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This study purposes to measure the influencing relations between macroeconomic indicators and the prices of gold. Further study measures several factors with the gold price in the context of the United States.

The secondary data are collected to measure relationship and fluctuation of gold prices the data collected from the website world development indicators (WDI) for the period of 31 years 1990–2019. This paper uses different econometric analysis such as analytical unit root test for stationary of data, descriptive statistical analysis for description of data, correlation coefficient test for measuring the inter correlation, and ordinary least square regression analysis for determine the impact of dependent and independents variables. In this research paper, gross domestic product (GDP), inflation rate (IR), unemployment rate (UR), real interest rate (RIR), gross national product (GNP), standard trade value (STV) are included in macroeconomic indicators and consider as independent. The gold prices are considered as dependent variable.

This study's overall results show an important and optimistic association between GDP, IR and STV with the gold price. Moreover, the RIR shows negative and does not show significant relation with the gold prices.

Since several economic crises were included during the data selection studied in this research paper, data error may be present, resulting in the instability of the overall data. However, the study still hopes to find the guiding role of these macro gold price factors in the price of gold from the limited data set. The basic scope of research is that research is limited in the United States.

The research aims to explore the influence mechanism of peer feedback and users' knowledge contribution behavior. This study draws on the social identity theory and considers social identity as a mediating factor into the research framework.

This paper collected users' activity data of 142,191 ideas submitted by 76,647 users from the MIUI community between October 2010 and May 2018 via Python software, and data were processed using Stata 16.0.

The results indicate that knowledge feedback and social feedback positively influence users' knowledge contribution (quantity and quality), respectively. User's cognitive identity positively mediates the relationship between peer feedback and knowledge contribution behavior, affective identity positively mediates the relationship between peer feedback and knowledge contribution behavior, while evaluative identity positively mediates the relationship between peer feedback and knowledge contribution quality, but there is no mediating effect between peer feedback and knowledge contribution quantity.

This study advances knowledge management by highlighting peer feedback on online innovation communities. By demonstrating the significant mediating effect of social identity, this study empirically clarifies the relationships of peer feedback (knowledge feedback and social feedback) to specific dimensions of knowledge contribution, thereby providing managerial guidance to the online innovation community on incentivizing and managing user interaction to foster the innovation development of firms.

Online brand communities (OBCs) are important platforms to obtain consumers' ideas. The purpose of this study is to examine how peer influence and consumer contribution behavior simulate innovative behaviors in OBCs to increase idea quality.

Using a firm-hosted popular online brand community – Xiaomi Community (MIUI), the authors collected a set of data from 6567 consumers and then used structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to empirically test the impact of peer influence and consumer contribution behaviors on idea quality in OBCs.

The results of this study show that both peer influence breadth and depth have a positive effect on idea adoption and peer recognition, wherein proactive contribution behavior positively mediates these relationships, and responsive contribution behavior negatively mediates the impact of peer influence breadth and peer influence depth on peer recognition. A more detailed analysis using the fsQCA method further identifies four types of antecedent configurations for better idea quality.

Based on the attention-based view and the theory of learning by feedback, this study explores the factors that affect idea quality in the context of social networks and extends the research of peer influence in the digital age. The paper helps improve our understanding of how to promote customer idea quality in OBCs.

Estimating product cost at the design stage, often referred to as product costing, is vitally important in the product configuration process in the mass customization paradigm. There are three research streams in this field: analogous, statistical, and analytical methods. However, in a mass customization environment, these methods suffer the drawbacks of poor accuracy, low‐agility and an undesirable degree of detail. It is imperative to develop an efficient framework for product costing in mass customization. The paper aims to address these issues.

Activity‐based costing (ABC) becomes a new trend in product costing which alleviates the shortcomings discussed above. However, the complexity of ABC probably increases in mass customization with a high degree of variety. In this research, a framework is formalized for product costing in mass customization, in which a generic activity definition is proposed to simplify and catalyze ABC practice in high‐variety production. Moreover, an activity dictionary with a hierarchical structure is developed to bring commonality and modularity to the storage and retrieval of activity data.

