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This study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.

To collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.

The respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.

There has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.

The proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.

This research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.

Research on artificial intelligence (AI) and its potential effects on the workplace is increasing. How AI and the futures of work are framed in traditional media has been examined in prior studies, but current research has not gone far enough in examining how AI is framed on social media. This paper aims to fill this gap by examining how people frame the futures of work and intelligent machines when they post on social media.

We investigate public interpretations, assumptions and expectations, referring to framing expressed in social media conversations. We also coded the emotions and attitudes expressed in the text data. A corpus consisting of 998 unique Reddit post titles and their corresponding 16,611 comments was analyzed using computer-aided textual analysis comprising a BERTopic model and two BERT text classification models, one for emotion and the other for sentiment analysis, supported by human judgment.

Different interpretations, assumptions and expectations were found in the conversations. Three subframes were analyzed in detail under the overarching frame of the New World of Work: (1) general impacts of intelligent machines on society, (2) undertaking of tasks (augmentation and substitution) and (3) loss of jobs. The general attitude observed in conversations was slightly positive, and the most common emotion category was curiosity.

Findings from this research can uncover public needs and expectations regarding the future of work with intelligent machines. The findings may also help shape research directions about futures of work. Furthermore, firms, organizations or industries may employ framing methods to analyze customers’ or workers’ responses or even influence the responses. Another contribution of this work is the application of framing theory to interpreting how people conceptualize the future of work with intelligent machines.

The category captain system (CC) of retailer category management (RCM) is established, accepted, and widely adopted. The paper empirically assesses the application of this system in building collaborations between retailers and their suppliers to generate growth following COVID-19. This study applies service-dominant logic (S-D logic) to RCM and establishes the current ‘practical’ application of the five axioms of S-D logic within the CC system.

The researchers adopted a qualitative research design which examined both category managers and retail buyers currently involved in the CC system, using thematic analysis of transcripts from 25 practitioner participants.

The study reveals service is not a fundamental basis of exchange in the CC system. Value is uniquely, independently, and separately created by the retailer that significantly restricts the scope of the category service eco systems and the opportunity to innovate through value co-creation.

Significant change is required to realise value co-creation and innovation applying S-D logic to RCM. The study indicates there is potential to start this change by the formalisation of wider informal category relationships between non-captain suppliers and retailers through consumer insight technology, and by aligning suppliers and retailers to make more effective and sustainable trading decisions.

The study indicates that certain elements of the CC system proposed by the literature's games-based theoretic models, are not applied in practice. The lived experiences of practitioners suggest informal ways of by-passing the formal system using S-D logic.

This study aims to minimize the cost and time of blood delivery in the whole blood supply chain network (BSCN) in disaster conditions. In other words, integrating all strategic, tactical and operational decisions of three levels of blood collection, processing and distribution leads to satisfying the demand at the right time.

This paper proposes an integrated BSCN in disaster conditions to consider four categories of facilities, including temporary blood collection centers, field hospitals, main blood processing centers and medical centers, to optimize demand response time appropriately. The proposed model applies the location of all permanent and emergency facilities in three levels: blood collection, processing and distribution. Other essential decisions, including multipurpose facilities, emergency transportation, inventory and allocation, were also used in the model. The LP metric method is applied to solve the proposed bi-objective mathematical model for the BSCN.

The findings show that this model clarifies its efficiency in the total cost and blood delivery time reduction, which results in a low carbon transmission of the blood supply chain.

The researchers proposed an integrated BSCN in disaster conditions to minimize the cost and time of blood delivery. They considered multipurpose capabilities for facilities (e.g. field hospitals are responsible for the three purposes of blood collection, processing and distribution), and so locating permanent and emergency facilities at three levels of blood collection, processing and distribution, support facilities, emergency transportation and traffic on the route with pollution were used to present a new model.

This article explores whether six broad categories of activities undertaken by Canadian business scholars’ academics: publications record, citations record, teaching load, administrative load, consulting activities, and knowledge spillovers transfer, are complementary, substitute, or independent, as well as the conditions under which complementarities, substitution and independence among these activities are likely to occur.

A multivariate probit model is estimated to take into account that business scholars have to consider simultaneously whether or not to undertake many different academic activities. Metrics from Google Scholar of scholars from 35 Canadian business schools, augmented by a survey data on factors explaining the productivity and impact performances of these faculty members, are used to explain the heterogeneities between the determinants of these activities.

Overall, the results reveal that there are complementarities between publications and citations, publications and knowledge spillovers transfer, citations and consulting, and between consulting and knowledge spillovers transfer. The results also suggest that there are substitution effects between publications and teaching, publications and administrative load, citations and teaching load, and teaching load and administrative load. Moreover, results show that public and private funding, business schools’ reputation, scholar’s relational resources, and business school size are among the most influential variables on the scholar’s portfolio of activities.

This study considers simultaneously the scholar’s whole portfolio of activities. Moreover, the determinants considered in this study to explain scholars’ engagement in different activities reconcile two conflicting perspectives: (1) the traditional self-managed approach of academics, and (2) the outcomes-focused approach of university management.

The objective of this paper is to study the impact of political connection and governance on credit rating and whether there is a substitution or complementary relationship between them.

In order to achieve the objective, a succession of eight ordered probit regressions has been carried out. Moderating variables between the political connection and governance characteristics were introduced. The whole population is taken as a sample, i.e., 27 Tunisian companies that are evaluated by FITSH NORTH AFRICA agencies over a period of 10 years (2009–2018).

The outcomes are mixed. They show that the political connection does not always influence credit rating; the size and board independence always improves credit rating; the duality between the functions affects credit rating; whereas the majorities’ proportion does not influence credit rating; and a substitution between the political connection and the governance characteristics is validated.

