Classical Item Research Articles

Bringing Self-Determination Theory to the Forefront: Examining How Human Resource Practices Motivate Employees of All Ages to Succeed

AbstractSelf-determination theory (SDT) is widely used in human resource (HR) practice studies to explain how HR practices impact work outcomes. However, there is little empirical evidence establishing the complete SDT mediational process in the HR domain, which entails basic psychological needs and motivation acting as mediators of relations between HR practices and work outcomes. The present study addresses this issue with a mixed methods approach. Using an item classification task with subject matter experts (N = 48), HR practices were classified as autonomy-, competence-, and relatedness-supportive. Based on this scheme, we tested an SDT-based conceptual model in a three-wave sample of working adults (N = 818). Results from Multilevel Structural Equation Modeling (MSEM) support the hypothesized model: basic psychological need satisfaction and autonomous motivation mediate the relationship between HR practice perceptions and work outcomes (performance, thriving, affective organizational commitment, and turnover intentions). Autonomy-supportive practices had a positive effect on autonomy need satisfaction, while competence-supportive practices had positive associations with all three basic psychological needs. Counterintuitively, relatedness-supportive practices had negative effects on autonomy and competence need satisfaction, and the relationship between relatedness-supportive practices and autonomy need satisfaction was moderated by chronological age. We discuss our findings in the context of prominent HR studies that utilize SDT and offer a supplemental age-inclusive HR practice scheme for HR managers interested in understanding the benefits of supporting basic psychological needs from both a motivation and workforce retention standpoint.

A Study Seeking Historical Evidence for Bases of Classified Balance Sheets

This paper explores the historical evolution of balance sheet layout and the reasons behind the positioning of assets on the left or right side. It delves into two main questions: why the discrepancy in asset placement on balance sheets across countries, and why the shift from right to left in the UK during the 19th century. Using historical evidence spanning from the 15th century to the mid-20th century, the study investigates the development and changes in listing assets, liabilities, and stockholders' equity. It aims to uncover the factors influencing the classification and presentation of items on balance sheets, driven by the needs of information users over time. By examining secondary sources in English literature, the paper provides insights into the evolution of balance sheet layouts globally. Researchers may find this exploration of historical balance sheet variations intriguing, shedding light on how layout decisions have been shaped by user needs and expectations.

Understanding Perspectives on Presbyopia and Use of Pilocarpine HCl 1.25% Twice Daily from Participants of the Phase 3 VIRGO Study.

The phase 3, randomized, vehicle-controlled, 14-day VIRGO study evaluated the efficacy and safety of twice-daily dosing of pilocarpine hydrochloride ophthalmic solution 1.25% (Pilo) in presbyopia. On VIRGO exit, a companion study was conducted to assess the patient experience with presbyopia and satisfaction with Pilo. Recruited individuals completed the Presbyopia Patient Satisfaction Questionnaire (PPSQ) plus a three-part exit survey, or a live interview. The PPSQ evaluated respondents' experience with Pilo. Survey parts 1 and 2 evaluated experience managing presbyopia before and during VIRGO, respectively; part 3 assessed future possibilities of using Pilo in real-world situations. The interview further informed the interviewees' experience with presbyopia and Pilo. The primary endpoint was responders (%) in each rating category of the PPSQ items 1-7; the secondary endpoints were summary of categorical (survey) and qualitative (interviews) responses. The PPSQ and survey included 62 participants who received Pilo (N = 28) or vehicle (N = 34) in VIRGO; the interview included ten participants (Pilo, N = 4; vehicle, N = 6). Per the PPSQ, 64.3% of Pilo users reported vision improvement, including 17.9% with complete improvement; ≥ 46.4% were satisfied/very satisfied with their ability to perform daily activities, see up close unaided, and read in dim light. Among vehicle users, these percentages were 35.3%, 0%, and ≤ 23.5%, respectively. In both subgroups, ≥ 67.9% were interested in using Pilo or Pilo and eyeglasses/contact lenses in the future. Per the interview, vehicle users (n = 6/6) found the eyedrop easy to use but none experienced meaningful near-vision improvements, stopped using other correction method(s) part of the day, were satisfied with the eyedrop, preferred it over their previous correction method(s), or would continue using it if prescribed. Conversely, 75% (n = 3/4) of Pilo users responded positively to each of these six criteria. Findings validate the VIRGO results and improve our understanding of the patient experience, demonstrating improved vision and satisfaction with Pilo (vs. vehicle) when performing daily activities.

