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In today’s economic conditions, businesses face great challenges related to competitiveness. The effort to respond flexibly to the changing business environment and customers’ demands constantly puts pressure on innovation. For this reason, research purpose to examine the effects of learning capabilities and market orientation in logistics companies during the pandemic.

In this research, a quantitative method was adopted and partial least squares structural equation modeling (PLS-SEM) was applied. The research is based on four variables. A conceptual research model was established through literature review and the model was tested. Within the scope of this research, A sample of 544 participants was taken to construct and test the model, and a 39-statement scale was presented to them. After presenting the reliability and validity values on the model, hypotheses were tested. Analyses were conducted using Smart-PLS 3.3 software.

As a result of the research conducted in logistics companies, the positive effects of learning orientation on performance outputs and the positive effects of market orientation as both a mediator and an independent variable show that these concepts are important for company management.

Considering the limitations of the research, it would be wrong to generalize the results of the analysis for different sectors because the data were collected from experts working in logistics companies. Therefore, it is necessary to make evaluations taking into account the limitations of the research. In future studies, it is recommended to make comparisons with research in different sectors, taking into account the limitations, and to enrich the literature with more research in this area.

Because of the importance of logistics activities seen in all countries with the pandemic period, it can be explained as a result of the analyses that the performance outputs are positively affected by the learning orientation, the companies’ adaptation to continuous change and development and their emphasis on market orientation at the same time.

The research is an original study as it analyzes the effects of learning orientations of logistics companies on the problems and opportunities experienced in the supply and distribution channel along with the pandemic process.

The purpose of the study is to examine the effects of innovative working behavior and leader support of employees in marketing and advertising companies, one of the sectors where creative ideas and innovative thoughts are common.

Within the scope of the research, a questionnaire was applied to 443 authorized employees working in marketing and advertising companies headquartered in Istanbul in order to examine innovative work behavior and leader support. The collected data were analyzed using the SmartPLS 3.3.9 package program.

As a result of the analysis of the collected data, it is supported by hypotheses that innovative work behavior and leadership support have positive effects on employees.

It is not possible to make generalizations because a questionnaire was administered to 443 authorized employees working in marketing and advertising companies whose research centers are located in Istanbul. However, it should be taken into account as a source to provide a basic idea for future research.

As a result of the analysis of the data collected from the marketing and advertising sectors, where creativity and innovation are common, it can be explained that innovative work behavior has positive effects, and that the positive effects continue with the effect of leader support on innovative work behaviors.

The research is original since it is carried out in marketing and advertising companies where traditional activities are not accepted, constantly innovative ideas and thoughts are stated and innovative behavior of employees is important in terms of raising awareness.

The purpose of this study is to understand how the level of readiness for artificial intelligence (AI) affects the overall performance of companies, determine the role of organizational flexibility in adapting to new technologies and business models and assess the importance of lean sustainability and value creation for technology-focused companies.

Technology companies working in technoparks in Istanbul were determined, and a questionnaire was applied to senior employees such as experts, engineers and managers working in these companies. The results were processed with a sample of 456 units. SmartPLS program was used for analysis.

As a result of the analyzes, it is supported by hypotheses that AI readiness and organizational flexibility have positive effects on lean sustainability and value creation.

When evaluated in terms of the limitations of the research, it would not be correct to evaluate the results of the analysis in general, since the data were collected from technology-focused companies in technoparks in Istanbul.

Examining the variables that make up the research model in technology-oriented companies helps to understand the critical factors for the future success of companies. At the same time, this research is important for companies to make more informed decisions in their strategic planning, technological transformation processes and value creation strategies.

This research topic offers a unique approach in terms of bringing together topics such as AI readiness, organizational flexibility, sustainability and value creation. These issues play an important role in the strategic planning of technology-focused companies, and when considered together, they are important in terms of examining the critical factors that affect the future success of companies.

