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A quick search of the headlines of major newspapers reveals a treasure trove of technology procurement gone wrong. While the private sector seems to adopt and implement new technology seamlessly and quickly to deliver for customers, the government struggles to accomplish technology purchases and integrations with the same ease. As governments in the United States are looking to retain their current workforce and attract the next generation of workers, the technological capabilities and ethos of governments will be paramount. With nearly every industry being transformed by technology and Generation T being the first generation to have an ingrained “technology first” mindset, the ability of governments to attract these workers depends, in large part, on the ability to transform their government technology culture, policies, and practices.

In this chapter, the authors examine the administrative branch and observe two key components at the root of most technology failures: poor organizational structure in the bureaucracy and the lack of an empowered Chief Information/Technology Officer. Building upon case studies from Massachusetts and California, this chapter looks at the factors related to failure or success to understand the technology procurement culture. The chapter concludes by presenting four key “best practice” principles of public policy and administration that can be implemented by almost any governmental entity to improve their acquisition and implementation of technology.

Traditional management models are no longer viable in this complex era, with fast-pacing markets and various emergent properties affecting organizational success. The present study acknowledges the need for developing a coherent framework that leaders may use to evaluate their organizational adaptive ability.

This paper uses theoretical analysis.

This paper proposes the CAL-R framework, which identifies key characteristics of complex adaptive systems (CAS). These actionable elements, manifested in respective behaviors, will increase organizational adaptability. Adaptability is then suggested to be the mediating factor between complex adaptive leadership practice and organizational performance.

The suggested actionable elements are developed based on a theoretical, non-measurable approach; however, the need for statistical rigorousness is also acknowledged. Further research and a quantitative approach are also suggested for the development of a sound measurement scale.

Applying effective complex adaptive leadership will potentially provide organizations with a competitive advantage by making them more innovative, able to transform and adapt to environmental needs and changes and eventually meet their goals and reach their vision. Leaders may use the identified actionable elements as benchmarks against which they can evaluate their organizations' complex adaptive leadership readiness (CAL-R).

The CAL-R framework may be used by leaders in order to evaluate their organization’s current adaptive maturity level (ability) and identify the elements they need to focus on more and develop in order to become more adaptive and competitive.

From the factors that affect shear strength of reinforced concrete (RC) beams, the study examines the effect of controversial parameters, width-to-depth (b/d) and effective length-to-depth (l_eff/d) ratio on shear strength of RC slender beams.

The researchers utilized a database of 676 experimental test results from ACI-DAfStb database, Conducted regression analysis to examine relationship between b/d and l_eff/d ratios and shear strength, compare and analyze sensitivity to changes in b/d and l_eff/d ratios for the selected 12 shear models for RC beams.

Increasing b/d ratio enhanced shear strength until b/d ˜ 3, but further increases had limited impact and increasing l_eff/d ratio resulted in decreased shear strength. From comparative analysis, the models provided by various design standards were found to be safe, with EC-2 and JSCE models being conservative. From considered research models, Campione and Arslan models were conservative, while Kim and White model were observed to be unsafe. Sensitivity analysis indicated ACI318-19, JSCE, CEB-FIP-90 and Arslan models were sensitive to changes in b/d and l_eff/d ratios. National code models generally captured shear strength characteristics well. Certain models suggested a constant/decreasing b/d effect despite observed shear strength enhancement. Most models indicated improved shear strength with an increasing l_eff/d ratio, contrary to experimental findings while TS500 and Hwang models aligned with experimental results.

The study's limitations include the dependence on the available database, which may not encompass all possible experimental scenarios. Further research should aim to expand the database and investigate additional parameters that may influence shear strength in RC beams.

The findings of this study have practical implications for the design and analysis of RC beams by suggesting that the width-to-depth and length-to-depth ratios should be carefully considered to optimize shear strength. The identified models can assist engineers in selecting appropriate shear strength prediction models based on specific design scenarios.

The study contributes to the advancement of knowledge in the field of reinforced concrete beam design, which has implications for the safety and reliability of structural systems. By understanding the factors influencing shear strength, engineers can design more efficient and robust structures, ensuring the safety of buildings and infrastructure.