A Novel Machine Learning Algorithm for Creating Risk-Adjusted Payment Formulas

Models predicting health care spending and other outcomes from administrative records are widely used to manage and pay for health care, despite well-documented deficiencies. New methods are needed that can incorporate more than 70 000 diagnoses without creating undesirable coding incentives. To develop a machine learning (ML) algorithm, building on Diagnostic Item (DXI) categories and Diagnostic Cost Group (DCG) methods, that automates development of clinically credible and transparent predictive models for policymakers and clinicians. DXIs were organized into disease hierarchies and assigned an Appropriateness to Include (ATI) score to reflect vagueness and gameability concerns. A novel automated DCG algorithm iteratively assigned DXIs in 1 or more disease hierarchies to DCGs, identifying sets of DXIs with the largest regression coefficient as dominant; presence of a previously identified dominating DXI removed lower-ranked ones before the next iteration. The Merative MarketScan Commercial Claims and Encounters Database for commercial health insurance enrollees 64 years and younger was used. Data from January 2016 through December 2018 were randomly split 90% to 10% for model development and validation, respectively. Deidentified claims and enrollment data were delivered by Merative the following November in each calendar year and analyzed from November 2020 to January 2024. Concurrent top-coded total health care cost. Model performance was assessed using validation sample weighted least-squares regression, mean absolute errors, and mean errors for rare and common diagnoses. This study included 35 245 586 commercial health insurance enrollees 64 years and younger (65 901 460 person-years) and relied on 19 clinicians who provided reviews in the base model. The algorithm implemented 218 clinician-specified hierarchies compared with the US Department of Health and Human Services (HHS) hierarchical condition category (HCC) model's 64 hierarchies. The base model that dropped vague and gameable DXIs reduced the number of parameters by 80% (1624 of 3150), achieved an R2 of 0.535, and kept mean predicted spending within 12% ($3843 of $31 313) of actual spending for the 3% of people with rare diseases. In contrast, the HHS HCC model had an R2 of 0.428 and underpaid this group by 33% ($10 354 of $31 313). In this study, by automating DXI clustering within clinically specified hierarchies, this algorithm built clinically interpretable risk models in large datasets while addressing diagnostic vagueness and gameability concerns.

Triplet attention-based deep learning model for hierarchical image classification of household items for robotic applications

Triplet attention-based deep learning model for hierarchical image classification of household items for robotic applications

Classification and presentation of equity in IFRS financial statements

Subject. This article examines the methodological basis for the classification and presentation of an organization's equity in financial statements prepared in accordance with IFRS. Objectives. The article aims to substantiate the approach to the classification and presentation of information on the equity of an organization, ensuring its relevance and faithfulness. Methods. For the study, we used a logical analysis. Results. Based on the analysis, the article confirms the prospects for the combined use of the two features of the classification of capital items proposed in the Exposure Draft IASB/ED/2023/5 by the International Accounting Standards Board (IASB). Conclusions. As innovative financial instruments emerge, new problems will arise with the classification of financial instruments that have characteristics of both capital and liabilities. In this regard, it is necessary to continue research in the direction of the evolution of approaches to the classification of capital.

Validation of the shortened version of the Canine Behavioral Assessment and Research Questionnaire (C-BARQ) using participants from the Dog Aging Project.

