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This study aims to explore how consumers’ purchase intentions concerning dietary supplements are affected by two pivotal factors: pharmacist professionalism and celebrity endorser attractiveness. In addition, this study investigates the moderating role of consumers’ awareness of product knowledge about dietary supplements in the relationship between brand trust and purchase intention.

The research data was collected through online questionnaires from a convenience sample of 340 individuals residing in Taiwan. The research model, consisting of four hypotheses, was formulated based on a literature review. The partial least squares structural equation modeling technique was used for data analysis.

The findings indicate positive effects of both pharmacist professionalism and celebrity endorser attractiveness on dietary supplement brand trust. The results also suggest a significant impact of brand trust on purchase intention. Finally, product knowledge significantly moderates the relationship between brand trust and purchase intention.

The findings from this study provide valuable insights for marketers, pharmacists and practitioners about the drivers of consumers’ dietary supplement purchase behavior. It will enable marketers to develop better strategies for the dietary supplement market segment.

This paper has made a significant contribution to the understanding of consumer buying intentions toward dietary supplements, addressing a gap in the literature. First, unlike previous research, this study considers both pharmacist professionalism and celebrity endorser attractiveness in the research model, effectively bridging this gap. Furthermore, this study identifies the pivotal role of brand trust as a critical factor in enhancing consumers’ purchase intentions toward dietary supplements, offering valuable insights for dietary supplement companies regarding consumer purchase decision-making.

This study aims to identify the strategic factors and their effects on the post-cross-border acquisitions (CBA) technological innovation performance of the acquiring firms. It develops a hierarchical model to examine the interrelationship between identified strategic factors such as strategic flexibility, strategic ambidexterity, environmental dynamism, etc.

This study uses modified total interpretive structural modeling qualitative methodology (m-TISM) to develop a hierarchical model and conducts a Matrice d’impacts croisés multiplication appliquée á un classment (MICMAC) analysis to show the interrelationship between strategic factors affects the acquirer’s post-CBA technological innovation performance. It determines the autonomous, dependent, linkage and independent strategic factors. It further uses comparative case analysis to empirically examine the strategic factors in real-time CBA situations.

This study shows the m-TISM-based hierarchical model highlighting the interrelation, level of autonomy, dependence and linkage among strategic factors affecting the acquirer’s post-CBA technological innovation performance. It suggests that strategic factors such as environmental dynamism, R&D competence, innovation capability and technological capability are largely autonomous and have significant driving power, whereas strategic ambidexterity and strategic flexibility are the connecting factors. post-M&A integration is the governing factor for technological innovation performance in CBA.

The strategists and practitioners could evaluate the key strategic factors having significant driving power for strategy formulation and implementing efficient policies. By implementing the m-TISM model acquiring a firm’s post-CBA performance can be enhanced. Future researchers might utilize quantitative methods like regression and structural equation modeling in the CBA context.

This study uses a novel m-TISM and MICMAC approach to identify the driving and dependent factors affecting post-CBA technological innovation performance. It further provides a detailed theoretical and conceptual understanding relating to the philosophy and establishes an interrelation amongst these under-researched strategic factors in CBA.

Given the rising popularity of mobile commerce among young consumers, this study aims to examine the effect of mobile applications service quality (MASQ), service convenience (SERCON) and satisfaction contributing to the retention of young consumers towards mobile applications.

Primary data were collected from 213 active online young smartphone users who have used mobile apps for shopping through a structured questionnaire. Structural equation modelling is used to analyse the data.

The results of this study reveal that both MASQ and SERCON strongly support satisfaction, which leads to the retention of young customers.

This study is one of the few relevant pieces of research that would benefit mretailers encompassing mobile commerce applications to improve their MASQ and SERCON with cutthroat competition in gaining and retaining young customers for shopping through smartphone applications.

This paper proposes selecting a construction project portfolio in the context of a public institution, which makes it possible to assess quantitative and qualitative criteria, thereby meeting the needs of the institution and the existing constraints.

The research design follows a framework using technique for order preference by similarity to ideal solution (TOPSIS) associated with integer linear programming.

The method involves a flow of assessments allowing criteria and weights to be elicited where outcomes are based on the experts' intra-criteria assessment of alternatives and decision-makers' inter-criteria assessment. This is of utmost interest to public organizations, where selections must result in benefits and lower costs, integrating the experts' technical and management perspectives.

Public institutions are characterized by having limited financial and personnel resources for project development despite having a high demand for requests not associated with profits, making it essential to have a framework that enables using multiple criteria to better evaluate the benefits related to these decisions.

The main contributions of this article are: (1) the proposition of a framework for selecting construction project portfolios considering the organization's strategic needs; (2) identifying quantitative and qualitative assessment criteria for project selection; (3) integrating TOPSIS with an optimization process for selecting the construction project portfolios and (4) providing a structured decision process for selecting the portfolio that best represents the interests of the institution within its limited resources and personnel.

The study explores the factors affecting the use of smart human resource management 4.0 (SHRM 4.0) practices and its effect on dynamic capabilities and, consequently, on organizational performance.

The authors used socio-technical and dynamic capabilities theory to propose the notable research model. The authors explored the factors driving the use of SHRM 4.0 practices and their contribution to organizational performance through the development of dynamic capabilities. The authors collected data from 383 senior HR managers using a structured questionnaire, and PLS-SEM was used to analyze the data.

The results show that socio-technical factors such as top management support, HR readiness, competitive pressure, technology readiness and perceived usefulness influence the use of SHRM 4.0 practices, whereas security and privacy concerns negatively influence them. Furthermore, the authors also found the use of SHRM 4.0 practices influencing the dynamic capacities (build (learning), integration and reconfiguration) and, subsequently, its impact on organizational performance.

