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This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This study aims to explore the intelligibility of communication with stakeholders as a result of accrual accounting adoption. It focuses on changes in the use of visual forms and the readability of text that occurred immediately after the adoption of accrual accounting in performance reports of Italian public universities.

The authors collect the stakeholder section of performance reports published before and after accrual accounting adoption. Then, the authors use manual and computer-assisted textual analysis. Finally, the authors explore the data using principal component analysis and qualitative comparative analysis.

This study demonstrates that switching from cash to accrual accounting provokes immediate changes in communication patterns. It confirms the significant reduction of readability and increase in visual forms after accruals accounting adoption. The results indicate that smaller universities especially put effort into increasing intelligibility while implementing a more complex accounting system. This study also finds a relation between the change in readability and the change in visual forms that are complementary, with the exception of several very large universities.

The findings underline the possibility of neutralising the adverse effects of accounting reform associated with its complexity and difficulties in understanding by the use of visual forms and attention to the document’s readability.

This paper adds a new dimension to the study of public sector accounting from the external stakeholder perspective. It provides further insight into the link between accrual accounting adoption and readability, together with the use of visual forms by universities.

The purpose of variable loading conditions (392 N-785N-392N-785N) with break-in period were used to study interactions between zinc dialkyl dithiophosphate (ZDDP) 0.1 P% (phosphorus) and fine-grade molybdenum disulfide (MoS₂) 3%, in different mixtures of NLGI 2 lithium stearate grease. Four-ball wear tests were used to evaluate the tribological properties of different grease mixtures such as coefficient of friction and wear. ASTM 2266 as reported by earlier studies is useful, but it is not representative of real-life applications where variable loads and speeds and different break-in periods play a role and could change the results and the nature of tribofilms.

In this study, chemical and mechanical properties of tribofilms were examined. Moreover, design of experiment was used to examine the data and shorten experimentation time. Research described here is investigating variable loading conditions for real-life applications by using a break-in period of 2 min at the start to minimize asperities and establish a clean surface. Design expert (DOE) analyzes responses to reveal those variables that are single factor and those that are multifactor whether synergistically or antagonistically.

The results indicated that spectrum loading with break-in period showed reduction in wear when tested in greases with ZDDP/MoS₂ combinations. Ramping up or down the load every 7.5 min for a rotational speed of 1,200 rpm and a total of 36,000 revolutions or 30-min time slowed the wear properties of lithium-based grease under different MoS₂ and ZDDP concentrations. Experiments indicated that wear was largely dependent on the loading condition and ZDDP additives during specific break-in period at 1,200 rotational speed. It is believed that MoS₂ greases perform better under spectrum loading and under constant loading when mixed with ZDDP phosphorus.

This research indicates that there is a synergistic interaction between ZDDP, MoS₂ and variable loading especially when a break-in period is applied. The results indicated that wear was largely dependent on the specific speed used with spectrum loading as presented in the energy dispersive spectroscopy and the Auger electron spectroscopy analysis, and thus a 3% MoS₂ grease with ZDDP (phosphorus: 0.1 Wt.%) are needed to improve the wear resistance and improve the friction characteristics.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0016/

This paper aims to investigate the difference between the impacts of indicators of trade barriers (TBs) on bankrupt enterprises (BEs), new enterprises (NEs) and other enterprises (OEs).

The paper has used a multiple-step approach. At the first stage, the initial data has been collected from interviews with 164 top managers of SMEs in West Azerbaijan in Iran during two periods of 2013–2015 and 2017–2019. At the second step, multiple correspondence analysis has been used to summarize the relationships between variables and construct indices for different groups of TBs. Finally, the generalized structural equation model method was used to examine the impact of export barriers.

The results showed that the political legal index is the main TBs for BEs and NEs, but it had a more significant impact on BEs; the financial index was the second major TBs factor for BEs, while OEs did not have a problem in performance index, and the financial index was classified as a minor obstacle for them. All indicators of marketing barriers (except production index) had a negative and significant effect on all enterprises; the most important TBs for NEs was the information index.

The results indicated that if enterprises have a strong financial system and function, they can lessen the impact of sanctions and keep themselves in the market.

To remain sustainable, open source software (OSS) projects must attract new members—or newcomers—who make contributions. In this paper, the authors develop a set of hypotheses based on the knowledge barriers framework that examines how OSS communities can encourage contributions from newcomers.

Employing longitudinal data from the source code repositories of 232 OSS projects over a two-year period, the authors employ a Poisson-based mixed model to test how community characteristics, such as the main drivers of knowledge-based costs, relate to newcomers' contributions.

The results indicate that community characteristics, such as programming language choice, documentation effort and code structure instability, are the main drivers of knowledge-based contribution costs. The findings also suggest that managing these costs can result in more inclusive OSS communities, as evidenced by the number of contributing newcomers; the authors highlight the importance of maintaining documentation efforts for OSS communities.

This paper assumes that motivational factors are a necessary but insufficient condition for newcomer participation in OSS projects and that the cost to participation should be considered. Using the knowledge barriers framework, this paper identifies the main knowledge-based costs that hinder newcomer participation. To the best of the authors' knowledge, this is the first empirical study that does not limit data collection to a single hosting platform (e.g., SourceForge), which improves the generalizability of the findings.

The purpose of this study is to examine the impact of a soft facility practice in the organized retail sector. In specifics, it draws context from a security practice and assesses its effect on customers’ satisfaction and patronage in retail properties.

