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The use of technology in 4th industrial revolution is at its peak. Industries are trying to reduce the consumption of resources by effectively utilizing information and technology to connect different functioning agents of the manufacturing industry. Without digitization “Industry 4.0” will be a virtual reality. The present survey-based study explores the factual status of digital manufacturing in the Northern India.

After an extensive literature review, a questionnaire was designed to gather different viewpoints of Indian industrial practitioners. The first half contains questions related to north Indian demographic factors which may affect digitalization of India. The latter half includes the queries concerned with various operational factors (or drivers) driving the digital revolution without ignoring Indian constraints.

The focus of this survey was to understand the current level of digital revolution under the ongoing push by the Indian government focused upon digital movement. The analysis included non-parametric testing of the various demographic and functional factors impacting the digital echoes, specifically in Northern India. Findings such as technological upgradations were independent of type of industry, the turnover or the location. About 10 key operational factors were thoughtfully grouped into three major categories—internal Research and Development (R&D), the capability of the supply chain and the capacity to adapt to the market. These factors were then examined to understand how they contribute to digital manufacturing, utilizing an appropriate ordinal logistic regression. The resulting predictive analysis provides seldom-seen insights and valuable suggestions for the most effective deployment of digitalization in Indian industries.

The country-specific Industry 4.0 literature is quite limited. The survey mainly focuses on the National Capital Region. The number of demographic and functional factors can further be incorporated. Moreover, an addition of factors related to ecology, environment and society can make the study more insightful.

The present work provides valuable insights about the current status of digitization and expects to facilitate public or private policymakers to implement digital technologies in India with less efforts and the least resistance. It empowers India towards Industry 4.0 based tools and techniques and creates new socio-economic dimensions for the sustainable development.

The quantitative nature of the study and its statistical predictions (data-based) are novel. The clubbing of similar success factors to avoid inter-collinearity and complexity is seldom seen. The predictive analytics provided in this study is quite elusive as it provides directions with logic. It will help the Indian Government and industrial strategists to plan and perform their interventions accordingly.

This paper investigates how two characteristics of language, abstractness vs concreteness and narrativity, influence user engagement in communication exercises on innovation targeted to the general audience. The proposed conceptual model suggests that innovation fits well with more abstract language because of the association of innovation with imagination and distal construal. Moreover, communication of innovation may benefit from greater adherence to the narrativity arc, that is, early staging, increasing plot progression and climax optimal point. These effects are moderated by content variety and emotional tone, respectively.

Based on a Latent Dirichlet allocation (LDA) application on a sample of 3225 TED Talks transcripts, the authors identify 287 TED Talks on innovation, and then applied econometric analyses to test the hypotheses on the effects of abstractness vs concreteness and narrativity on engagement, and on the moderation effects of content variety and emotional tone.

The authors found that abstractness (vs concreteness) and narrativity have positive effects on engagement. These two effects are stronger with higher content variety and more positive emotional tone, respectively.

This paper extends the literature on communication of innovation, linguistics and text analysis by evaluating the roles of abstractness vs concreteness and narrativity in shaping appreciation of innovation.

This paper reports conceptual and empirical analyses on innovation dissemination through a popular medium – TED Talks – and applies modern text analysis algorithms to test hypotheses on the effects of two pivotal dimensions of language on user engagement.

The application of indigo dyes in the denim industries has been criticised due to the introduction of non-renewable oxidation products into the environment. Previous studies have investigated that reducing sugars can be used as green alternatives to sodium dithionite in the indigo dyeing of cotton fabric owing to their reduced and stable redox potential in the dye bath. The purpose of this study was to dye denim cotton fabric with indigo dye using various reducing sugars and alkalis. The use of sucrose and potassium hydroxide (KOH) for indigo dyeing has been explored for the first time.

A mixed factorial design with four variables including alkali, pH, number of dips and type of reducing sugar at different levels was studied to identify a significant correlation between the effect of these variables on the colour strength and fastness properties of the dyeings.

