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This research aims to identify the key factors affecting Outbound Open Innovation between Startups and Big organizations using the multiple criteria decision-making analysis (MCDM) approach. The MCDM technique ranks the four key factors identified from the literature study that can help to improve collaboration opportunities with Startups.

Identification of key factors affecting Outbound Open Innovation between Startups and big organizations based on extant literature. A questionnaire is prepared based on these four identified key factors to gather views of the startup's employees, from the designer level to the startup's founder. MCDM techniques are used to evaluate the questionnaire. The ensemble technique is used to rank the key factors coming from three different MCDM methods.

The findings from the MCDM approach and Ensemble techniques give insight to the big organizations to facilitate outbound Open Innovation effectively. It also provides insight into the requirements of the startups and the kind of support they seek from the big organizations. The ranking can help the big organization close the gaps and make an informed decision to increase the effectiveness of the collaborations and boost innovation.

This is a unique research work where the MCDM approach is used to identify the ranking of key factors affecting outbound open innovation between startups and big organizations. The MCDM technique is followed by the ensemble method to rationalize the findings. Technology Relevance ranks highest, followed by Innovation Ecosystem, Organization commitment and Knowledge Sharing.

This article investigated whether the executives' compensation and corporate governance attributes are aligned with stakeholders' demands for higher corporate voluntary disclosures. Moreover, the study also examined the moderating role of the auditor's reputation in the direction of association among executive compensation, corporate governance attributes, and voluntary disclosures.

The study used a sample of S&P BSE index constituents' 90 Indian firms for 2017–2019. The voluntary disclosure scores were fetched from the India Disclosure Index Report published by FTI Consulting. This analysis was carried out in two parts by applying four panel-data regression models in the agency and signalling theories framework. First, the study examined the association between executive compensation, board strength, composition, gender diversity, and voluntary disclosures. Second, the article investigated the moderating role of the “Big 4” in the direction of association among executive compensation, corporate governance attributes, and voluntary disclosures.

The willingness of executives to share private information with stakeholders depends on the compensation they receive from their employer. The higher compensation paid to executives leads to a higher “tone from the top,” which is better aligned with stakeholder interests. Further, the research also found that bigger board sizes, a higher proportion of independent and woman directors (indicators of good governance), and an auditor's reputation are associated with increased voluntary disclosure.

The findings showed that the executives' compensation and corporate governance attributes are aligned with stakeholders' demand for higher voluntary information from firms. Moreover, the study also found that the “Big 4” play a moderating role in this direction. The choice of a reputed auditor indicates the firms' long-term positive future perspectives, which strengthens investor confidence in the financial market.

The study suggests that fair executive compensation can address the agency problem.

This research furnishes managers and different stakeholders with significant implications of executives' compensation, corporate governance, and auditor's reputation in the best interests of a firm through reducing potential risks of information asymmetry.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

The goal of this research is to analyse the obstacles to the implementation of mobile health (mHealth) in India and to gain an understanding of the contextual inter-relationships that exist amongst those obstacles.

Potential barriers and their interrelationships in their respective contexts have been uncovered. Using MICMAC analysis, the categorization of these barriers was done based on their degree of reliance and driving power (DP). Furthermore, an interpretive structural modeling (ISM) framework for the barriers to mHealth activities in India has been proposed.

The study explores a total of 15 factors that reduce the efficiency of mHealth adoption in India. The findings of the Matrix Cross-Reference Multiplication Applied to a Classification (MICMAC) investigation show that the economic situation of the government, concerns regarding the safety of intellectual technologies and privacy issues are the primary obstacles because of the significant driving power they have in mHealth applications.

Promoters of mHealth practices may be able to make better plans if they understand the social barriers and how they affect each other; this leads to easier adoption of these practices. The findings of this study might be helpful for governments of developing nations to produce standards relating to the deployment of mHealth; this will increase the efficiency with which it is adopted.

At this time, there is no comprehensive analysis of the factors that influence the adoption of mobile health care with social cognitive theory in developing nations like India. In addition, there is a lack of research in investigating how each of these elements affects the success of mHealth activities and how the others interact with them. Because developed nations learnt the value of mHealth practices during the recent pandemic, this study, by investigating the obstacles to the adoption of mHealth and their inter-relationships, makes an important addition to both theory and practice.

Supply chain vulnerability (SCV) analysis is vital for manufacturers globally because it creates a pathway for building resilient supply chains in uncertain environments. This study aims to identify drivers of SCV in the Indian manufacturing sector.

Sixteen drivers were identified from the literature review and followed by expert interviews. Interpretive structural modeling was used to determine the hierarchical structural relationship among identified SCV factors.

It was found that risk is not a board room agenda. Misaligned performance measures with incentives and lack of risk dashboard are the causal factors of SCV. Supply chain security, centralized production and distribution and lack of trust in the supply chain were driven factors.

This provides new insights to assess and prioritize initiatives for supply chain sustainability in terms of continuing business operations. The structural model provides a systemic view of SCV and helps reduce vulnerability.

This paper adopts a modified Taguchi approach to develop empirical relationships to the performance characteristics (output responses) in terms of process variables and demonstrated their validity through comparison of test data. The method suggests a few tests as per the orthogonal array and provides complete information for all combinations of levels and process variables. This method also provides the estimated range of output responses so that the scatter in the repeated tests can be assessed prior to the tests.

In order to obtain defect-free products meeting the required specifications, researchers have conducted extensive experiments using powder bed fusion (PBF) process measuring the performance indicators (namely, relative density, surface roughness and hardness) to specify a set of printing parameters (namely, laser power, scanning speed and hatch spacing). A simple and reliable multi-objective optimization method is considered in this paper for specifying a set of optimal process parameters with SS316 L powder. It was reported that test samples printed even with optimal set of input variables revealed irregular shaped, microscopic porosities and improper melt pool formation.

Finally, based on detailed analysis, it is concluded that it is impossible to express the performance indicators, explicitly in terms of equivalent energy density (E_0ˆ*), which is a combination of multiple sets of selective laser melting (SLM) process parameters, with different performance indicators. Empirical relations for the performance indicators are developed in terms of SLM process parameters. Test data are within/close to the expected range.

Based on extensive analysis of the SS316 L data using modified Taguchi approach, the optimized process parameters are laser power = 298 W, scanning speed = 900 mm/s and hatch distance = 0.075 mm, for which the results of surface roughness = 2.77 Ra, relative density = 99.24%, hardness = 334 Hv and equivalent energy density is 4.062. The estimated data for the same are surface roughness is 3.733 Ra, relative density is 99.926%, hardness is 213.64 Hv and equivalent energy density is 3.677.

Even though equivalent energy density represents the energy input to the process, the findings of this paper conclude that energy density should no longer be considered as a dependent process parameter, as it provides multiple results for the specified energy density. This aspect has been successfully demonstrated in this paper using test data.