Like any other research, our results are factors of our measures and variable choice and depends heavily on the how these variables were conceived. Also, although our number of observations responds to the statistical result generalization requirements, our sample remains relatively narrow with 27 companies only.

In practice, the research will allow investors to have a better vision upon the future of their investments based on whether to develop their governance system or promote political networking. It will also prompt lenders to look beyond ratings and consider factors such as political connections to make a rational judgment on their future placements.

This study leads us to find various solutions: the establishment of credit agencies that take into consideration all the data of all the operators taken as a whole (bank, leasing company, and factoring). It encourages the reorganization of the Tunisian banking sector through mergers for example.

This study is a pioneer in the credit rating field in Tunisia, where the source of debt financing is the most used by all enterprises across all sectors. This study extends the literature of political connection effectiveness, independent directors, board size, in improving corporate performance and credit rating.

This study aims to estimate the value of the impact from digital transformation (DX) focusing on its automation effect, looking at the time and cost savings coming from the substitution effect with an adoption of digital technologies. For example, cloud and artificial intelligence technologies such as ChatGPT have the potential to change ways of working, substituting and replacing several of the tasks that are currently carried out by public administration (PA) employees and labor processes underpinning PA services.

The paper outlines a new framework to estimate the potential impact of DX on the public sector. The authors apply this framework to estimate the value of the impact of DX on the Italian PA, defining the latter by the collection of the value of its labor (i.e. PA workforce salaries) and by the collection of the value of its outputs (i.e. public services’ costs).

This study ultimately maps out the magnitude and trends of how likely the PA occupations and services could be substituted in a wider process of DX. To do this, the authors apply their framework to the Italian PA, and they triangulate secondary data collection, from official accounts of the Italian Ministry of Economics and the National Statistical Institute, with methodological antecedents from the UK Office for National Statistics and experts’ insights. Results provide a snapshot on the type and magnitude of PA jobs and services projected to be affected by automation over the next 10 years.

To the best of the authors’ knowledge, this paper provides for the first time an approach to estimate the value of the impact of DX on the public sector in a data-constrained environment – or in the lack of the required primary data. Once applied to the Italian PA, this approach provides a granular map of the automatability of each of the PA occupations and of the PA services. Finally, this paper mentions preliminary insights on potential challenges related to equity in public sector jobs and implications on recruitment processes.

COVID-19 has affected the economies adversely from all sides. The sudden halt in production has impacted both the supply and demand sides. It calls for analysis to quantify the impact of the reduction in economic activity on the economy-wide variables so that appropriate steps can be taken. This study aims to evaluate the sensitivity of various sectors of the Indian economy to this dual shock.

The eight-sector open economy general equilibrium Global Trade Analysis Project (GTAP) model has been simulated to evaluate the sector-specific effects of a fall in economic activity due to COVID-19. This model uses an economy-wide accounting framework to quantify the impact of a shock on the given equilibrium economy and report the post-simulation new equilibrium values.

The empirical results state that welfare for the Indian economy falls to the tune of 7.70% due to output shock. Because of demand–supply linkages, it also impacts the inter- and intra-industry flows, demand for factors of production and imports. There is a momentous fall in the demand for factor endowments from all sectors. Among those, the trade-hotel-transport and manufacturing sectors are in the first two positions from the top. The study recommends an immediate revival of the manufacturing and trade-hotel-transport sectors to get the Indian economy back on track.

The present study has modified the existing GTAP model accounting framework through unemployment and output closures to account for the impact of change in sectoral output due to COVID-19 on the level of employment and other macroeconomic variables.

Develop a comprehensive framework for assessing the knowledge organization systems (KOSs), including the taxonomy of Wikipedia and the ontologies of Wikidata, with a specific focus on enhancing management and retrieval with a gender nonbinary perspective.

This study employs heuristic and inspection methods to assess Wikipedia’s KOS, ensuring compliance with international standards. It evaluates the efficiency of retrieving non-masculine gender-related articles using the Catalan Wikipedian category scheme, identifying limitations. Additionally, a novel assessment of Wikidata ontologies examines their structure and coverage of gender-related properties, comparing them to Wikipedia’s taxonomy for advantages and enhancements.

This study evaluates Wikipedia’s taxonomy and Wikidata’s ontologies, establishing evaluation criteria for gender-based categorization and exploring their structural effectiveness. The evaluation process suggests that Wikidata ontologies may offer a viable solution to address Wikipedia’s categorization challenges.

The assessment of Wikipedia categories (taxonomy) based on KOS standards leads to the conclusion that there is ample room for improvement, not only in matters concerning gender identity but also in the overall KOS to enhance search and retrieval for users. These findings bear relevance for the design of tools to support information retrieval on knowledge-rich websites, as they assist users in exploring topics and concepts.

The aim of this paper is to summarise the state-of-the-art debate on impact of artificial intelligence on unemployment and reporting up-to-date academic findings.

The paper is designed as a review of the labour vs capital conundrum, the differences between industrial automation and artificial intelligence, threat to employment, the difficulty of substituting, role of soft skills and whether technology leads to the deskilling of human workers or favors increasing human capabilities.

Some authors praise the bright future developments of artificial intelligence while others warn about mass unemployment. Therefore, it is paramount to present an up-to-date overview of the problem, compare and contrast its features with what happened in past innovation waves and contribute to academic discussion about the pros/cons of current trends.

The main value of this paper is presenting a balanced view of 100+ different studies, the vast majority from the last five years. Reading this paper will allow to quickly grasp the main issues around the thorny topic of artificial intelligence and unemployment.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-05-2023-0338