Search results

The research study intends to gain a better understanding of members' behaviors in the context of crowdsourcing contests. The authors examined the key factors that can motivate or discourage contributing to a team and within the community.

The authors conducted 21 semi-structured interviews with Kaggle.com members and analyzed the data to capture individual members' contributions and emerging determinants that play a role during this process. The authors adopted a qualitative approach and used standard thematic coding techniques to analyze the data.

The analysis revealed two processes underlying contribution to the team and community and the decision-making involved in each. Accordingly, a set of key factors affecting each process were identified. Using Holbrook's (2006) typology of value creation, these factors were classified into four types, namely extrinsic and self-oriented (economic value), extrinsic and other-oriented (social value), intrinsic and self-oriented (hedonic value), and intrinsic and other-oriented (altruistic value). Three propositions were developed, which can be tested in future research.

The study has a few limitations, which point to areas for future research on this topic. First, the authors only assessed the behaviors of individuals who use the Kaggle platform. Second, the findings of this study may not be generalizable to other crowdsourcing platforms such as Amazon Mechanical Turk, where there is no competition, and participants cannot meaningfully contribute to the community. Third, the authors collected data from a limited (yet knowledgeable) number of interviewees. It would be useful to use bigger sample sizes to assess other possible factors that did not emerge from our analysis. Finally, the authors presented a set of propositions for individuals' contributory behavior in crowdsourcing contest platforms but did not empirically test them. Future research is necessary to validate these hypotheses, for instance, by using quantitative methods (e.g. surveys or experiments).

The authors offer recommendations for implementing appropriate mechanisms for contribution to crowdsourcing contests and platforms. Practitioners should design architectures to minimize the effect of factors that reduce the likelihood of contributions and maximize the factors that increase contribution in order to manage the tension of simultaneously encouraging contribution and competition.

The research study makes key theoretical contributions to research. First, the results of this study help explain the individuals' contributory behavior in crowdsourcing contests from two aspects: joining and selecting a team and content contribution to the community. Second, the findings of this study suggest a revised and extended model of value co-creation, one that integrates this study’s findings with those of Nov et al. (2009), Lakhani and Wolf (2005), Wasko and Faraj (2000), Chen et al. (2018), Hahn et al. (2008), Dholakia et al. (2004) and Teichmann et al. (2015). Third, using direct accounts collected through first-hand interviews with crowdsourcing contest members, this study provides an in-depth understanding of individuals' contributory behavior. Methodologically, this authors’ approach was distinct from common approaches used in this research domain that used secondary datasets (e.g. the content of forum discussions, survey data) (e.g. see Lakhani and Wolf, 2005; Nov et al., 2009) and quantitative techniques for analyzing collaboration and contribution behavior.

The authors advance the broad field of crowdsourcing by extending the literature on value creation in the online community, particularly as it relates to the individual participants. The study advances the theoretical understanding of contribution in crowdsourcing contests by focusing on the members' point of view, which reveals both the determinants and the process for joining teams during crowdsourcing contests as well as the determinants of contribution to the content distributed in the community.

Internationally, rates of suicide and lifetime self-harm are higher in prisoners compared to the general population. This study aims to identify specific characteristics of self-harming behaviour and to establish a profile of prisoners who engage in self-harm.

Data from the Self-Harm Assessment and Data Analysis Project (SADA) on self-harm episodes in prisons in the Republic of Ireland during 2017–2019 was used. Annual rates per 1,000 were calculated by age and gender.

The rate of self-harm between 2017 and 2019 was 31 per 1,000 prisoners for men and six times higher at 184 per 1,000 prisoners for women. The rate of self-harm was twice as high among prisoners on remand than sentenced prisoners (60.5 versus 31.3 per 1,000). The highest rates of self-harm among sentenced prisoners were observed among 18–29-year-old men (45 per 1,000) and women (125 per 1,000). The rate of self-harm was higher among women prisoners in all age groups. Contributory factors associated with self-harm were mainly related to mental health but also linked to a prisoner’s environment and relationships.

There is a need to ensure access to timely and suitable mental health services, including both appropriate referral and provision of evidence-based mental health interventions to address the needs of these cohorts.

To the best of the authors’ knowledge, this is the first national study to systematically examine incidence and patterns of self-harm among the prison population in Ireland. The recording of severity/intent of each episode is novel when assessing self-harm among the prison population.

Task complexity is one of the significant factors that influences and is used for forecasting employee performance and determining labor cost. However, the complexity level of tasks is unstructured, dynamic and complicated to perform. This paper develops a new method for evaluating the complexity level of tasks in the production process to support production managers to control their manufacturing systems in terms of flexibility, reliability to production planning and labor cost.

The complexity level of tasks will be analyzed based on the structuralist concept. Using the structure of task, the factors that significantly affect the task complexity in an assembly line will be defined, and the complexity level of the task will be evaluated by measuring the number of task components. Using the proportional 2-tuples linguistic values, the difference between the complexity levels of tasks can be compared and described clearly.

