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It is often argued that anything observable may be simulated on a computer. Using this as a basis, workers in artificial intelligence (AI) often go forward to maintain that machines can be made intelligent by machine simulation of human intelligence processes. There are two difficulties with this concept. The first difficulty lies in the knowledge of human intelligence processes that we have presently obtained and may possibly obtain in the near future. A more basic question is of the sufficiency of the concept itself. Simulation in itself is not sufficient to produce intelligent action where perhaps modelling might be. There are fundamental difficulties in the problem of establishing an adequate mapping function. It is held that there is insufficient correspondence between human and machine intelligence processes to allow human intelligence modelling on existing digital computers.

This paper aims to understand what kind of fashion product picture can arouse greater embodied mental simulation at two distinct steps of consumers' shopping journey (choice between options and purchase intention).

Two experimental studies were developed. Study 1 (n = 169) investigated consumers' purchase intention, and Study 2 (n = 156) investigated consumers' choice for a T-shirt displayed in an e-commerce store. The authors manipulated the product picture by considering pictures with the presence or absence of a human model wearing the product (flat vs. mannequin vs. human model without a face vs. human model with a face).

Consumers demonstrated greater choice and purchase intention for the picture that aroused greater embodied mental simulation. Different pictures aroused greater embodied mental simulation depending on the consumer journey step (choice between two options or purchase intention). Perceived product attractiveness influenced this finding.

The data on men and women were analyzed together due to the low number of male participants in both studies.

The results suggest that mannequin pictures should be used in situations involving product evaluation (e-commerce categories' pages) and that pictures with human models should be used in situations entailing further analysis of the product (e-commerce product page) to encourage purchase decisions. E-commerce managers also need to use pictures of human models when the product is viewed as less attractive.

This research investigated embodied mental simulation around product pictures at two distinct steps of consumers' shopping journey.

This paper reviews the pros and cons of different parametric modeling methods, which can provide a theoretical reference for parametric reconstruction of 3D human body models for virtual fitting.

In this study, we briefly analyze the mainstream datasets of models of the human body used in the area to provide a foundation for parametric methods of such reconstruction. We then analyze and compare parametric methods of reconstruction based on their use of the following forms of input data: point cloud data, image contours, sizes of features and points representing the joints. Finally, we summarize the advantages and problems of each method as well as the current challenges to the use of parametric modeling in virtual fitting and the opportunities provided by it.

Considering the aspects of integrity and accurate of representations of the shape and posture of the body, and the efficiency of the calculation of the requisite parameters, the reconstruction method of human body by integrating orthogonal image contour morphological features, multifeature size constraints and joint point positioning can better represent human body shape, posture and personalized feature size and has higher research value.

This article obtains a research thinking for reconstructing a 3D model for virtual fitting that is based on three kinds of data, which is helpful for establishing personalized and high-precision human body models.

Baron et al. briefly summarized the history of human performance modeling¹ (HPM) in their 1990 review. The application of control theory to aircraft simulations and the development of task network models stimulated the development of methods to represent the human contribution to system dynamics. These groundbreaking efforts first identified the manifold difficulties associated with the simulation of human performance in military settings, and many of these difficulties remain matters of contemporary concern. The technical challenges associated with the representation of human performance have endured, and military applications continue to be a major driver of interest in HBR. The expense and various difficulties associated with laboratory research, field studies, and operational tests have pushed modeling and simulation to center stage as an affordable alternative to empirical studies. Simulation is now an essential component of military force development, operational planning, engineering development and acquisition, and training.

Machine learning (ML) techniques are increasingly important in enabling business-to-business (B2B) companies to offer personalized services to business customers. On the other hand, humans play a critical role in dealing with uncertain situations and the relationship-building aspects of a B2B business. Most existing studies advocating human-ML augmentation simply posit the concept without providing a detailed view of augmentation. Therefore, the purpose of this paper is to investigate how human involvement can practically augment ML capabilities to develop a personalized information system (PIS) for business customers.

The authors developed a research framework to create an integrated human-ML PIS for business customers. The PIS was then implemented in the energy sector. Next, the accuracy of the PIS was evaluated using customer feedback. To this end, precision, recall and F1 evaluation metrics were used.

The computed figures of precision, recall and F1 (respectively, 0.73, 0.72 and 0.72) were all above 0.5; thus, the accuracy of the model was confirmed. Finally, the study presents the research model that illustrates how human involvement can augment ML capabilities in different stages of creating the PIS including the business/market understanding, data understanding, data collection and preparation, model creation and deployment and model evaluation phases.

This paper offers novel insight into the less-known phenomenon of human-ML augmentation for marketing purposes. Furthermore, the study contributes to the B2B personalization literature by elaborating on how human experts can augment ML computing power to create a PIS for business customers.