Investigations were made to examine the significant factors and interactions of the selected responses in the eco-friendly dyeing method. This process has the potential to reduce the load of sulphite and sulphate generated in the dyebath due to the use of a conventional reducing agent, sodium dithionite. The colour strength of the dyeing reduced with fructose was found to be better than other reducing sugars and significantly influenced by the number of dips, pH levels and the interaction between pH and reducing sugars. Using fructose for indigo dyeing with two dips at a pH of 11.5, using KOH as an alkali, results in higher colour strength values. The fastness properties of the indigo-dyed sample with reducing sugars ranging from fair to good or good to excellent. Specifically, colour change receives a rating of grey scale 3–4, staining 4–5, dry rubbing 4 and light fastness 3–4. These assessments hold true across various factors such as the type of reducing sugar, alkali, pH and the number of dips. The optimised parameters leading to improved colour strength and fastness properties are also discussed.

This dyeing technique is novel and a green alternative to dithionite denim dyeing. This process is found to be useful for indigo dyeing of denim fabric leading to reduced and stable redox potential in the dyebath and acceptable colour strength of the dyed fabric.

In recent years, there has been growing interest in the use of stainless steel (SS) in reinforced concrete (RC) structures due to its distinctive corrosion resistance and excellent mechanical properties. To ensure effective synergy between SS and concrete, it is necessary to develop a time-saving approach to accurately determine the ultimate bond strength τ_u between the two materials in RC structures.

Three robust machine learning (ML) models, including support vector regression (SVR), random forest (RF) and extreme gradient boosting (XGBoost), are employed to predict τ_u between ribbed SS and concrete. Model hyperparameters are fine-tuned using Bayesian optimization (BO) with 10-fold cross-validation. The interpretable techniques including partial dependence plots (PDPs) and Shapley additive explanation (SHAP) are also utilized to figure out the relationship between input features and output for the best model.

Among the three ML models, BO-XGBoost exhibits the strongest generalization and highest accuracy in estimating τ_u. According to SHAP value-based feature importance, compressive strength of concrete f_c emerges as the most prominent feature, followed by concrete cover thickness c, while the embedment length to diameter ratio l/d, and the diameter d for SS are deemed less important features. Properly increasing c and f_c can enhance τ_u between ribbed SS and concrete.

An online graphical user interface (GUI) has been developed based on BO-XGBoost to estimate τ_u. This tool can be utilized in structural design of RC structures with ribbed SS as reinforcement.

The study explores the relationship between the breadth of external pressures facing leaders of small and medium-sized enterprises (SMEs) and the entrepreneurial stance they adopt for their firm, that is, entrepreneurial orientation (EO).

Blending attention theory with EO literature, we argue that increasing breadth of external pressures will challenge leaders' attentions with implications for how they seek innovation, risk-taking and bold acts. We highlight an inflection point after which a negative relationship between the breadth of external pressure and EO will turn positive. We use data from a survey of 125 small-sized wineries in France to test this and capture a range of 15 external pressures on entrepreneurs.

The main tests and additional robustness tests provide support. It is the breadth of external pressures – as opposed to intensity of any one specific form of pressure – that plays a fundamental role in shaping leaders' adoption of EO in small enterprises over and above internal characteristics.

While the results may be context-dependent, they provide support for an attention-based view of entrepreneurial responses by leaders of SMEs under pressure.

SME leaders and entrepreneurs should be aware of how their attention is challenged by breadth of pressures from external sources, as this can influence the EO they adopt for their SME.

This nonlinear perspective on external pressures influencing the EO of small firms has not been taken in the EO literature to date, despite some recent work that considers only a small range of external pressures.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

The author identifies the traits of consumer resilience in emerging markets, classifies these major traits into five categories and analyses the influence relationships among them with distinctive focus on the psychological and personal resilience aspects.

The influence relations among the traits of consumer resilience from an expert perspective were identified with typical focus on electronic supply chains, and later the same was analysed through an intelligent influence modelling method, the grey causal modelling (GCM).

