Search results

The purpose of this study is to determine the enablers of the quality of work life (QWL) of employees working in the Garment industries.

The study was carried out in a fivefold step. In the first step, the enablers of QWL were identified through an exhaustive literature survey, in the second step identified vital few components through Pareto analysis. Then the third step was followed by exploratory factor analysis (EFA) to further, to identify the precise components and validate the same using confirmatory factor analysis in fourth step. The final step included interpretive structural modeling and Cross-Impact Matrix Multiplication Applied to Classification analysis to model the validated components and determine the interrelationships and linkages.

Predominant QWL enablers of employees working in the garment industries are training and development, satisfaction in job, compensation and rewards, relation and co-operation, grievance handling, work environment, job nature, job security and facilities.

In this study, the interpretive structural model is designed based on the opinion of the experts who are working in the garment industry considering the responses from employees in garment sectors. The framework can be extended further to the other sectors.

In future, the researchers in QWL may develop a model to quantify the level of employees’ QWL who are working in different sectors. Enablers of QWL are essential, and based on this further statistical analysis can be carried out. This study will provide limelight to the researchers in choosing the valid and reliable set of enablers for the empirical studies. Organizations can get benefit by implementing the outcome of this research for the enhancement of the QWL of employees.

The study was carried out in 133 garment industries where 851 workers constituted the final valid responses that were considered for analysis. The outcomes from the study help administrators, policy and decision-takers in taking decisions to enhance QWL.

This paper explains why some firms manage to capture disproportionate value from their network of relationships, leading to superior performance. The paper examines how a firm's dependencies affect its value appropriation potential (VAP) in economic networks.

The paper follows the axiomatic method and the embeddedness perspective of firms to develop an index called nodal power, which captures the power that accrues to a firm in exchange-based economic networks. Thereafter, using the formal method and simulation, it shows nodal power reflects a firm's VAP in economic networks.

The study analysis and findings prove that a firm's dyadic level exchange relations and the embedded network structure determine its VAP by affecting the nodal power. A firm with lesser nodal power is likely to appropriate less value from its relations even if it equally contributes to the value creation. This finding explains how the structural and relational characteristics of a firm's network enable disproportionate value appropriation.

Nodal power furthers the scope of analyzing firms' economic relationships and changing power equations in dynamic networks. It can help firms build optimal strategic networks and manage the portfolio of relationships by predicting the impact of changing relations on firms' VAP.

The paper's original contribution is to explain, through formal analysis, why and how the structure and nature of relations of firms affect their VAP. The paper also formalizes the power-dependence principle through a dependency-based index called nodal power and uses it to show how interfirm dependencies are key to value appropriation.

This study aims to capture how the association between a multichannel relational communication strategy (MRCS) and customer performance is contingent upon such customer performance (low- versus high-performance customers) and to reconcile past contradictory results in this marketing-related topic. To this end, the authors propose and validate the method of quantile regression as an unconventional, yet effective, means to proceed to that reconciliation.

This study collected data from 4,934 customers of a private pension fund firm and accounted for both firm- and customer-initiated relational communication channels (RCCs) and for customer lifetime value (CLV). This study estimated a generalized linear model and then a quantile regression model was used to account for customer performance heterogeneity.

This study finds that specific RCCs present different levels of association with performance for low- versus high-performance customers, where outcome customer performance is the dependent variable. For example, the relation between firm-initiated communication (FIC) and performance is stronger for low-CLV customers, whereas the relation between customer-initiated communication (CIC) and performance is increasingly stronger for high-CLV customers but not for low-CLV ones. This study also finds that combining different forms of FIC can result in a negative association with customer performance, especially for low-CLV customers.

The authors tested the conceptual model in one single firm in the specific context of financial services and with cross-sectional data, so there should be caution when extrapolating this study’s findings.

This study offers nuanced and precise managerial insights on recommended resource allocation along with relational communication efforts, showing how managers can benefit from adopting a differentiated-customer performance approach when designing their MRCS.

This study provides an overview of the state of the art of MRCS, proposes a contingency analysis of the relationship between MRCS and performance based on customer performance heterogeneity and suggests the quantile method to perform such analysis and help reconcile past contradictory findings. This study shows how the association between RCCs and CLV varies across the conditional quantiles of the distribution of customer performance. This study also addresses a recent call for a more holistic perspective on the relationships between independent and dependent variables.

Software defect prediction (SDP) is a critical aspect of software quality assurance, aiming to identify and manage potential defects in software systems. In this paper, we have proposed a novel hybrid approach that combines Gray Wolf Optimization with Feature Selection (GWOFS) and multilayer perceptron (MLP) for SDP. The GWOFS-MLP hybrid model is designed to optimize feature selection, ultimately enhancing the accuracy and efficiency of SDP. Gray Wolf Optimization, inspired by the social hierarchy and hunting behavior of gray wolves, is employed to select a subset of relevant features from an extensive pool of potential predictors. This study investigates the key challenges that traditional SDP approaches encounter and proposes promising solutions to overcome time complexity and the curse of the dimensionality reduction problem.

