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This paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.

The approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.

Risk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.

To better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.

Present a method to impute missing data from a chaotic time series, in this case lightning prediction data, and then use that completed dataset to create lightning prediction forecasts.

Using the technique of spatiotemporal kriging to estimate data that is autocorrelated but in space and time. Using the estimated data in an imputation methodology completes a dataset used in lightning prediction.

The techniques provided prove robust to the chaotic nature of the data, and the resulting time series displays evidence of smoothing while also preserving the signal of interest for lightning prediction.

The research is limited to the data collected in support of weather prediction work through the 45th Weather Squadron of the United States Air Force.

These methods are important due to the increasing reliance on sensor systems. These systems often provide incomplete and chaotic data, which must be used despite collection limitations. This work establishes a viable data imputation methodology.

Improved lightning prediction, as with any improved prediction methods for natural weather events, can save lives and resources due to timely, cautious behaviors as a result of the predictions.

Based on the authors’ knowledge, this is a novel application of these imputation methods and the forecasting methods.

Recent research has highlighted limited explicit knowledge of a company’s strategy on the part of its employees. The concept of Open Strategy has emerged as a promising approach to address and enhance knowledge and understanding of an organization’s strategy across all hierarchical levels. However, empirical investigations of the relationship between the degree of openness in a strategy process and employees’ strategy knowledge remain scarce. This study aims to fill this gap.

We conducted an online survey of individuals from various organizations, measuring the degree of openness of their strategy process and using regression analysis to determine the impact on strategy knowledge among the respondents. We operationalized strategy knowledge using two concepts. The first concept encompassed general knowledge of the employer’s strategy, mission, and vision. The second concept measured strategy knowledge in a more concrete manner by assessing factors such as the company’s markets, how it positions itself and its economic logic.

Our findings indicate that regardless of the level of hierarchy, the degree of openness has a strong positive effect on strategy knowledge. However, this effect is smaller when specific aspects of strategy knowledge are assessed compared to a general understanding of strategy. Our results highlight the potential of Open Strategy as an effective approach to enhancing employees’ knowledge of the employer’s strategy.

Our research demonstrates that the degree of openness in strategy processes significantly enhances strategy knowledge, thereby laying the groundwork for effective strategy implementation. The results highlight the importance of cultivating a culture of transparency, participation and inclusion of employees as well as ongoing communication to effectively integrate strategy into organizational practices. We offer practical implications for managers striving to optimize their strategic management processes.

The main aim of this study was to examine gender differences in job satisfaction in Europe.

For the empirical analysis, data from the Sixth European Working Conditions Survey were used. Oaxaca–Blinder decomposition with a principal component analysis (PCA) aggregated variable, after unconditional quantile regressions in a multiple imputation background, was implemented.

Women report higher job satisfaction than men do. Women were significantly more satisfied than men for the middle levels of the job satisfaction distribution.

This study expands the evidence on the determinants of job satisfaction in the European labour market by applying a recent form of decomposition that invests in unconditional quantile regression (UQR). To the best of this study knowledge, this is the first time that the Oaxaca–Blinder decomposition with a PCA aggregated variable after unconditional quantile regression has been employed to study gender-based differences in job satisfaction.

To better understand the impact of choosing a carbon data provider for the estimated portfolio emissions across four asset classes. This is important, as prior literature has suggested that Environmental, Social and Governance scores across providers have low correlation.

The authors compare carbon data from four data providers for developed and emerging equity markets and investment grade and high-yield corporate bond markets.

Data on scope 1 and scope 2 is similar across the four data providers, but for scope 3 differences can be substantial. Carbon emissions data has become more consistent across providers over time.

The authors examine the impact of different carbon data providers at the asset class level. Portfolios that invest only in a subset of the asset class may be affected differently. Because “true” carbon emissions are not known, the authors cannot investigate which provider has the most accurate carbon data.

The impact of choosing a carbon data provider is limited for scope 1 and scope 2 data for equity markets. Differences are larger for corporate bonds and scope 3 emissions.

The authors compare carbon accounting metrics on scopes 1, 2 and 3 of corporate greenhouse gas emissions carbon data from multiple providers for developed and emerging equity and investment grade and high yield investment portfolios. Moreover, the authors show the impact of filling missing data points, which is especially relevant for corporate bond markets, where data coverage tends to be lower.

This paper explores the relationships between firm absorptive capacity, novel business model design (NBMD), product differentiation strategy and performance in a transition economy.

The study uses structural equation modeling (SEM) to analyze firm-level data from a unique sample of Albanian manufacturing and service firms.

The study shows that absorptive capacity enables and shapes the NBMD that, in turn, leads to performance gains. The authors also find that the NBMD capacity mediates the impact of realized absorptive capacity on performance, whereas product differentiation strategy moderates the relationship between new business model and performance.