The major traits were analysed using the GCM, where the cause–consequence relations were observed for various objectives and the situational effects are noted. By constructing a magnitude plot and further a causal magnitude table, the important influence traits of consumer resilience for the considered case were observed and the same were auxiliary validated using an interpretive structural modelling (ISM) based approach.

As perceived from the results, it is evident that social support and recommendations from customers emerge as the principal influence traits of consumer resilience from an expert perspective, considering the case. The study can be further extended empirically to validate the findings.

Altogether, the author can recommend for practitioners that the influence of family, society, friends, peers as well as ratings from the customers can determine the level of consumer resilience. Hence, practitioners of customer relationship management can focus on improving the product and brand awareness among customers, so that more customers may recommend for typical products.

Consumer resilience depend on several factors, where the author has identified 25 major traits of the same and classified them into five major categories, including individual psychological factors, individual attitudes, individual socio demographic factors, micro environmental factors and macro environmental factors and the influence relations among them were studied from an expert perspective.

This paper presents an experimental investigation in establishing the relationship between FDM process parameters and tensile strength of polycarbonate (PC) samples using the I-Optimal design.

I-optimal design methodology is used to plan the experiments by means of Minitab-17.1 software. Samples are manufactured using Stratsys FDM 400mc and tested as per ISO standards. Additionally, an artificial neural network model was developed and compared to the regression model in order to select an appropriate model for optimisation. Finally, the genetic algorithm (GA) solver is executed for improvement of tensile strength of FDM built PC components.

This study demonstrates that the selected process parameters (raster angle, raster to raster air gap, build orientation about Y axis and the number of contours) had significant effect on tensile strength with raster angle being the most influential factor. Increasing the build orientation about Y axis produced specimens with compact structures that resulted in improved fracture resistance.

The fitted regression model has a p-value less than 0.05 which suggests that the model terms significantly represent the tensile strength of PC samples. Further, from the normal probability plot it was found that the residuals follow a straight line, thus the developed model provides adequate predictions. Furthermore, from the validation runs, a close agreement between the predicted and actual values was seen along the reference line which further supports satisfactory model predictions.

This study successfully investigated the effects of the selected process parameters - raster angle, raster to raster air gap, build orientation about Y axis and the number of contours - on tensile strength of PC samples utilising the I-optimal design and ANOVA. In addition, for prediction of the part strength, regression and ANN models were developed. The selected ANN model was optimised using the GA-solver for determination of optimal parameter settings.

The proposed ANN-GA approach is more appropriate to establish the non-linear relationship between the selected process parameters and tensile strength. Further, the proposed ANN-GA methodology can assist in manufacture of various industrial products with Nylon, polyethylene terephthalate glycol (PETG) and PET as new 3DP materials.

The purpose of this paper is to investigate the vehicle-based sensor effect and pavement temperature on road condition assessment, as well as to compute a threshold value for the classification of pavement conditions.

Four sensors were placed on the vehicle’s control arms and one inside the vehicle to collect vibration acceleration data for analysis. The Analysis of Variance (ANOVA) tests were performed to diagnose the effect of the vehicle-based sensors’ placement in the field. To classify road conditions and identify pavement distress (point of interest), the probability distribution was applied based on the magnitude values of vibration data.

Results from ANOVA indicate that pavement sensing patterns from the sensors placed on the front control arms were statistically significant, and there is no difference between the sensors placed on the same side of the vehicle (e.g., left or right side). A reference threshold (i.e., 1.7 g) was computed from the distribution fitting method to classify road conditions and identify the road distress based on the magnitude values that combine all acceleration along three axes. In addition, the pavement temperature was found to be highly correlated with the sensing patterns, which is noteworthy for future projects.

The paper investigates the effect of pavement sensors’ placement in assessing road conditions, emphasizing the implications for future road condition assessment projects. A threshold value for classifying road conditions was proposed and applied in class assignments (I-17 highway projects).