Based on the structure of the task, three contributory factors including input factors, process-operation factors and output factors that significantly affect the task complexity in an assembly line are identified in the present study. The complexity level of the task is quantified through analyzing the details of the three factors according to two criteria and six sub-criteria within the textile case study.

The proposed approach provides a new insight about the factors that have an effect on the complexity of tasks in production and remedies some of limitations of previous methods. The combination of experts' experience and scientific knowledge will improve the accuracy in determining the complexity level of tasks.

The aim of this study is to increase the understanding of collaborative relationships and assess according to the project size, the influence of the contributory factors in shaping collaboration network structure in projects developed in global supply chains (GSC).

The paper used a case study methodology applied to eight global projects developed by an Austrian company leader in global market intra-logistics solutions and warehouse automation. The cases were studied by two approaches in network analysis. First, visual and descriptive analysis to describe structural aspects of the network. Second, stochastic network analysis to evaluate the influence of contributory factors in the structure of the collaboration network.

The results evidence that independently of the project size and project manager influence, project team roles (PTR) who have a reciprocal communication among other PTR tend to have a higher collaboration intensity (CI). Additionally, the results highlight the influence of the project manager in shaping the collaboration network in standard projects (STP) and small projects (SMP). According to the project size, the results show that the PTR that form complete triangles or cluster or who communicate frequently among each other tend to have a high CI, being more evident these tendencies in large-scale projects than STP and SMP.

This research provides a framework to identify the key actors and contributory factors in shaping collaborative relationships in GSC. The findings could be used to support the decision-making process and formulation strategies for effective collaborative relationship management in GSC.

This article intends to explore critical factors that affect the adoption of organic farming in emerging economies. The authors respond to the calls from policymakers, non-government organizations, business firms and scholars to improve the farmers' awareness of the negative impact of synthetic chemical pesticides, phosphorus, potassium fertilizers and mineral nitrogen used in traditional farming.

Through self-administered survey questionnaires, responses were obtained from 397 farmers (conventional) regarding organic farming adoption in Haryana (India). The survey responses were collected between October 2022 and December 2022. The authors apply the “partial least squares structural equation modeling” (PLS-SEM) to test the framed hypotheses.

The present article demonstrates six critical determinants of organic farming adoption, i.e. behavioral, cultivation, economic, governmental, marketing, and social factors. These six factors drive 71.0% (R²) variation in organic farming adoption. Governmental factors have a positive but insignificant influence on organic farming adoption. Interestingly, the impact of behavioral and cultivation factors is crucial per path coefficient values.

For the first time, the authors conducted a study on organic farming adoption in Haryana that lies in its context-specific implementation, utilization of localized knowledge and expertise, regional policy support, agricultural diversification and community participation. Future research can build upon by adding agriculture scientists to the study to respond to the cost, quality of the crop and impact of socio-economic policies as moderators/mediators on adoption decisions.

Quality failures in grid infrastructure construction would cause large-scale collapses in power supply and additional expenditures by reworks and repairs. Learning from quality failures (LFQF) extracts experience from previous quality events and converts them into preventive measures to reduce or eliminate future construction quality issues. This study aims to investigate the influence factors of LFQF in the construction of grid infrastructure.

The related factors of LFQF, including quality management (QM) practices, quality rectification, and individual learning, were identified by reviewing literature about organizational learning and extracting experience from previous failures. A questionnaire survey was distributed to the grid companies in North, Northeast, Northwest, East, Central, and Southwest China. 381 valid responses collected and analyzed using structural equation modeling (SEM) to test the influence of these factors on LFQF.

The SEM results support that QM practices positively affect individual learning and LFQF. Quality rectification indirectly impacts LFQF via individual learning, while the results did not support the direct link between quality rectification and LFQF.

The findings strengthen practical insights into extracting experience from poor-quality issues and continuous improvement. The contributory factors of LFQF found in this study benefit the practitioners by taking effective measures to enhance organizational learning capability and improve the long-term construction quality performance in the grid infrastructure industry.

Existing research about the application of LFQF still stays at the explorative and conceptual stage. This study investigates the related factors of LFQF, including QM practices, quality rectification, and individual learning, extending the model development of learning from failures (LFF) in construction QM.

The steady dropout of senior women from the corporate ladder motivated us to gain insights into their opting out decisions. This exploratory study revealed internal aspiration as a contributory factor of opting out beyond the well-established push and pull factors.

Nineteen senior women executives who had opted out of successful careers due to their internal aspirations were interviewed and grounded theory was leveraged to derive the emergent themes.

The spirit of autonomy and strong personal value emerged as a common thread amongst the women and the basis for their opting out decision. These factors led these senior women to embark upon newer pastures, which included entrepreneurship, dedication to a cause, a passion or academics. The findings were mapped with the protean career concept.

Recommendations would help organizations reimagine and strengthen the existing interventions for the retention of women at the senior levels while simultaneously empowering the women to align their career with their aspirations.

The study uniquely identifies the protean career concept as the force behind the journey undertaken by these women executives. This is in contrast with the push and pull factors that have been vastly studied as reasons for the opting out decisions.