A generic activity‐dictionary‐based method for product costing in mass customization is developed.

The method proposed for product costing provides valuable cost data for product specifications, as well as the activities to create them. Therefore, a product configuration solution can be financially evaluated and optimized.

Based on the methodology proposed, a prototype system has been developed and implemented in a computer‐assembling company.

This paper alleviates the negative impact of activity variety on product costing.

Making decisions on production and maintenance separately, as is often done in practice and research literature, may not result in overall optimization. This paper aims to propose a joint method that better integrates production planning and maintenance at the tactical level. The potential of improving the performance of the classic planning method is also explored.

An integrated production planning and maintenance model is proposed. The production capacity losses resulted from both preventive and corrective maintenance activities are considered. Meanwhile, the reliability deterioration of the machine is considered to be operation dependent. An iterative approach is presented to find a solution for the nonlinear model through iteratively solving a sequence of mixed integer linear programming instances, accompanied by modification of some parameters prior to each iteration. Computational experiments are conducted to evaluate the performance of the proposed method compared with three other methods, including two methods based on separate planning and one integrated model.

The superiority of the proposed method compared with all the other three methods is demonstrated. Thus, the values of both integrated planning and considering operation-dependent failures are testified. The advantage of the proposed method is highlighted in the cases of high capacity utilization, long maintenance durations and low maintenance costs. The performance of the two methods based on separate planning is sensitive to the system utilization, and when utilization is high, the one with an availability-sensitive objective function defined for the maintenance problem performs better.

Few studies have been carried out to integrate decisions on production lot and maintenance. Their considerations are either incomplete or not realistic enough. A more comprehensive and realistic integrated model is proposed in this paper, along with an iterative solution algorithm for it. A potential way to improve the performance of the classic planning method with its simplicity preserved is also presented.

This paper aims to propose a novel method to predict and alleviate feature fatigue. Many products now are loaded with an extensive number of features. Adding more features to one product generally makes the product more attractive on the one hand but, on the other hand, may result in increasing difficulty to use the product. This phenomenon is called “feature fatigue”, which will lead to dissatisfaction and negative word-of-mouth (WOM). Feature fatigue will damage the brand’s long-term profit, and ultimately decrease the manufacturer’s customer equity. Thus, a problem of balancing the benefit of increasing “attractiveness” with the cost of decreasing “usability” exists.

A novel method based on the Bass model is proposed to predict and alleviate feature fatigue. Product capability, usability and WOM effects are integrated into the Bass model to predict the impacts of adding features on customer equity in product development, thus helping designers alleviate feature fatigue. A case study of mobile phone development based on survey data is presented to illustrate and validate the proposed method.

The results of the case study demonstrate that adding more features indeed increases initial sales; however, adding too many features ultimately decreases customer equity due to usability problems. There is an optimal feature combination a product should include to balance product capability with usability. The proposed method makes a trade-off between initial sales and long-term profits to maximize customer equity.

The proposed method can help designers predict the impacts of adding features on customer equity in the early stages of product development. It can provide decision supports for designers to decide what features should be added to maximize customer equity, thus alleviating feature fatigue.

Building on social media and destination brand-related literature, this study aims to explore World Heritage Sites’ (WHSs) brand diffusion and formation process from long-term and short-term perspectives, which includes brand diffusion, user-generated content (UGC), opinion leaders and brand events’ impact.

This study uses a mixed-method including text mining, keyword analysis and social network analysis to explore the brand formation process of four popular WHSs in Beijing, namely, the Palace Museum, Great Wall, Summer Palace and Temple of Heaven and more than 10,000,000 users’ data on Sina Weibo has been implemented to uncover the underlying social media branding mechanism.

The results show that the number of postings keeps in a stable range in most months, but, in general, there are no common rules for changing trends among the four WHSs; long-term high-frequency keywords related to history and culture account for a higher percentage; different kinds of accounts have varying impacts on information diffusion, in which media accounts lead to a bigger influence. However, more followers do not necessarily mean more interactions and most of the interaction ratio is much lower than 0.01000; brand events facilitate brand dissemination and have an impact on the creation of UGC.