The Canine Behavioral Assessment and Research Questionnaire (C-BARQ) is a 100-item owner-completed survey instrument used for assessing behavior and temperament of companion dogs. The shortened version of the C-BARQ (C-BARQ(S)) consists of 42 items of the long C-BARQ. We aimed to validate the shortened C-BARQ(S) by comparing it with the long questionnaire in the same human-dog pair. We examined data from a nationwide cohort of companion dogs enrolled in the large-scale longitudinal Dog Aging Project (DAP) study. Among 435 participating owners who completed both the long and shortened versions of the C-BARQ within 60 days of each other, agreement between individual questions of the long and shortened C-BARQ using an unweighted kappa statistic and percent agreement was examined. Associations between the two questionnaires for mean behavior and temperament domain scores and mean miscellaneous category scores were assessed using Pearson correlation coefficients. Of 435 dogs in the study, the mean (SD) age was 7.3 (4.3) years and 216 (50%) were female. Kappa values between the long and shortened C-BARQ for individual questions within the 14 behavior and temperament domains and a miscellaneous category ranged from fair to moderate (0.23 to 0.40 for 21 items and 0.41 to 0.58 for 26 items, respectively). Pearson correlation coefficients above 0.60 between both questionnaires for 12 of the 14 mean behavior and temperament domain scores and a category of miscellaneous items were observed. Kappa values for individual questions between the long and shortened C-BARQ ranged from fair to moderate and correlations between mean domain scores ranged from moderate to strong.

Heterogeneous Adaptive Preference Learning for Recommendation

Graph-based collaborative filtering techniques have emerged as a promising recommendation approach by modeling user-item interaction as graphs. Recently, contrastive learning has been employed in graph collaborative through data augmentation, which can effectively offer data efficiency and reduce labeling costs. Nonetheless, most existing contrastive learning approaches overlook the heterogeneous auxiliary information pertaining to users and items, such as user social relationships and item categories, which are crucial to alleviate the data sparsity issue. In this paper, we propose a novel contrastive learning method, referred to as Heterogeneous Adaptive Preference Learning for Recommendation (HAPLRec), which explicitly incorporates fine-grained preference information from both users and items. Specifically, we construct user relationship graphs and item relationship graphs based on specific meta-paths in a heterogeneous graph. Subsequently, we conduct data augmentation on these graphs individually to obtain auxiliary contrastive tasks. Moreover, we introduce an optimization algorithm that leverages the gradient similarity between the main task and the auxiliary tasks, dynamically adjusting the weight assigned to each task to expedite achieving superior performance within a shorter time frame. The effectiveness of the proposed model is demonstrated through extensive experiments conducted on three publicly available datasets.

AI-Driven Precision Clothing Classification: Revolutionizing Online Fashion Retailing with Hybrid Two-Objective Learning

In the ever-expanding online fashion market, businesses in the clothing sales sector are presented with substantial growth opportunities. To utilize this potential, it is crucial to implement effective methods for accurately identifying clothing items. This entails a deep understanding of customer preferences, niche markets, tailored sales strategies, and an improved user experience. Artificial intelligence (AI) systems that can recognize and categorize clothing items play a crucial role in achieving these objectives, empowering businesses to boost sales and gain valuable customer insights. However, the challenge lies in accurately classifying diverse attire items in a rapidly evolving fashion landscape. Variations in styles, colors, and patterns make it difficult to consistently categorize clothing. Additionally, the quality of images provided by users varies widely, and background clutter can further complicate the task of accurate classification. Existing systems may struggle to provide the level of accuracy needed to meet customer expectations. To address these challenges, a meticulous dataset preparation process is essential. This includes careful data organization, the application of background removal techniques such as the GrabCut Algorithm, and resizing images for uniformity. The proposed solution involves a hybrid approach, combining the strengths of the ResNet152 and EfficientNetB7 architectures. This fusion of techniques aims to create a classification system capable of reliably distinguishing between various clothing items. The key innovation in this study is the development of a Two-Objective Learning model that leverages the capabilities of both ResNet152 and EfficientNetB7 architectures. This fusion approach enhances the accuracy of clothing item classification. The meticulously prepared dataset serves as the foundation for this model, ensuring that it can handle diverse clothing items effectively. The proposed methodology promises a novel approach to image identification and feature extraction, leading to impressive classification accuracy of 94%, coupled with stability and robustness.