The purpose of this paper is to develop a parametric design method for 3D human bodies to be used in computer-aided style design and the 3D presentations of warp-knitted seamless garment.

In order to obtain 3D human bodies of different sizes, all of which have been based on anthropometric measurement, a human body model template was constructed by importing vertices and facets information in an OBJ model file which had been exported from POSER. A parametric model was then established by extracting feature information from the template model using a method combining 3D geometry analysis and human semantic analysis; this information included the template model’s feature points and measurements. By applying a mesh deformation method, based on the radius basis function interpolation, to the template model, different size human bodies were then generated according to user-specific anthropometric measurements.

The test results validated the method presented in this paper as a useful and effective approach to generate diffident size human models from a template model by modifying anthropometric measurements, which establishes a foundation for the style design and 3D presentations of warp-knitted seamless garments.

This paper provides parametric design methods for generating bodies of varying size according to different anthropometric measurements in the 3D domain, which is the basis of style design and 3D presentation for warp-knitted seamless garments.

The purpose of this paper is to propose an improved economic value model for human resource valuation. For this purpose, the probability of promoting people to a higher position and the satisfaction coefficient of employees and customers have been computed by competency and Kano models.

In order to calculate the probability of promoting people to a higher position, competency model and questionnaire have been used. The satisfaction coefficient of employees and customers has been calculated as a factor influencing the survival of an individual in the organization by using the Kano model. For this purpose, two questionnaires have been designed; one was completed by the employees and the other by the customers. The proposed model has been examined in a consulting company in Iran.

The human resource value of the company under study has been estimated over 29bn rials (Iranian currency). The obtained results indicate that the proposed approach as an integrative monetary and nonmonetary measure can remove the limitations of the economic value model.

The proposed model helps organizations in managing their human capitals more effectively.

In this study, the Kano and competency models have been integrated with one of the common models of human resource valuation, i.e. the economic value model. The proposed integrated model seems more effective compared to the basic model of economic value model. Application of the proposed model within the context of Iran for the first time would constitute as potential for contribution to the knowledge of human resource management in the developing countries.

This paper is concerned with identifying the role(s) of the individual employee(s) in the value creation process of three network‐based business models that are analyzed through case studies. The paper aims to focus on how the relationships between the network‐partners internally and also outside the network affect value creation.

The study conducted is a case study of three network‐based business models applying a semi‐structured interview approach as the primary source of data.

The study shows that, together with the terms “integrating” and “enabling” which the traditional literature tends to use, expressions like “building” and “aligning” can be added in order to describe the role of human resources in the value creation process of the network‐based business models. Human resources are found to be important for aligning the value proposition of the network as a whole to the customers' needs and expectations.

The generalizability of the results are restricted by the fact that the network‐based businesses studied have a different set of stakeholder tensions than more traditionally organized.

This paper demonstrates the advantages of network‐based value configurations and indicates how performance measures may be directly derived from value creation maps.

This paper contributes to understanding advantages and drawbacks of network‐based companies and how such types of organization challenge the people within and outside of them.

This bibliography is offered as a practical guide to published papers, conference proceedings papers and theses/dissertations on the finite element (FE) and boundary element (BE) applications in different fields of biomechanics between 1976 and 1991. The aim of this paper is to help the users of FE and BE techniques to get better value from a large collection of papers on the subjects. Categories in biomechanics included in this survey are: orthopaedic mechanics, dental mechanics, cardiovascular mechanics, soft tissue mechanics, biological flow, impact injury, and other fields of applications. More than 900 references are listed.

The purpose of this paper is to focus on one of the major emerging Asian economies – India – to examine the role of human capital in asset prices.

The analysis uses various statistical techniques (e.g. multifactor regression model, 3D graphs, GRS test and residual graphs) to test the role of human capital in asset prices.

A six-factor model designed for capturing the size, value, profitability, investment and human capital patterns in average portfolio returns performs better than both Fama–French’s (1993) three- and Fama–French’s (2015) five-factor model. The main problem of six-factor model is its failure in capturing the average returns on “microcap with low-value stocks that are highly profitable invests aggressively for asset growth but invests much lesser for human growth” and “microcap with unprofitable stocks whose returns behave like those of low-value firms with conservative investment”. The study finds the investment factor (CMA) of Fama–French’s (2015) five-factor model as the redundant factor for describing the portfolio average returns in the study sample.

The paper argues that human capital also plays a role in predicting returns. This has significant public policy content.

The present study is novel for several reasons: first, it includes six-factor model descriptions; second, no comprehensive asset pricing study is done with human capital in Asian emerging markets, especially in India. Perhaps, this is the first study to examine whether portfolio returns are affected by the human capital in the Indian context. Third, the study period and methodology used are completely different from the previous studies.