This study is valuable for destination marketers to deeper understand brand diffusion and formation and provides valuable insights for developing effective destination marketing strategies.

Unlike previous studies that only concern a few parts of destination brand formation via social media (e.g. brand diffusion, brand events or opinion leaders’ impact), this study takes a more comprehensive perspective by systematically analyzing the brand formation process of WHSs on social media. By considering both long-term diffusion and short-term representative events, this study provides a more holistic understanding of the branding mechanism.

This study aims to identify the bioactive compounds in the Onion (Allium burdickii [A.B]) bulb using Raman and Fourier transform infrared spectroscopy (FTIR) spectroscopy. It assessed the extraction conditions of bioactive compounds from A.B. while evaluating the best extraction conditions.

The research opted for an experimental qualitative approach. It examined the extraction conditions of A.B., namely, temperature (°C), time (min) and mass-to-liquor ratio (M:R) using ultraviolet-visible spectrophotometry. Identification of bioactive compounds present in the dye was performed using Raman spectroscopy and the validation of the results was done by FTIR spectroscopy.

The study determined the best extraction conditions (time, temperature, M:R) for A.B bulb. The study confirmed the presence of bioactive compounds.

The limitation was quantification of bioactive compounds in A.B bulb.

The findings prove that the A.B. bulb can provide a sustainable source of bioactive compounds (functionalized compounds). The study provides suitable extraction conditions for A.B. and further elaborates on the techniques for identifying bioactive compounds in A.B. bulb extracts.

The study provides A.B. as a source of bioactive compounds and a clean dye for textile coloration.

To the best of the authors’ knowledge, there is no documented study on the qualitative analysis of bioactive compounds in A.B using Raman and FTIR. Therefore, the study fulfils the identified need to ascertain alternative procedures for the analysis of bioactive compounds.

Recent research has demonstrated an increasing awareness among business communities about the importance of environmental concerns. Green human resource management (GHRM) has become a crucial business strategy for organizations because the human resource department can play a key role in going “green.” This study tests an integrative model incorporating the indirect effects of GHRM practices on employee organizational citizenship behavior toward environment (OCBE), through green employee empowerment. Moreover, this study investigates the moderating effect of individual green values on OCBE.

Using a paper–pencil survey, we collected multisource data from 365 employees and their immediate supervisors from Pakistan.

The results of structural regression revealed that GHRM has a significant indirect effect on OCBE through green employee empowerment. The results also indicated that individual green values moderated the positive relationship between green employee empowerment and OCBE.

Organizations should appropriately appraise workers’ green behavior and align their behavior to pay and promotion. Organizations should also encourage and motivate employees to be engaged in green activities and contribute to environmental management.

This study suggests that green employee empowerment and individual green values are important factors that influence the relationship between GHRM and employees' OCBE, and it empirically analyzes these proposed relationships in a developing country context.

The purpose of this paper is to present a detection method based on computer vision for automatic flexible printed circuit (FPC) defect detection.

This paper proposes a new method of watershed segmentation based on morphology. A dimensional increment matrix calculation method and an image segmentation method combined with a fuzzy clustering algorithm are provided. The visibility of the segmented image and the segmentation accuracy of a defective image are guaranteed.

Compared with the traditional one, the segmentation result obtained in this study is superior in aspects of noise control and defect segmentation. It completely proves that the segmentation method proposed in this study is better matches the requirements of FPC defect extraction and can more effectively provide the segmentation result. Compared with traditional human operators, this system ensures greater accuracy and more objective detection results.

The extraction of FPC defect characteristics contains some obvious characteristics as well as many implied characteristics. These characteristics can be extracted through specific space conversion and arithmetical operation. Therefore, more images are required for analysis and foresight to establish a more widely used FPC defect detection sorting algorithm.

This paper proposes a new method of watershed segmentation based on morphology. It combines a traditional edge detection algorithm and mathematical morphology. The FPC surface defect detection system can meet the requirements of online detection through constant design and improvement. Therefore, human operators will be replaced by machine vision, which can preferably reduce the production costs and improve the efficiency of FPC production.