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Muga is an exclusive naturally golden coloured wild silk obtained by the Antherea assamensis silkworm species grown in the north-eastern region of India. Although many research studies on Bombyx mori silk can be found, but studies that involve muga silk is uncommon. This article attempts to characterise the muga cocoon of two commercially available crops (Kotia and Jethua) in three prominent muga rearing regions. Reeling studies on these have also been conducted to assess the regional and seasonal influences on silk reelability performance. Kotia cocoons are found to possess better cocoon quality and reeling performance in comparison to Jethua muga cocoons.

Performance assessment of layouts requires a systematic approach because of its multi-objective nature. The purpose of this paper is to propose a framework to the performance assessment of layout designs.

A layout performance assessment framework is proposed, grounded on a literature review. Then, the causal relationships and prioritization of the sub-criteria are analyzed by fuzzy Decision Making Trial and Evaluation Laboratory technique in an elevator and escalator-manufacturing firm.

An integrated holistic performance assessment framework, specifically, the 7 criteria, 19 sub-criteria and 112 measures, are studied in this model which represents causal relationships and prioritization of sub-criteria.

The proposed framework can be generalized, because an integrative framework can be used in future empirical studies to analyze performance of layout design. However, the causal relationships and prioritization among sub-criteria are analyzed based on the needs and capabilities of the individual company; therefore, the results of the causal relationships are company specific.

With this framework, the companies may assess their current layout’s performance, may analyze causal relationships and prioritization of sub-criteria.

There are very few models or frameworks regarding the performance assessment of layout designs. In this paper, a new conceptual holistic framework was proposed as three-dimensional hierarchy, which includes the main criteria, sub-criteria and the measures, respectively. Cost, flow, flexibility, surrounding environment, environment quality, time and characteristics are identified as the main criteria for the layout design performance assessment. In addition, cause-effect relationships, which will be the base for improvement of the performance, are found.

The study here responds to the view that the crucial problem in strategic management (research) is firm heterogeneity – why firms adopt different strategies and structures, why heterogeneity persists, and why competitors perform differently. The present study applies complexity theory tenets and a “neo-configurational perspective” of Misangyi et al. (2016) in proposing complex antecedent conditions affecting complex outcome conditions. Rather than examining variable directional relationships using null hypotheses statistical tests, the study examines case-based conditions using somewhat precise outcome tests (SPOT). The complex outcome conditions include firms with high financial performances in declining markets and firms with low financial performances in growing markets – the study focuses on seemingly paradoxical outcomes. The study here examines firm strategies and outcomes for separate samples of cross-sectional data of manufacturing firms with headquarters in one of two nations: Finland (n = 820) and Hungary (n = 300). The study includes examining the predictive validities of the models. The study contributes conceptual advances of complex firm orientation configurations and complex firm performance capabilities configurations as mediating conditions between firmographics, firm resources, and the two final complex outcome conditions (high performance in declining markets and low performance in growing markets). The study contributes by showing how fuzzy-logic computing with words (Zadeh, 1966) advances strategic management research toward achieving requisite variety to overcome the theory-analytic mismatch pervasive currently in the discipline (Fiss, 2007, 2011) – thus, this study is a useful step toward solving the crucial problem of how to explain firm heterogeneity.

The purpose of this paper is to uncover the significance of lean manufacturing technique in manufacturing environments.

Lean manufacturing is a management approach focused on incremental improvements in operations. Different lean strategies are being utilized by manufacturing industry to improve the performance of current manufacturing system processes. This study attempts to evaluate the performance of different lean manufacturing tools in the manufacturing industry of northern India. The importance level of different lean tools, important benefits achieved after successful implementation of lean manufacturing approach and benefits occurred after implementation of different lean tools have been identified. A questionnaire survey in the case company has been performed and the most important element of lean manufacturing has been implemented.

Results explicitly depict that just-in-time manufacturing is the most important element of lean manufacturing. Results indicate the net savings of rupees 242,208 annually after implementing lean manufacturing technique in a case company.

The paper demonstrates the practical application of lean technique showing how it can bring real breakthroughs in saving cost in the manufacturing industry.

The purpose of this paper is to assess whether multiple directorships have an influence on earnings management for Australian publicly listed firms. This paper attempts to determine whether boards with multiple directorships are effective monitors and are able to constrain earnings management activities.

The study adopts resource dependency theory on the relationship between multiple directorships and the extent of earnings management. Data analysis is based on publicly listed firms on Australian Stock Exchange utilising SIRCA database with a final pooled sample of 1,815 firm-year observations from 2008 to 2012.

Using different measures of multiple directorships, it is found that firms having board of directors with multiple directorships exhibit lower levels of earnings management. The results validate the applicability of resource dependency theory on the relationship between multiple directorships and the extent of earnings management suggesting that directors with multiple board seats by sharing experiences, skills, information and other resources limit the extent of earnings management by firms. Evidence also suggests that earnings management behaviour is more pronounced in larger firms compared to smaller firms and as predicted, industry audit specialists restrain earnings management activities.

This study introduces methodological enhancements to the literature as it measures the multiple directorships in a number of different ways. Firms may be encouraged to actively seek board members with diverse backgrounds, international exposure/experience and pertinent skill-sets with multiple board memberships. These benefits will assist firms to determine the optimal board composition that will enable it to function effectively.

Empirical studies on the association between multiple directorships and earnings management in Australia are scarce and this paper provides an update of the effect of multiple directorships on earnings quality in Australia.