The research paper’s purpose is to contribute to the literature by analysing the essential resources and processes required for successful commercialisation, the contemporary challenges and opportunities of artificial intelligence initiatives in Türkiye, and the diverse models and methods employed by these initiatives.

Within the scope of the research, interviews were conducted with 10 entrepreneurs who established artificial intelligence-oriented enterprises in technoparks in Istanbul and Antalya. All 10 interviews were analysed using the MAXQDA20 software tool. Structured qualitative content analysis was used for the data analysis procedure.

Based on the research, external factors have a significant impact on the future growth opportunities of the market. Expanding the client base, gaining international recognition, and securing financing are crucial for success. However, the findings reveal challenges in the relatively young local ecosystem. One major criticism is the lack of support in marketing and sales activities for refined products. To address this, providing financial incentives and knowledge transfer to those in need is vital.

Since the research was conducted only with entrepreneurs who established and successfully commercialised artificial intelligence-oriented enterprises, it is recommended that future studies be performed with a widespread sample group, considering this limited situation. Furthermore, to overcome survivorship bias, it is recommended that posterior studies include failed commercialisation attempts in AI ventures.

It can be argued that there is no deliberate approach or model for commercialization. Entrepreneurs often draw from their own prior experiences or observe industry trends. Given the limited financial resources available in the domestic market and the challenge of attracting foreign investors to Turkish brands, entrepreneurs tend to rely on internal approaches for commercialisation.

This research delves into the commercialisation prospects and obstacles encountered by AI start-ups in Türkiye. It comprises qualitative insights into business models, commercialisation approaches, opportunities, and challenges. The data were obtained from interviews with entrepreneurs operating in the industry.

The main purpose of examining innovation development (ID), technology management (TM) and big data analytics capability (BDAC) from the perspective of information technology companies is to help these companies optimize their business strategies and increase their competitiveness. When these concepts are considered together, it is aimed to present suggestions that information technology companies can increase their innovation capacities, optimize their technology portfolios and develop their big data analytics capacities.

Data were collected from information technology companies working on big data analytics in technoparks in Istanbul. In the research, the Marmara region of Turkey was preferred because it is the region where the information technology sector is most common. In total, 503 questionnaires were collected. SmartPLS (4.0.8.4) licensed software was used in the research, and the results are presented with tables and figures.

As a result of the analysis of the data, it is supported by hypotheses that ID and TM have positive effects as independent variables and BDAC has positive effects as both independent and mediation variables.

In terms of the limitations of the research, since the data were collected only from the information technology companies in the technoparks in Istanbul, it would not be correct to generalize the analysis results. For this reason, it is recommended to develop a research model and contribute to the literature by considering this limited situation for similar studies to be conducted in the future.

By focusing on ID, it is important for companies to analyze their innovation processes and increase their ID capacity. On the subject of TM, analyses help companies identify their current technological infrastructure and development needs and optimize their technology portfolios. Big data analytics is an important tool that companies can use in their decision-making processes. Therefore, analyses of big data analytics can evaluate companies' current data analytics capacities and offer improvement suggestions.

So why are ID, TM and BDAC important? Why should a research model be developed to examine the effects of these variables? This situation can be understood by looking at the investments made by two world-class companies with headquarters in Istanbul/Turkey. L'Oréal Turkey integrates big data, cloud computing, artificial intelligence and digital platforms into its business processes by investing in new technologies and also makes a difference with innovation in environmental sustainability and social responsibility. PepsiCo, on the other hand, placed a great emphasis on innovation by opening its third Design and Innovation Center in Turkey and Europe in Istanbul. For this reason, examining the effects of ID, TM and big data analytics together in the research is important for the originality of the research. Examining these variables by focusing on their interactions and effects increases the originality of the subject.

This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies.

This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context.

High-tech enterprises seeking to improve performance should enhance employee creativity and innovation. The impact can be greater if intellectual property is protected and additional value provided to customers.

The briefing saves busy executives and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.