The research method is quantitative. The study used a survey research design with the use of a structured questionnaire for data collection. The data were collected from the head of households who purchase items in the retail shops. It applied a logistic regression model to estimate customers’ satisfaction and the effect of the current security practice on patronage.

Contrary to expectation, descriptive analysis of data shows that respondents were satisfied with the security procedure with weighted means scores (3.62, 3.74, 3.78 and 3.66) above average for bag check at exits, reconciliation of receipts with purchase, the attitude of security personnel during exit checks and time taken during exit checks. With specific reference to bag checks at the exit, logit shows that 32% were neutral while 8% were dissatisfied with the security practice. Among the dissatisfied, logit shows an odds ratio of 0.059, which implies that they are likely to verbally express their dissatisfaction with the shop. On the other hand, the chances that they would not do this are more likely with an odds ratio of 162818201.343. Further, continuous patronage (Loyalty) is strongly less likely with an odds ratio of 1.250E-22. This was corroborated by a similar odd ratio of 4.068E-11 estimated for those that would take the exit option due to the bag’s check.

The limitation of this study is that samples were randomly drawn from an unknown population of customers. However, the study was guided by Cochran (1963) to select a valid representative sample and support the reliability of the research findings.

The findings on satisfaction imply that the convenience and swiftness associated with shopping in a retail shop had been eroded by the current security facility practices which may lead to a reduction in the growth and retail sector turnover. While many dissatisfied customers would have exited if there are alternative shops with more customer-friendly security practices, the limited number of organized retail shops in the study area will prevent this from happening.

Literature on the management of facilities in real estate is quite vast; however, not much attention has been paid to the management of security in the retail sector particularly in Nigeria. This study is, therefore, novel, as it provides seminal evidence on this important topic and will serve as a reference for further research in Nigeria.

This study analyzes the performance implications of adopting blockchain to support supply chain business processes. The technology holds as many promises as implementation challenges, so interest in its impact on operational performance has grown steadily over the last few years.

Drawing on transaction cost economics and the contingency theory, we built a set of hypotheses. These were tested through a long-term event study and an ordinary least squares regression involving 130 adopters listed in North America.

Compared with the control sample, adopters displayed significant abnormal performance in terms of labor productivity, operating cycle and profitability, whereas sales appeared unaffected. Firms in regulated settings and closer to the end customer showed more positive effects. Neither industry-level competition nor the early involvement of a project partner emerged as relevant contextual factors.

This research presents the first extensive analysis of operational performance based on objective measures. In contrast to previous studies and theoretical predictions, the results indicate that blockchain adoption is not associated with sales improvement. This can be explained considering that secure data storage and sharing do not guarantee the factual credibility of recorded data, which needs to be proved to customers in alternative ways. Conversely, improvements in other operational performance dimensions confirm that blockchain can support inter-organizational transactions more efficiently. The results are relevant in times when, following hype, there are signs of disengagement with the technology.

We studied the relationship between job engagement and systematic problem solving (SPS) among shop-floor employees and how lean production (LP) and Internet of Things (IoT) systems moderate this relationship.

We collected data from a sample of 440 shop floor workers in 101 manufacturing work units across 33 plants. Because our data is nested, we employed a series of multilevel regression models to test the hypotheses. The application of IoT systems within work units was evaluated by our research team through direct observations from on-site visits.

Our findings indicate a positive association between job engagement and SPS. Additionally, we found that the adoption of lean bundles positively moderates this relationship, while, surprisingly, the adoption of IoT systems negatively moderates this relationship. Interestingly, we found that, when the adoption of IoT systems is complemented by a lean management system, workers tend to experience a higher effect on the SPS of their engagement.

One limitation of this research is the reliance on the self-reported data collected from both workers (job engagement, SPS and control variables) and supervisors (lean bundles). Furthermore, our study was conducted in a specific country, Italy, which might have limitations on the generalizability of the results since cross-cultural differences in job engagement and SPS have been documented.

Our findings highlight that employees’ strong engagement in SPS behaviors is shaped by the managerial and technological systems implemented on the shop floor. Specifically, we point out that implementing IoT systems without the appropriate managerial practices can pose challenges to fostering employee engagement and SPS.

This paper provides new insights on how lean and new technologies contribute to the development of learning-to-learn capabilities at the individual level by empirically analyzing the moderating effects of IoT systems and LP on the relationship between job engagement and SPS.

This study aims to develop a machine learning model to detect structure fire fatalities using a dataset comprising 11,341 cases from 2011 to 2019.

Exploratory data analysis (EDA) was conducted prior to modelling, in which ten machine learning models were experimented with.

The main fatal structure fire risk factors were fires originating from bedrooms, living areas and the cooking/dining areas. The highest fatality rate (20.69%) was reported for fires ignited due to bedding (23.43%), despite a low fire incident rate (3.50%). Using 21 structure fire features, Random Forest (RF) yielded the best detection performance with 86% accuracy, followed by Decision Tree (DT) with bagging (accuracy = 84.7%).

Limitations of the study are pertaining to data quality and grouping of categories in the data pre-processing stage, which could affect the performance of the models.

The study is the first of its kind to manipulate risk factors to detect fatal structure classification, particularly focussing on structure fire fatalities. Most of the previous studies examined the importance of fire risk factors and their relationship to the fire risk level.