Search results

In this context, the aim of the present research is to examine what factors determine that consumers restrain from shopping used products through the Internet. So, this research aims to analyze what makes consumers prevent from shopping second-hand products online.

For this purpose, the authors propose and empirically test a conceptual model of the barriers towards online second-hand shopping behavior. Drawing on a sample of 405 consumers data were analyzed through structural equation modeling (SEM).

The findings reveal that contamination effects and the lack of trust towards the online store, followed by the low perceived product reliability and the poor product perceived quality prevent consumers from shopping used products online. Conversely, consumer embarrassment for shopping second-hand products and the purchase uncertainty do not influence consumers' second-hand shopping behavior.

This study contributes to the marketing literature on second-hand shopping, being an attempt to explore the factors that prevent consumers from purchasing used products through the Internet.

This study investigates the effect of total quality management (TQM) on customer satisfaction, personnel satisfaction and company reputation.

The study results rely on a structured survey conducted among an extensive sample of Finnish SMEs. In addition to the examination of the relationship between TQM and company performance in terms of customer satisfaction, personnel satisfaction and company reputation, the study takes a view on the possible effects of the industry, the company size and the certified quality system.

The results reveal that two TQM dimensions, namely Customer Focus and Product Management, were related to companies' customer satisfaction, whereas four TQM dimensions, namely Management/leadership, Customer Focus, Personnel Management and Risk Management, were related to personnel satisfaction. None of the TQM dimensions were related to company reputation. The control variables – the industry, the company size and the certified quality system – were not found to affect customer satisfaction, personnel satisfaction or company reputation.

Most previous studies have been based on traditional TQM classification and have not shown the effects of the latest TQM-related dimensions. Compared to previous studies, this work integrates risk management, digitization, system deployment efficiency and stakeholder management into TQM, which has not been implemented in any previous study. The roles of hard and soft TQM factors have been carefully considered in this study; thus, the study does not place too much emphasis on either direction but provides a balanced picture of the performance of the management systems studied. Although there are studies on the effects of TQM on personnel satisfaction, customer satisfaction and reputation, they are based on a much narrower definition of TQM than that in this study. The business environment is constantly changing, but only a few studies have been conducted to extend the TQM approach. This has led to duplication of studies, and the effects of performance-relevant procedures have not been extensively studied in the past as part of TQM. Therefore, the concept of this study brings significant added value to TQM research and returns the TQM concept to the overall level while considering the requirements of the ISO 9001: 2015 and EFQM 2019 quality standards. The study also considers the effects of ISO 9001 certification and EFQM requirements.

The purpose of this paper is to investigate how different manufacturing technologies are bundled together and how these bundles influence operations performance and, indirectly, business performance. With the emergence of Industry 4.0 (I4.0) technologies, manufacturing companies can use a wide variety of advanced manufacturing technologies (AMT) to build an efficient and effective production system. Nevertheless, the literature offers little guidance on how these technologies, including novel I4.0 technologies, should be combined in practice and how these combinations might have a different impact on performance.

Using a survey study of 165 manufacturing plants from 11 different countries, we use factor analysis to empirically derive three distinct manufacturing technology bundles and structural equation modeling to quantify their relationship with operations and business performance.

Our findings support an evolutionary rather than a revolutionary perspective. I4.0 technologies build on traditional manufacturing technologies and do not constitute a separate direction that would point towards a fundamental digital transformation of companies within our sample. Performance effects are rather weak: out of the three technology bundles identified, only “automation and robotization” have a positive influence on cost efficiency, while “base technologies” and “data-enabled technologies” do not offer a competitive advantage, neither in terms of cost nor in terms of differentiation. Furthermore, while the business performance impact is positive, it is quite weak, suggesting that financial returns on technology investments might require longer time periods.

Relying on a complementarity approach, our research offers a novel perspective on technology implementation in the I4.0 era by investigating novel and traditional manufacturing technologies together.

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

This study is intended to motivate and guide future researchers to rethink and update their theories of operational capability development. By examining the extensive body of research on operational capabilities and working closely with an industry partner, the authors are iteratively developing new thinking about why our existing models seem to be failing and what aspects are likely to be useful in updating them.

This pathway paper is based on observations gained through a structured literature review, close collaboration with an industry partner and discussions with other industry partners and executives.

The authors identify ways in which the operations management community could begin to challenge and expand existing models of operational capability development. They provide reflections on the network structure of operational capabilities, i.e. their interconnectedness and interactions, which are likely to evolve dynamically over time and have not yet been part of the authors’ thinking about operational capability development.

The authors hope to stimulate new research through this pathway paper. By synthesizing their existing knowledge of operational capabilities and collaborating with an industry partner, the authors have attempted to highlight their limited knowledge of capability development. In addition, the authors offer several opportunities to rethink their existing models.

The purpose of this study is to propose an extended reliability method for an industrial motor drive by integrating the physics of failure (PoF).