Microplastics in personal care products and cosmetics in Sri Lanka

In the Sri Lankan context, the lack of baseline studies to mitigate microplastic emissions through personal care and cosmetic products poses a huge problem. Hence this study serves as the first scientific investigation to analyze and characterize microplastics in selected personal care and cosmetic items available in the Sri Lankan markets. Fifteen brands representing five categories (face wash, facial scrubs, baby creams, shaving creams, and skin creams) of personal care and cosmetic items served as the basis for this investigation. Based on a questionnaire survey, from each category, three highly utilized brands were chosen and triplicates from each brand were used for the analysis. All samples were treated with the Fenton reagent to extract microplastics. Then through Nile red staining suspected microplastic were screened and characterized through FT-IR spectroscopy. The Nile Red analysis revealed seven brands of the fifteen to be stained with Nile Red and demonstrate luminance properties under UV light. However, FT-IR analysis proved only six brands contained actual microplastics. Low-density polyethylene and ethylene-propylene copolymer were the dominant types of microplastic. Most microplastics were irregularly shaped and white in color with sizes ranging from 238.55 ± 50.74 to 450.69 ± 174.9 μm. An emission estimation revealed that products FS-01 and FW-03 contain 3.36 ± 0.20 g and 0.2 ± 0.05 g of isolatable microplastics per product. While the present study provides scientific evidence for the availability of microplastics in products in Sri Lankan markets, it also provides a great opportunity to develop relevant policies and regulations to control them.

Novel item selection strategies for cognitive diagnostic computerized adaptive testing: A heuristic search framework.

The computerized adaptive form of cognitive diagnostic testing, CD-CAT, has gained increasing attention in the domain of personalized measurements for its ability to categorize individual mastery status of fine-grained attributes more accurately and efficiently through administering items tailored to one's ability progressively. How to select the next item based on previous response(s) is crucial for the success of CD-CAT. Previous item selection strategies for CD-CAT have often followed a greedy or semi-greedy approach, which makes it difficult to strike a balance between diagnostic performance and item bank utilization. To address this issue, this study takes a graph perspective and transforms the item selection problem in CD-CAT into a path-searching problem, in which paths refer to possible test construction and nodes refer to individual items. A heuristic function is defined to predict the prospect of a path, indicating how well the corresponding test can diagnose the current examinee. Two search mechanisms with different biases towards item exposure control are proposed to approximate the optimal path with the best prospect. The first unused item on the resulting path is selected as the next item. The above components compose a novel CD-CAT item selection framework based on heuristic search. Simulation studies are conducted under a variety of conditions regarding bank designs, bank-quality conditions, and testing scenarios. The results are compared with different types of classic item selection strategies in CD-CAT, showing that the proposed framework can enhance bank utilization at a smaller cost of diagnostic performance.

Validity and reliability of concept inventory test in human physiology

Biology education plays a vital role in nurturing the understanding of learners about the intricacy of life. Various efforts have emerged to strengthen learning biological concepts but there were still studies that showed that learners have low mastery in some aspects. To determine how well students understood various biological topics, including human physiology, Concept inventory tests (CIT) were used. The concept inventory test may be able to spot students' misconceptions and ultimately lead to improved comprehension. The crafted CIT developed with the aid of a table of specifications based on Bloom’s taxonomy of cognitive domain was assessed according to its validity and reliability. In validation, content validity and item analysis were considered while reliability test was employed through Cronbach’s alpha. Distractor analysis was also performed to determine possible source of misconception per item. The CIT was administered to 120 senior high school STEM students (50.8% from the private schools, 37.5% in regular public schools and 11.7% from public schools with special programs in science). The results displayed high content validity with a mean of 4.83 for content validity and an average Aiken’s validity coefficient of 0.98. It also highlighted that the test is moderately difficult with the test difficulty of 0.58, as well as, discriminatory with a discriminating level of 0.46. After item classification, 63 items were retained (39 accepted, 24 for revisions) and Chronbach’s alpha (α=0.74) indicated good internal consistency. The concept inventory test propounds to be a good classroom test with minor items to be revised.