Hyperfocus (HF) is characterised by an intense state of concentration/focus. The purpose of this study is to explore in what ways HF has been found to contribute towards offending behaviour and what treatments have been found to be efficacious to reduce offending behaviour where HF was a contributing factor.

The systematic review was performed according to the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. Papers were screened for quality appraisal and risk of bias. The initial search yielded 9,446 articles. Fifty-seven papers were deemed as meeting the inclusion criteria.

HF was noted to act as a vulnerability factor for aggression, sexual offending, radicalisation and stalking. HF was also observed in clients with psychopathy, autism, mental illness and those without a diagnosis. Thus, HF was not found to be offence or diagnosis specific. Furthermore, HF was found across males and females and adults and adolescents. The results identified themes in relation to the mechanisms by which HF may contribute to offending and suggested interventions for HF and offending.

Practitioners working with clients with HF currently lack evidence on the extent to which this contributes towards offending and recidivism and how HF could be addressed in interventions. Future research is needed to establish the role of HF in offending behaviour. Ideally, this should involve longitudinal data collection, retrospective analysis of data and sophisticated statistical analysis. This should also include exploration of the ways in which HF may be interconnected with offence risks/need factors which contribute towards offending. Research could inform the development of formal measurement tools for HF which are validated with norms for adult and adolescent offender samples. Practitioners should use case formulation to explore if HF represents a treatment need for clients they are working with.

This review noted that although HF has been postulated as being an important contributing factor to offending behaviour, few studies have tested this directly. From the limited studies available, HF was found to be relevant across different types of offences. HF was noted to contribute to offending due to neuropsychological mechanisms linked to executive functioning deficits and positive rewards associated with offending behaviour. Reasons for the lack of HF research are noted and recommendations for future research are discussed.

Outstanding unexercised stock options can motivate managers to engage in actions that increase the value of their company’s stock, including buying back their firm’s stock. The objective of granting stock options to managers is to align their interests with stockholders by tying a portion of their compensation to the company’s stock performance. However, unexercised stock options may have unintended consequences by providing managers with a vested interest in artificially boosting stock prices via stock buybacks. The primary objective of this research is to study the main factors that influence firms' buyback decisions amongst hospitality firms at a time when these firms were clamoring for taxpayer bailouts. Results from logistic regression seem to suggest that outstanding executive stock options are a major contributory factor in a firm’s buyback decision. Estimates also indicate that larger, more profitable firms will likely engage in stock buybacks. These findings survive a battery of tests.

The authors use logistic regression to predict the probability of a firm’s buyback decision based on a given set of exogenous explanatory variables.

The paper supports the hypothesis that buyback decisions are guided by the motive to prop support stock prices in the presence of outstanding restricted stock options/warrants granted to firms' executives.

The paper focuses on the buyback decision of U.S. hospitality firms. The results, therefore, might not be generalizable to firms in other industries or countries.

U.S. share repurchase corporate policy and government regulation needs to be revisited given the economic imperative for firms to invest in activities to restore employment and put them in a position for economic recovery.

Public criticism of the size, structure and form (i.e. loan vs grant) of COVID-19 bailouts warrants an examination of whether the factors that drive hospitality and tourism firms to repurchase shares support economic recovery.

Consistent with agency theory, the authors find a significant positive association between outstanding restricted stocks and a firm’s decision to support the stock prices by buying back shares.

This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life of SAC305 solder across structural parts. Using the finite element simulation (FEM) and continuous damage mechanics (CDM) model, a fatigue life database is built. The stacked machine learning (ML) model's iterative optimization during training enables precise fatigue predictions (2.41% root mean square error [RMSE], R² = 0.975) for diverse structural components. Outliers are found in regression analysis, indicating potential overestimation for thickness transition specimens with extended lifetimes and underestimation for open-hole specimens. Correlations between fatigue life, stress factors, nominal stress and temperature are unveiled, enriching comprehension of LCF, thus enhancing solder behavior predictions.

This paper introduces stacked ML as a novel approach for estimating LCF life of SAC305 solder in various structural parts. It builds a fatigue life database using FEM and CDM model. The stacked ML model iteratively optimizes its structure, yielding accurate fatigue predictions (2.41% RMSE, R² = 0.975). Outliers are observed: overestimation for thickness transition specimens and underestimation for open-hole ones. Correlations between fatigue life, stress factors, nominal stress and temperature enhance predictions, deepening understanding of solder behavior.

The findings of this paper highlight the successful application of the SMLA in accurately estimating the LCF life of SAC305 solder across diverse structural components. The stacked ML model, trained iteratively, demonstrates its effectiveness by producing precise fatigue lifetime predictions with a RMSE of 2.41% and an “R²” value of 0.975. The study also identifies distinct outlier behaviors associated with different structural parts: overestimations for thickness transition specimens with extended fatigue lifetimes and underestimations for open-hole specimens. The research further establishes correlations between fatigue life, stress concentration factors, nominal stress and temperature, enriching the understanding of solder behavior prediction.

The authors confirm the originality of this paper.