i.e. OHI–GA–ANN, OHI–SQO–ANN, OHI–MOO–ANN, OHI–GA–KMC, OHI–SQO–KMC and OHI–MOO–KMC).

Optimum prediction models performance are recorded and compared with the actual value. Finally, based on error term values best optimum prediction model is proposed for evaluation of RUL of REBs.

Proposed OHI–GA–KMC model is compared in terms of error values with previously published work. RUL predicted by OHI–GA–KMC model is smaller, giving the advantage of this method.