다차원 IRT 모형 적합 혼합형 검사를 위한 공통문항 척도 연계 방법

Under the context of the common-item nonequivalent groups design, item response theory (IRT) scale
 linking methods have been presented for each of the multidimensional extensions of the three-parameter
 logistic (M3PL), graded response (MGR), generalized partial credit (MGPC), and nominal response (MNR)
 models. The common-item multidimensional IRT (MIRT) linking methods that have been proved effective
 for single-format tests include the direct least squares (DLS), item category response function (ICRF), and
 test response function (TRF) methods. This paper extends the three MIRT linking methods to any mixture
 of the exploratory M3PL, MGR, MGPC, and MNR models for mixed-format tests. Each linking method
 extended attempts to find the linking coefficients in the rotation matrix and translation vector that
 constitute a linear transformation between two multidimensional ability scales. Simulations are conducted to
 investigate the performance of the three linking methods under various linking conditions. The results
 indicated that overall, the ICRF method performed best in estimating the rotation matrix and translation
 vector of the linear transformation between the new and true simulation scales. In not a few conditions, the
 DLS method resulted in the second best linking solutions. The TRF method tended to estimate the rotation
 matrix well but the translation vector poorly and thus showed the worst performance among the linking
 methods in some conditions.

Distribution Matching for Multi-Task Learning of Classification Tasks: A Large-Scale Study on Faces & Beyond

Multi-Task Learning (MTL) is a framework, where multiple related tasks are learned jointly and benefit from a shared representation space, or parameter transfer. To provide sufficient learning support, modern MTL uses annotated data with full, or sufficiently large overlap across tasks, i.e., each input sample is annotated for all, or most of the tasks. However, collecting such annotations is prohibitive in many real applications, and cannot benefit from datasets available for individual tasks. In this work, we challenge this setup and show that MTL can be successful with classification tasks with little, or non-overlapping annotations, or when there is big discrepancy in the size of labeled data per task. We explore task-relatedness for co-annotation and co-training, and propose a novel approach, where knowledge exchange is enabled between the tasks via distribution matching. To demonstrate the general applicability of our method, we conducted diverse case studies in the domains of affective computing, face recognition, species recognition, and shopping item classification using nine datasets. Our large-scale study of affective tasks for basic expression recognition and facial action unit detection illustrates that our approach is network agnostic and brings large performance improvements compared to the state-of-the-art in both tasks and across all studied databases. In all case studies, we show that co-training via task-relatedness is advantageous and prevents negative transfer (which occurs when MT model's performance is worse than that of at least one single-task model).

Automatic classification of products in P2P marketplaces

Product classification is a critical task for the smooth running of the purchase process in e-commerce websites. When it comes to P2P marketplaces, users can act both as sellers and as buyers, and they need to assign predefined categories to the products they want to sell. Besides being tedious for users, this task can result in ambiguous or inaccurate assignments. This article presents a method for the automatic categorization of items offered in a local P2P marketplace using a multi-level classification approach. Our experiments demonstrated a significant improvement in the classification results of the proposed solution compared to a traditional direct classification approach.

