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Social media influencers (SMIs) have become vital components of interactive marketing to promote beauty-endorsed products. However, there are three major research gaps in the literature on influencer marketing. This research aimed to fill these gaps by integrating the stimulus-organism-response (SOR) model with theories of source credibility and parasocial interaction (PSI).

A qualitative method was used to gain a deep understanding of the Korean beauty field in YouTube influencer marketing.

The data showed that YouTube influencer marketing is an ongoing PSI process that conforms to the extended SOR model. This model was based on four concepts: stimulus, which was the SMIs’ source credibility; organism, which was the followers’ perceptions of homophily/relevance to the SMIs in PSIs; response, which was the desire to imitate and engage in impulse buying behavior, and management, which was the parasocial trust friendship and/or relationship.

This study expands the new concept into the existing SOR model and releases an insight into ongoing PSIs in YouTube influencer marketing.

Integrating project scheduling and material ordering problems is vital in realistically estimating project cost and duration. Also, the quality level of materials is important as one of the key project success factors.

In this paper, a three-objective mathematical model is presented for green project scheduling with materials ordering problems considering rental resources. The first objective is to minimize the total cost of the project site and logistics. The second objective is to minimize the environmental impacts of producing materials and the third objective is to maximize the total quality of materials. Since costs trigger several challenges in projects, cost constraints are considered in this model for the first time and also the cost of delay in supplying of materials by the suppliers has been deducted from the project costs. Subsequently, the model was implemented in a real case and solved by the Lagrangian Relaxation algorithm as an exact method on GAMS software for model validation.

Based on sensitivity analysis of some parameters, the findings indicate that the cost constraint and lead time have considerable effects on the project duration. Also, integrating project scheduling and material ordering improves the robustness of the project schedule.

The primary contributions of the present research can be stated as follows: considering the cost constraints in the project scheduling with material ordering problem, incorporating the rental resources and taking the quality levels of materials as well as the environmental impacts into account.

This study aims to present a new rapid enhancement digital elevation model (DEM) framework using Google Earth Engine (GEE), machine learning, weighted interpolation and spatial interpolation techniques with ground control points (GCPs), where high-resolution DEMs are crucial spatial data that find extensive use in many analyses and applications.

First, rapid-DEM imports Shuttle Radar Topography Mission (SRTM) data and Sentinel-2 multispectral imagery from a user-defined time and area of interest into GEE. Second, SRTM with the feature attributes from Sentinel-2 multispectral imagery is generated and used as input data in support vector machine classification algorithm. Third, the inverse probability weighted interpolation (IPWI) approach uses 12 fixed GCPs as additional input data to assign the probability to each pixel of the image and generate corrected SRTM elevations. Fourth, gridding the enhanced DEM consists of regular points (E, N and H), and the contour interval is 5 m. Finally, densification of enhanced DEM data with GCPs is obtained using global positioning system technique through spatial interpolations such as Kriging, inverse distance weighted, modified Shepard’s method and triangulation with linear interpolation techniques.

The results were compared to a 1-m vertically accurate reference DEM (RD) obtained by image matching with Worldview-1 stereo satellite images. The results of this study demonstrated that the root mean square error (RMSE) of the original SRTM DEM was 5.95 m. On the other hand, the RMSE of the estimated elevations by the IPWI approach has been improved to 2.01 m, and the generated DEM by Kriging technique was 1.85 m, with a reduction of 68.91%.

A comparison with the RD demonstrates significant SRTM improvements. The suggested method clearly reduces the elevation error of the original SRTM DEM.

The study aimed to test the validity and reliability of the Polish version of the identity leadership inventory (ILI) proposed by Steffens, Haslam, Reicher et al. (2014) and to confirm the relationship between identity leadership and various job-related outcomes (i.e., trust in leaders, job satisfaction, work engagement and turnover intentions) among employees from Poland-based organizations. Identity leadership appears to be a universal construct (van Dick, Ciampa, & Liang, 2018) but no one has studied it in Poland so far.

The sample consisted of 1078 employees collected in two independent subsamples from different organizations located in Northern and Central Poland. We evaluated the ILI’s factorial structure using confirmatory factor analysis.

The results confirm that the 15-item Polish version of the ILI has a four-dimensional structure with factors representing prototypicality, advancement, entrepreneurship and impresarioship. It showed satisfactory reliability. The identity leadership inventory-short form (four items) also showed a good fit with the data. As expected, the relationships between identity leadership and important work-related outcomes (general level of job satisfaction, work engagement, trust toward the leader and turnover intentions) were also significant.

Despite the cultural specifics of Polish organizations, the research results were generally very similar to those in other countries, confirming the universality of the ILI as shown in the Global Identity Leadership Development project (GILD, see van Dick, Ciampa, & Liang, 2018; van Dick et al., 2021).

Although businesses increasingly use augmented reality (AR) to enhance service experiences, the way AR service marketing inspires consumers remains underexplored. Drawing on the consumer inspiration literature, the authors examine how AR service marketing activities such as entertainment, interaction, trendiness and customization enhance consumer inspiration. In addition, the authors explore the role of consumer empowerment and skepticism as key underlying mechanisms between consumer inspiration and value co-creation (VCC) or co-destruction (VCD) intentions.

The study used a mixed method, explanatory sequential design to gain a more comprehensive understanding of their proposed theoretical framework. The quantitative survey study involved 344 AR app users, followed by a qualitative open-ended essay study with 34 AR app users.

Results suggest that AR service marketing activities positively influence consumer inspiration, which in turn increases consumer empowerment and reduces skepticism. The authors also found that consumer empowerment leads to VCC, while skepticism leads to VCD. These findings provide valuable insights for practitioners seeking to implement AR service marketing activities effectively to inspire consumers, foster value creation and manage value destruction.

The study highlights inspiration as a key factor in motivating consumers to co-create value, transcending typical service experiences and limitations. Empowered consumers, feeling inspired, are more inclined to contribute effectively to VCC, also fostering trust in the service provider. AR serves not just as a sales channel, but also as a tool for relationship-building and brand retention. Managers should leverage AR to elicit feelings of trendiness, customization and interaction, fostering empowerment and inspiring consumers to co-create value.

This study significantly contributes to the growing body of literature on consumer inspiration and AR service marketing. It emphasizes the need to consider external (i.e. marketing-induced) stimuli in understanding the sources and consequences of consumer inspiration through AR.