All variables were measured based on a self-assessed scale leading to potential method bias. Also, based on relevant literature, the study focuses on only one type of business model (BM) design.

Since dynamic capabilities are the foundation of NBMD, firms should invest carefully in developing such capabilities. Thus, the study results provide an integrative framework for understanding the role of absorptive capacity in NBMD adoption and for explaining the relationship between NBMD adoption and performance, an aspect that helps organizations in a dynamic environment.

This study strives to investigate the relationships between absorptive capacity, business model design, product strategies and performance by answering the call of Teece (2018) to “flesh out the details” of such relationships.

This study aims to examine whether Chinese firms increase their concerns about analysts’ earnings forecasts following the split-share structure reform (SSR) in 2005, which removed trading restrictions on approximately 70% of the shares of listed firms.

Using data from 2002 to 2019, the authors empirically test the association between meeting or beating analysts’ earnings expectations and the implementation of SSR.

The authors find that firms are more inclined to meet analysts’ earnings expectations after the introduction of SSR. Further analysis shows that firms guide analysts to walk their forecasts down by manipulating third-quarter earnings, suggesting enhanced value relevance between analysts’ forecasts and third-quarter earnings management in the postreform period.

The findings reveal an undesirable side effect of SSR and suggest that policymakers and regulators should consider and carefully manage the complex relationships between firms and analysts.

In contrast to prior studies that predominantly focus on the positive effects of the reform, this study reveals the side effects of SSR and provides new evidence on the mechanisms of meeting or beating analysts’ earnings expectations.

Minimizing the impact on the surrounding environment and maximizing the use of production raw materials while ensuring that the relevant processes and services can be delivered within the specified time are the contents of enterprise supply chain management in the green financial system.

With the continuous development of China's economy and the continuous deepening of the concept of sustainable development, how to further upgrade the enterprise supply chain management is an urgent need to solve. How to maximize the utilization of resources in the supply chain needs to be realized from the whole process of raw material purchase, transportation and processing.

It was proved that digital twin technology had a partial intermediary role in the role of supply chain big data analysis capability on corporate finance, market, operation and other performance.

This paper focused on describing how digital twin technology could be applied to big data analysis of enterprise supply chain under the green financial system and proved its usability through experiments. The experimental results showed that the indirect effect of the path big data analysis capability digital twin technology enterprise financial performance was 0.378. The indirect effect of the path big data analysis capability digital twin technology enterprise market performance was 0.341. The indirect effect of the path big data analysis capability digital twin technology enterprise operational performance was 0.374.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

Discussions of authenticity in touristic experiences indicate that it is a significant area in the search for meaning. This study aims to demonstrate that the quest for authenticity in a tourist experience begins in the pre-travel period by associating it with meaning in life.

Data were collected via survey from 356 people planning to participate in tourism activities. The survey included demographic questions, the meaning in life scale and the quest for authenticity (QfA) scale, designed by previous researchers.

The analysis revealed that meaning in life levels significantly explained the quest for subjective authenticity (R2 = 0.303) and objective authenticity (R2 = 0.131) in tourist experiences. The search for objective authenticity in a tourist experience significantly mediated the relationship between meaning in life and subjective authenticity.

The research has several limitations. Primarily, the sampling group of the research consists of Turkish tourists who are planning to participate in tourism mobilities, and mostly females volunteered to respond in the data collection process. Hence it is necessary to study potential tourists from other countries for a more generalized conclusion. Second, the authors did not specifically ask the sample group which forms of tourism they are planning to participate in, heritage and culture, fair, etc. Subsequent studies may address this distinction and the explanatory power of the independent variable may differ according to plan to participate in different forms of tourism.

Tourism has an important place in individuals' search for meaning in life and authenticity. Because it offers an environment/setting where individuals can find answers to these searches. As a result of modernism, individuals become alienated from themselves, and their environment and the meanings they attribute to life are sometimes blurred. Some individuals experiencing this tend towards tourist mobility. This process individuals go through also includes the need for authenticity. Thus, such tourist behaviors cause the emergence of different authentic products that can meet the expectations and wishes of individuals.

One of the main elements that encourages people to travel is quest for authenticity, which they feel is lacking in modern life. Thus, tourists are more likely to experience meaning in life based on subjective experiences than viewed objects.

The study offers three novel findings: individuals seek authenticity in tourist experiences in order to find meaning in life; quest for authenticity begins in the pre-travel process; and objective authenticity is necessary to seek subjective authenticity through meaning in life. While a few studies have investigated these variables, the authenticity literature has neglected the pre-travel phase. However, this needs attention to better understand authenticity in tourism.