Integration of short and long-term interests: A preference aware session-based recommender

Session-based recommenders have gathered tremendous e-commerce and media streaming applications where the task is to predict the next item user would consume based on the session history. In this arena, advancements have been accomplished using deep learning techniques to model users’ long-term and short-term preferences in a session. The long-term module focuses on the entire item set in a session, while the short-term emphasizes a fixed set of items just before making a choice. While users’ short-term preferences in general reflect their immediate actions or intentions, consideration of such preferences, specifically with a fixed item set, makes the prediction task increasingly challenging. In this work, we attempt to capture the short-term behavior of users considering a dynamic window which self-tunes depending on the category of the last few items. A novel gating network that leverages the different drivers such as price, rating, dwell time, etc. and the latent representation of the items and their categories is introduced. The efficacy of our proposed method Gated Session-based Recommender integrating Short and Long-term preferences (GSRSL) has been evaluated against eight recent baselines on four publicly available real-world datasets, Tmall, Diginetica, and Cosmetics. Our findings demonstrate a significant improvement in MRR, Hit, and NDCG of up to 13.62%, 11.46%, and 13.00%, respectively, across all the datasets.

Overlap of International League of Associations for Rheumatology and Preliminary Pediatric Rheumatology International Trials Organization Classification Criteria for Nonsystemic Juvenile Idiopathic Arthritis in an Established UK Multicentre Inception Cohort.

The goal was to assess the degree of overlap between existing International League of Associations for Rheumatology (ILAR) and preliminary Paediatric Rheumatology International Trials Organisation (PRINTO) classification criteria for juvenile idiopathic arthritis (JIA). Participants from the Childhood Arthritis Prospective Study, a multicenter UK JIA inception cohort, were classified using the PRINTO and ILAR classification criteria into distinct categories. Systemic JIA was excluded because several classification items were not collected in this cohort. Adaptations to PRINTO criteria were required to apply to a UK health care setting, including limiting the number of blood biomarker tests required. The overlap between categories under the two systems was determined, and any differences in characteristics between groups were described. A total of 1,223 children and young people with a physician's diagnosis of JIA were included. Using PRINTO criteria, the majority of the patients had "other JIA" (69.5%). There was a high degree of overlap (91%) between the PRINTO enthesitis/spondylitis- and ILAR enthesitis-related JIA categories. The PRINTO rheumatoid factor (RF)-positive category was composed of 48% ILAR RF-positive polyarthritis and 52% undifferentiated JIA. The early-onset antinuclear antibodies-positive PRINTO category was largely composed of ILAR oligoarthritis (50%), RF-negative polyarthritis (24%), and undifferentiated JIA (23%). A few patients were unclassified under PRINTO (n=3) and would previously have been classified as enthesitis-related JIA (n=1) and undifferentiated JIA (n=2) under ILAR. Under the preliminary PRINTO classification criteria for childhood arthritis, most children are not yet classified into a named category. These data can help support further delineation of the PRINTO criteria to ensure homogenous groups of children can be identified.

Intersectional Stigma, Fear of Negative Evaluation, Depression, and ART Adherence Among Women Living with HIV Who Engage in Substance Use: A Latent Class Serial Mediation Analysis.

Women Living with HIV (WLHIV) who use substances face stigma related to HIV and substance use (SU). The relationship between the intersection of these stigmas and adherence to antiretroviral therapy (ART), as well as the underlying mechanisms, remains poorly understood. This study aimed to examine the association between intersectional HIV and SU stigma and ART adherence, while also exploring the potential role of depression and fear of negative evaluation (FNE) by other people in explaining this association. We analyzed data from 409 WLHIV collected between April 2016 and April 2017, Using Multidimensional Latent Class Item Response Theory analysis. We identified five subgroups (i.e., latent classes [C]) of WLHIV with different combinations of experienced SU and HIV stigma levels: (C1) low HIV and SU stigma; (C2) moderate SU stigma; (C3) higher HIV and lower SU stigma; (C4) moderate HIV and high SU stigma; and (C5) high HIV and moderate SU stigma. Medication adherence differed significantly among these classes. Women in the class with moderate HIV and high SU stigma had lower adherence than other classes. A serial mediation analysis suggested that FNEand depression symptoms are mechanisms that contribute to explaining the differences in ART adherence among WLHIV who experience different combinations of intersectional HIV and SU stigma. We suggest that FNE is a key intervention target to attenuate the effect of intersectional stigma on depression symptoms and ART adherence, and ultimately improve health outcomes among WLHIV.