The integration of GWOFS and MLP results in a robust hybrid model that can adapt to diverse software datasets. This feature selection process harnesses the cooperative hunting behavior of wolves, allowing for the exploration of critical feature combinations. The selected features are then fed into an MLP, a powerful artificial neural network (ANN) known for its capability to learn intricate patterns within software metrics. MLP serves as the predictive engine, utilizing the curated feature set to model and classify software defects accurately.

The performance evaluation of the GWOFS-MLP hybrid model on a real-world software defect dataset demonstrates its effectiveness. The model achieves a remarkable training accuracy of 97.69% and a testing accuracy of 97.99%. Additionally, the receiver operating characteristic area under the curve (ROC-AUC) score of 0.89 highlights the model’s ability to discriminate between defective and defect-free software components.

Experimental implementations using machine learning-based techniques with feature reduction are conducted to validate the proposed solutions. The goal is to enhance SDP’s accuracy, relevance and efficiency, ultimately improving software quality assurance processes. The confusion matrix further illustrates the model’s performance, with only a small number of false positives and false negatives.

The current study aims to empirically analyze the influence of different information sources, together with the persuasiveness of recommender systems (RSs) on the consumer’s purchase intention (PI). It also expands the research on RSs from the point of view of consumer behavior and psychology, considering perceived usefulness and relevance. In addition, it analyzes how different types of personalized recommendations, along with non-personalized ones, influence PI.

The proposed model has been validated using partial least squares structural equation modeling (PLS-SEM), based on the data collected from 597 online shoppers.

This study proves that both information search and RSs influence PI, being complementary rather than mutually exclusive. Recommender systems’ findings indicate that the PI is primarily influenced by the perceived relevance of RSs, the information provided by manufacturers and reviews. Moreover, only the influence of the perceived usefulness of personalized recommendations strongly affects PI. Conversely, non-personalized recommendations do not affect PI.

Developers should focus on increasing the perceived usefulness and relevance of RSs. Thus, they could adopt the hybridization of RSs with the aggregation of both personal shopping behavior and social network contacts. It should integrate information signals from multiple sources to include sentiment extracted from reviews or links to the manufacturer’s page. Furthermore, the recommendation of discounted products must be only for products preferred by customers, because only these influence the PI.

This research provides a structural model that examines together, for the first time, the influence on the PI of the main RSs and sources of information.

The choice of a sales channel for fresh vegetables is an important decision a farmer can make. Typically, the farmers rely on their personal experience in directing the produce to a sales channel. This study examines how sales forecasting of fresh vegetables along multiple channels enables marginal and small-scale farmers to maximize their revenue by proportionately allocating the produce considering their short shelf life.

Machine learning models, namely long short-term memory (LSTM), convolution neural network (CNN) and traditional methods such as autoregressive integrated moving average (ARIMA) and weighted moving average (WMA) are developed and tested for demand forecasting of vegetables through three different channels, namely direct (Jaivasree), regulated (World market) and cooperative (Horticorp).

The results show that machine learning methods (LSTM/CNN) provide better forecasts for regulated (World market) and cooperative (Horticorp) channels, while traditional moving average yields a better result for direct (Jaivasree) channel where the sales volume is less as compared to the remaining two channels.

The price of vegetables is not considered as the government sets the base price for the vegetables.

The existing literature lacks models and approaches to predict the sales of fresh vegetables for marginal and small-scale farmers of developing economies like India. In this research, the authors forecast the sales of commonly used fresh vegetables for small-scale farmers of Kerala in India based on a set of 130 weekly time series data obtained from the Kerala Horticorp.

Using perspectives from the technology affordance and social capital theories, this study aims to unpack the process through which platform-enabled co-development unfolds in supply chain contexts. Specifically, it explores how innovation outcomes can be fostered through platform affordances and supply chain relationship (SCR) capital.

The paper integrates literature on digital platforms, SCRs and co-development to produce an integrative framework, developing propositions on the relationships among digital platforms, SCR capital and innovation outcomes.

The authors identify affordances for distinctive strategic use of platforms: value co-creation, relationship building and strategic learning. The authors discuss ways in which each affordance contributes to the advances in SCR capital, thus altogether enabling focal firms to orchestrate and integrate internal and external resources to attain incremental and radical innovation.

Based on the proposed research framework, further empirical studies can use quantitative data to measure the relationship between affordances and SCR capital and use longitudinal case studies to explore how affordances and SCR capital evolve to provide more fine-grained and contextualised information in different research settings.