Plant cultivation holds a pivotal role in agriculture, necessitating precise disease identification for the overall health of plants. This research conducts a comprehensive comparative analysis between two prominent deep learning algorithms, convolutional neural network (CNN) and DenseNet121, with the goal of enhancing disease identification in tomato plant leaves.

The dataset employed in this investigation is a fusion of primary data and publicly available data, covering 13 distinct disease labels and a total of 18,815 images for model training. The data pre-processing workflow prioritized activities such as normalizing pixel dimensions, implementing data augmentation and achieving dataset balance, which were subsequently followed by the modeling and testing phases.

Experimental findings elucidated the superior performance of the DenseNet121 model over the CNN model in disease classification on tomato leaves. The DenseNet121 model attained a training accuracy of 98.27%, a validation accuracy of 87.47% and average recall, precision and F1-score metrics of 87, 88 and 87%, respectively. The ultimate aim was to implement the optimal classifier for a mobile application, namely Tanamin.id, and, therefore, DenseNet121 was the preferred choice.

The integration of private and public data significantly contributes to determining the optimal method. The CNN method achieves a training accuracy of 90.41% and a validation accuracy of 83.33%, whereas the DenseNet121 method excels with a training accuracy of 98.27% and a validation accuracy of 87.47%. The DenseNet121 architecture, comprising 121 layers, a global average pooling (GAP) layer and a dropout layer, showcases its effectiveness. Leveraging categorical_crossentropy as the loss function and utilizing the stochastic gradien descent (SGD) Optimizer with a learning rate of 0.001 guides the course of the training process. The experimental results unequivocally demonstrate the superior performance of DenseNet121 over CNN.

In Finance Capital Hilferding suggests that, in the early stages of capitalist development, banks engage in short-term lending for “circulation” purposes, while concerning themselves with their liquidity. As capitalist development proceeds, banks lend longer-term for “investment” purposes, and their concern shifts to securing their solvency. Consequently, banks and industrial enterprises become amalgamated into “finance capital,” developing mutual “commitment” relations, and giving a bank-based character to the financial system. The core of Hilferding’s argument resembles Smith’s analysis of banking, but in important respects his argument is reminiscent of Steuart’s earlier and opposing analysis. Hilferding was able to integrate key elements of both approaches to banking by relying on Marx’s concept of loanable money capital, as well as on Marx’s claim that the average rate of interest is normally lower than the average rate of profit. However, Hilferding’s view that financial systems spontaneously become bank-based has not stood the test of time well. This failure is probably due to underestimating the importance of state intervention in shaping the financial system.

A necessary preprocessing of document classification is to label some documents so that a classifier can be built based on which the remaining documents can be classified. Because each document differs in length and complexity, the cost of labeling each document is different. The purpose of this paper is to consider how to select a subset of documents for labeling with a limited budget so that the total cost of the spending does not exceed the budget limit, while at the same time building a classifier with the best classification results.

In this paper, a framework is proposed to select the instances for labeling that integrate two clustering algorithms and two centroid selection methods. From the selected and labeled instances, five different classifiers were constructed with good classification accuracy to prove the superiority of the selected instances.

Experimental results show that this method can establish a training data set containing the most suitable data under the premise of considering the cost constraints. The data set considers both “data representativeness” and “data selection cost,” so that the training data labeled by experts can effectively establish a classifier with high accuracy.

No previous research has considered how to establish a training set with a cost limit when each document has a distinct labeling cost. This paper is the first attempt to resolve this issue.

Companies have been implementing lean manufacturing to improve their business performances. However, many of them have difficulties in the implementation because of various barriers, thus encountering failures. This paper aims to prioritize and analyze the lean barriers for better understanding and interpretation for successful lean implementation.

Extensive literature review has been carried out to identify the lean barriers. Subsequently, total interpretive structural modeling (TISM) has been adopted where lean experts’ inputs have been sought to obtain the self-interaction and reachability matrix. Further, driving power and dependence of lean barriers have been derived, and TISM-based lean barrier model has been developed.

Insufficient management time, insufficient supervisory skills and insufficient senior management skills are the significant barriers with highest driving power and lowest dependence. With low driving power, cost- and funding-related barriers such as cost of the investment, internal funding and external funding are found to be less important barriers.

This model provides a more realistic approach to the problems faced by practitioners during lean implementation. Thus, it provides a roadmap to implement lean by focusing on reducing or eliminating important barriers.

The paper not only provides a TISM-based model of contextual relationships among lean barriers but also describes the validation of this model.

Although many studies explored the drivers of innovative development and the innovation performance of different countries, very few studies looked at the association of the country’s GII score with the qualitative indicators of innovation performance. The purpose of this paper is to contribute such an investigation by looking at the Republic of Kazakhstan (79th in 2019 GII ranking).

This study looks at eight dynamic variables, among which one dependent (the GII score) and seven independent (R&D spending, innovation grants, the total cost of innovative goods and services, the percentage of innovative organizations, the share of innovative goods and services in gross domestic product (GDP) and the number of R&D staff and R&D institutions) variables associated with innovation performance. Changes in variables were tracked over the period from 2010 to 2018..

The study found that the Kazakhstan’s GII score was reliant on variables, such as the percentage of innovative organizations, the value of innovative goods and services as a share of GDP, R&D spending and the cost of innovative goods and services. At the same time, the number of R&D institutions, innovation grants and number of R&D staff had no substantial impact on the GII score of Kazakhstan.

Using the proposed approach, this study proved that factors, which have no direct association with the country’s level of innovative development expressed in GII, could have a significant synergistic impact on this indicator.