Its novelty lies in developing a model using dynamic capabilities and socio-technical theory to explore how SHRM 4.0 practices influence organizational performance through dynamic capabilities. This study extends the literature on SHRM 4.0 practices, HR technology use, HR and dynamic capabilities by contributing to socio-technical theory and dynamic capabilities and expanding the scope of these theories in the area of HRM. It provides crucial insights into HR and top managers to benchmark SHRM 4.0 practices for improved organizational performance.

Considering the world population, an additional 415.1 billion m² of built floor will be needed by 2050, which could worsen the environmental impact of the construction industry that is responsible for one-third of global Carbon Emissions (CEs). Thus, the current construction practices need to be upgraded toward eco-friendly technologies. Building Information Modeling (BIM) proved a significant potential to enhance Building and Infrastructure (B&I) ecological performances. However, no previous study has evaluated the nexus between BIM and B&I CEs. This study aims to fill this gap by disclosing the research evolution and metrics and key concepts and tools associated with this nexus.

A mixed-method design was adopted based on scientometric and scoping reviews of 52 consistent peer-reviewed papers collected from 3 large scientific databases.

This study presented six research metrics and revealed that the nexus between BIM and CEs is a contemporary topic that involves seven main research themes. Moreover, it cast light on six key associated concepts: Life Cycle Assessment; Boundary limits; Building Life Cycle CE (BLCCE); Responsible sources for BLCCE; Green and integrated BIM; and sustainable buildings and related rating systems. Furthermore, it identified 56 nexus-related Information and Communication Technologies tools and 17 CE-coefficient databases and discussed their consistency.

This study will fill the knowledge gap by providing scholars, practitioners and decision-makers with a good grasp of the nexus between CEs and BIM and paving the path toward further research, strategies and technological solutions to decrease CEs of B&I sectors and their impacts on the climate change.

This study aims to provide a review for scour in complex rivers and streams with coarser bed material, steep longitudinal bed slopes and dynamic environments, in the interest of the safety and the economy of hydraulic structures. The knowledge of scour in such geographical complexities is very crucial for a comprehensive understanding of scour failures and for establishing definitive criteria to bridge this major research gap.

The existing available literature shows significant work done in case of silt, sand and small sized coarser bed material but any substantial work for bed material of gravel size or above is lacking, resulting in a wide gap. Though some researchers have attempted to explore possibilities of refining the existing models by adding pier size, shape, sediment non-uniformity and armouring effects, which otherwise have been given a miss by the various researchers, including the pioneer in the field Lacey–Inglis (1930). But still, a rational model for scour estimation in such complex conditions for global use is yet to come. This is because all the parameters governing the scour have not been studied properly till date as is evident from the globally available literature and is witnessed in the field too, in recurrent failure of hydraulic structures especially bridges.

The researchers presume that the finer materials move only as a result of erosion. However, in actual field conditions, it has been observed that the large-sized stones also roll down and cause huge erosion along the river bed and damage the hydraulic structures, especially in the steep river/stream beds along hilly slopes. This fact has been overlooked in the models available globally and has been highlighted only in the current work in an attempt to recognize this major research gap. A study carried out on a number of streams globally and in Jammu and Kashmir, India also, has shown that in steep river and stream beds with bed material consisting of gravel size or greater than gravel, large scour holes ranging from 1 m to 5 m were created by furious floods, and due to other unknown forces along the channel path and near foundations of hydraulic structures.

To the best of the authors’ knowledge, this work is purely original.

As voice search has progressively become a new way of information acquisition and human–computer interaction, this paper aims to explore the users' voice search behavior in human–vehicle interaction.

This study employed mixed research methods, including questionnaires and interviews. A total of 151 Amazon MTurk volunteers were recruited to complete a questionnaire based on their most recent and impressive voice search experience. After the questionnaire, this paper conducted an online interview with the participants.

This paper studied users' voice search behavior characteristics in the context of the human–vehicle interaction and analyzed the voice search content, search need, search motivation and user satisfaction. In addition, this paper studied the barriers and suggestions for voice search in human–vehicle interaction through a content analysis of the interviews.

This paper's analysis of users' barriers and suggestions has a specific reference value for optimizing the voice search interaction system and improving the service.

This study is exploratory research that seeks to identify users' voice search needs and tasks and investigate voice search satisfaction in human–vehicle interaction context.

The purpose of this study is to propose an alternative efficient 3D emotion recognition model for variable-length electroencephalogram (EEG) data.

Classical AMIGOS data set which comprises of multimodal records of varying lengths on mood, personality and other physiological aspects on emotional response is used for empirical assessment of the proposed overlapping sliding window (OSW) modelling framework. Two features are extracted using Fourier and Wavelet transforms: normalised band power (NBP) and normalised wavelet energy (NWE), respectively. The arousal, valence and dominance (AVD) emotions are predicted using one-dimension (1D) and two-dimensional (2D) convolution neural network (CNN) for both single and combined features.

The two-dimensional convolution neural network (2D CNN) outcomes on EEG signals of AMIGOS data set are observed to yield the highest accuracy, that is 96.63%, 95.87% and 96.30% for AVD, respectively, which is evidenced to be at least 6% higher as compared to the other available competitive approaches.

The present work is focussed on the less explored, complex AMIGOS (2018) data set which is imbalanced and of variable length. EEG emotion recognition-based work is widely available on simpler data sets. The following are the challenges of the AMIGOS data set addressed in the present work: handling of tensor form data; proposing an efficient method for generating sufficient equal-length samples corresponding to imbalanced and variable-length data.; selecting a suitable machine learning/deep learning model; improving the accuracy of the applied model.