Lead figurines of scythian horsemen from Olbia

In the article, the author aims to examine a specific category of lead artefacts discovered in Olbia, with a focus on investigating the technological features of their production and Olbia’s connections with its barbarian surroundings. The research employs an interdisciplinary approach utilising methods of analysis, synthesis, and socialisation, particularly emphasising the comparative-historical method. Against the backdrop of extensive studies on metalworking in Olbia, especially in foundries and blacksmithing, the processing of lead and the production of artefacts from it remain relatively under-explored. Therefore, this article delves into one category of items made of this metal. A distinct group of composite figurines and their fragments — statuettes of riders on stationary standing horses is explored in the article. Based on the details of their attire, they can be identified as Scythian horsemen, raising questions about Olbia’s connection with its barbarian environment. According to the classification of K. I. Zaitseva, in addition to riders on stationary standing horses, there is a small number of figurines depicting horsemen on galloping horses. All examined lead figurines are cast in the technique of either single- or double-sided relief, indicating their different usage. If double-sided statuettes could be used autonomously, single-sided ones may have needed to be attached to some surface. The presented depictions of Scythian riders not only provide insights into the production of various lead items in Olbia, but also raise questions about Greek-barbarian contacts, possibly indicating the residence of local inhabitants in Olbia and their status in both the city and barbarian interactions. They also expand our understanding of Scythian attire and horsemen. Lead items originating from Olbia can be categorised into several groups depending on their purpose. Primarily, these are household items (clamps, spindle whorls, fishing weights). During the Hellenistic period, handles (clamps) for wooden coffins (biers) were manufactured. However, the most interesting group comprises votive items, with around 350 known examples to date. These include bucrania, stylised sheep heads, and labryses. A smaller group consists of anthropomorphic figurines of gods, warriors, chariots, plaques with narrative scenes (sacrifices) and riders. Such votive figurines are found in mounds and tombs as burial offerings. Less frequently, they are found directly within burials. Additionally, they may have been used in religious rituals and in decorating household altars. The question of the ownership of such statuettes remains unanswered. However, Herodotus wrote about a barbarian population known as Callipidae or Hellenes-Scythians in the territory of the Olbian state. It is essential to note that in the 4th century BC, coins were minted in Olbia bearing the image of the Scythian king Ateas, and Scythian weaponry was commonly depicted on the Bosporus, indicating traditional Scythian-Hellenic relations that likely solidified after the Scythians defeated Zopyrion’s army under the walls of Olbia. Thus, the presence of residents in the city cannot be denied.

A multi-variable service supply management portfolio model for Telecom industry

Supply operations require some kind of classification according to the features of items. Based on the literature review and expert interview, a multi-variable service supply management portfolio model was developed to classify the procurable service items in the Telecom industry in a more realistic way with better precision. First, a multi-level relational reflective model was built based on four dimensions using the fuzzy Delphi method and the model was validated by factor analysis. Then, 50 service items were placed in 16 clusters of the portfolio model. For each category of services in the model, the pertinent supplier relationship management practices were proposed using the literature review accompanied by a focus group. The presented model contributed to the development of previous theoretical trends in supply management portfolio models by introducing a new dimension, called “pace of innovation in technology”, to those extracted from the literature review, such as “strategic importance of the items”, “supply risk”, and “extent of buyer captivity”. Despite the classical models targeting tangible goods and products, our model was customized for the service industry context and can be considered a starting point towards service supply market segmentation procedures according to the attributes of each service item category.