This paper sheds light on how the relation between the adoption of digital platforms and SCR capital shapes digitally enabled service co-development. The authors provide an alternative explanation of resource integration in platform-mediated supply chain contexts and enrich the related literature on how digital platforms can maximise value from introducing ambidextrous innovation by leveraging internal and external resources.

This paper aims to empirically investigate the typologies of phygital (synaeresis of “physical” and “digital”) customer experiences (CXs) that can arise in high-tech retail based on the intensity of consumers' responses and reactions to the stimuli triggered by firms. Moreover, it explores how firms attempt to shape the architecture of the phygital CXs. Notably, this article identifies the flexible and agile strategies implemented by firms to enhance the several typologies of phygital CXs, with the intention of better exploiting physical and digital features to respond to the differences in customers' needs, preferences and expectations.

This study performs an in-depth exploratory single-case study based on semi-structured interviews with the customers, managers and employees of the Webidoo Store.

This study develops a framework illustrating the main typologies of ordinary (“hostile”, “controversial” and “disappointing”) and extraordinary (“passionate” and “explorative”) CXs that can arise in phygital contexts. Also, it identifies some key flexible and agile strategies (“decompressive strategy”, “mentoring strategy”, “prompting strategy” and “entertaining strategy”) that companies might follow to adjust their offerings and respond quickly to the different forms of phygital CXs to create a more compelling experience tailored to customers' needs, preferences and expectations.

Among the study's limitations are the single-case study methodology and a specific setting like the Italian one. As a result, future studies could broaden the study to include other research contexts and countries. The paper offers significant managerial insights based on the many forms of CX across ordinary and extraordinary CXs. Thus, it provides critical takeaways for businesses to meet customer demand.

This paper analyzes the different typologies of ordinary and extraordinary CXs that could occur in phygital contexts based on the intensity of consumers' responses and reactions to firms' stimuli. Also, it explores how firms attempt to shape the architecture of the phygital CXs through flexible and agile strategies. From this paper, managers and decision-makers can reflect on successful strategies they could use to affect the stimuli to which customers respond in an agile manner, thus enhancing phygital CXs.

The dispute resolution process in the construction industry is known for delays in settlement, with some cases even escalating to complex arbitration and litigation. To avoid conflicts turning into disputes, the parties need to be proactive in identifying and resolving conflicts in their nascent stages. It is here that innovative lean construction practices can potentially act as a game-changer to avoid disputes, and this study aims to attempt to understand this phenomenon empirically.

A questionnaire-based empirical study, followed by semi-structured interviews, is conducted to understand the relevance of key tenets of lean principles in dispute avoidance.

Although stakeholders agree on the usefulness and practicality of lean principles in dispute avoidance, the extent of agreement is lesser when it comes to its implementation practicality. Moreover, there is a demographic influence observed on lean tenets such as “open communication”, “stakeholder collaboration” and “constraint identification”.

The results point towards an approach that combines contractual mandate, training and awareness creation to iron out the differences in the usefulness and practicality of lean approaches to avoid disputes.

Lean implementation is widely discussed in many construction contexts, such as sustainability, productivity improvement and planning. However, a discussion on lean philosophy’s role in dispute avoidance is muted. Therefore, this study assumes significance.

Retailers are increasingly adding multiple platform apps. For instance, Hilton Hotel is listed on booking.com, Expedia and TripAdvisor. The purpose of this study is to examine whether and how the adoption of a second homogenous mobile platform app by new and existing consumers affects their purchasing behavior in both the original app and the overall platform apps.

With 604,864 unique data from a Chinese fast-food company, which sequentially add three food delivery platforms, this paper explores the influence of a second homogeneous mobile platform app adoption on consumer purchase frequency, order size and spending.

The results of the log-linear regression model show that multiplatform consumers are more profitable than single-platform consumers. For both existing and new consumers, multiplatform adoption would increase purchase frequency, decrease order size and increase total spending with the retailer. However, for existing consumers, multiplatform adopters are more likely to buy less frequently, spend less per order and have lower total spending in the original platform app.

This paper contributes to platform addition and multichannel literature by empirically finding that multiplatform adopters, both new and existing consumers, are more profitable than single-platform consumers. Managerially, the results suggest that companies should not hesitate to add multiple platforms and should encourage consumers to use multiple mobile apps.

First, this study examines the multiplatform addition effect on both new and existing consumers, which has not been discussed yet. Second, this study contributes to multichannel literature by finding that multiplatform consumers are more profitable than single-platform consumers. Third, unlike Rong et al. (2021), this study supports that channel capability theory is still valid in the homogenous mobile-to-mobile channel expansion context.