Industrial motor drive systems (IMDS) are currently expected to perform beyond the desired operating conditions to meet the demand. The PoF of the subsystem affects its reliability under such harsh operating circumstances. It is crucial to estimate reliability by integrating PoF, which helps in understanding its impact and to develop a fault-tolerant design, particularly in such an integrated drive system. An integrated PoF extended reliability method for industrial drive system is proposed to address this issue. In research, the numerical failure rate of each component of industrial drive is obtained first with the help of the MIL-HDBK-217 military handbook. Furthermore, the mathematically deduced proposed approach is modeled in the GoldSim Monte Carlo reliability workbench.

From the results, for a 15% rise in integrated PoF, the reliability and availability of the entire IMDS dropped by 23%, resulting in an impact on mean time to failure (MTTF).

The integrated PoF of the motor and motor controller affects industrial drive reliability, which falls to 0.18 with the least MTTF (2.27 years); whose overall reliability of industrial drive drops to 0.06 if it is additionally integrated with communication protocol.

This study addresses the reliability of a composite fiber (carbon fibers/epoxy matrix) at microscopic level, with a specific focus on its behavior under compressive stresses. The primary goal is to investigate the factors that influence the reliability of the composite, specifically considering the effects of initial fiber deformation and fiber volume fraction.

The analysis involves a multi-step approach. Initially, micromechanics theory is employed to derive limit state equations that define the stress levels at which the fiber remains within an acceptable range of deformation. To assess the composite's structural reliability, a dedicated code is developed using the Monte Carlo method, incorporating random variables.

Results highlight the significance of initial fiber deformation and volume fraction on the composite's reliability. They indicate that the level of initial deformation of the fibers plays a crucial role in determining the composite reliability. A fiber with 0.5% initial deformation exhibits the ability to endure up to 28% additional stress compared to a fiber with 1% initial deformation. Conversely, a higher fiber volume fraction contributes positively to the composite's reliability. A composite with 60% fiber content and 0.5% initial deformation can support up to 40% additional stress compared to a composite containing 40% fibers with the same deformation.

The study's originality lies in its comprehensive exploration of the factors affecting the reliability of carbon fiber-epoxy matrix composites under compressive stresses. The integration of micromechanics theory and the Monte Carlo method for structural reliability analysis contributes to a thorough understanding of the composite's behavior. The findings shed light on the critical roles played by initial fiber deformation and fiber volume fraction in determining the overall reliability of the composite. Additionally, the study underscores the importance of careful fiber placement during the manufacturing process and emphasizes the role of volume fraction in ensuring the final product's reliability.

This paper aims to investigate the influential aspects of industrial branding in building customer brand engagement from the buyer’s and the seller’s points of view. Collecting buyer and seller information is essential to understand business-to-business interaction better. Buyer’s and seller’s perspective integration is significant for stakeholders to develop proper strategies to achieve customer brand engagement.

This study uses a structural equation model to examine the antecedents of customer brand engagement from the buyer’s perspective; then, the result is compared with the seller’s view by conducting an analytical hierarchy process. The authors exercise 140 valid data from the buyer’s industry and 9 experts from the seller’s industry.

This study finds that in developing customer brand engagement, rational brand quality is the most influential from the buyer’s view and top priority from the seller’s view. Surprisingly, both parties have different perspectives about the second and third priorities. The buyers put emotional brand associations as a second priority; perceived value is meaningless and insignificant. On the contrary, the sellers set the perceived value as the second priority and emotional brand associations as the last.

The respondents from the buyer industry cover various industries, and the research is limited to the buyer and the seller in the chemical polymer emulsion market, a market where product quality and application quality on the buyers’ side are essential and where the buyer–seller interaction is intense. Replicating the study in other industries and cultural backgrounds is recommended for generalization.

The paper’s novelty is that there are different priorities and perspectives from the buyer’s and the seller’s views. This study contributes to industrial brand engagement research studies. Investigation of the buyer’s and the seller’s perspectives in industrial brand engagement research studies is still limited.

Research on market information use in product innovation suggests that firms utilize two key strategic decision-making processes: incremental and comprehensive. Drawing from organizational information processing theory, literature implies that these processes operate differently. However, this assumption remains untested. Moreover, the degree to which a comprehensive process affects the innovation strategy outcomes depends on market information time sensitivity (MITS) and analyzability. To-date, no study has tested these assertions, either. Finally, it is suggested that meaningful market strategy is a key driver of new product success and it is important to understand how decision-making processes influence it under differing time sensitivity and analyzability.

Based on survey data from 250 Chinese firms, authors use structural equation modeling to test the hypotheses.

The results generally support authors’ contentions. More specifically, marketing strategy outcomes are influenced by marketing strategy incrementality (MSI) and marketing strategy comprehensiveness (MSC) differently. Further, time sensitivity moderates the effect of both MSI and MSC on outcomes, except for the effect of MSI on decision quality. Finally, analyzability moderates the relationships between decision making processes and certain strategy outcomes such as between MSI and meaningfulness.

Drawing from information processing theory, authors argue that incremental and comprehensive marketing strategy decision making for new product operate differentially under the same conditions. Further, the effects of these decision processes on outcomes depend on time sensitivity and analyzability of market information. Finally, auhtors argue that meaningful market strategy is a driver of success. The authors find support for most of our hypotheses and provide directions for future research.