This study provides valuable insights into the influence of the width-to-depth and effective length-to-depth ratios on shear strength in reinforced concrete beams. It contributes to the understanding of these factors and their impact on shear strength, addressing the lack of consensus among researchers. The comparative analysis of shear models and the sensitivity analyses add value by identifying the models that align better with experimental observations. The study emphasizes the need for accurate models that account for these factors and highlights the importance of further research to refine and develop improved predictive models.

In spite of the rise of social media influencers observed in the 2010s, the direction and future of influencers are ambiguous. This popular yet overused marketing tool has shown certain problems and limitations such as a decrease in perceived authenticity and market saturation. Additionally, the outbreak of COVD-19 has amplified the significance of these factors and made many companies and influencers reconsider their involvement in influencer marketing. Within this context, this paper aims to explore whether influencers were impacted by diminishing perceived authenticity, market saturation or the prolonged pandemic. Also, the authors aim to investigate influencers’ perception of the future of influencer marketing post-pandemic.

To gain insight into trends in influencer marketing from the influencer’s perspective, this paper uses qualitative research in the form of interviews with influencers and industry professionals.

The findings highlight the importance of perceived authenticity for success in influencer marketing. Most interviewees indicated that they had noticed a boom in social media influencer marketing before the pandemic, yet provided mixed views regarding the market during the pandemic. Several believe that influencers will continue to be relevant in the increasingly digital world (e.g. increasing digital marketing spend and e-commerce), whereas an expectation of new digital platforms and innovations was also observed. In the long term, saturation and decreased effectiveness were predicted by several interviewees.

This under-researched topic is of relevance especially to consumer goods companies, as social media marketing and influencer marketing are currently highly effective and popular tools. To refine marketing strategies designed around influencers, understanding the limitations, in the context of COVID-19, is crucial.

The purpose of this study is to examine how consumers perceive nutrition labeling on wine and how this information impacts healthiness perceptions of wine.

A series of four experiments focused on healthiness perceptions and purchase likelihood.

Consumers who read wine labels rate wine as significantly less healthy. Sugar content affects healthiness perceptions of wine more than calories. Changing the serving size on the label moderates these effects. Consumers high in dietary restraint process this nutrition information differently.

Future research could examine actual purchase behavior using retail data.

This study has implications for consumers, manufacturers and public policy. While currently most consumers are not motivated to read a nutrition label on wine, specific nutrition information can impact consumer perceptions of wine. Consumer education is recommended.

Research on nutrition labeling of alcohol specifically has been very limited.

The purpose of this article is to share this study’s efforts to foster belonging in special collections public service spaces, as Black and Latina practitioners of color, while navigating known systemic professional barriers to inclusivity in the library information profession.

In this conceptual essay, frameworks from Black and Chicana feminist theories are applied which resonate deeply with this study’s practices but are not often encountered in library spaces, namely intersectional nepantla, which is used to situate the positionality within special collections.

Fostering belonging in special collections environments is an ongoing effort, but this study offers reflections in solidarity with all who seek to increase inclusivity and equity in their spaces. It is believed that the cumulative impact of many small actions implemented from the ground up can potentially be as significant as top-down, administrative charges.

This article's originality stems from both its authors and the methodology. As BIPOC practitioners, to the authors emphasize the authentic, day-to-day interactions that are essential to developing inclusivity and equity in special collections and archival spaces. Special collections reference workers have limited time off desk to collaborate and conduct research.

The purpose of this paper is to propose a scenario-based grey methodology using clustering and optimizing with imprecise and uncertain body size data in an emergency assembly point area to assign the people on a campus to reach the emergency assembly points under uncertain disaster times.

Grey clustering and a new grey p-median linear programming model are developed to determine which units to assign to the pre-determined assembly points for a main campus in case of a disaster. The models have two scenarios: 70 and 100% occurrence capacities of administrative and academic personnel and students.

In this study, the academic and administrative units have been assigned to determine five different emergency assembly points on the main campus by using the numbers of the academic and administrative personnel and student and distances of the units to the assembly point areas of each other. The alternative solutions are obtained effectively by evaluating capacity utilization rates in the scenarios.

It is often unclear when disasters can occur and therefore, a preliminary preparation time must be required to minimize the risk. In the case of natural, man-made (unnatural) or technological disasters, the people are required to defend themselves and move away from the disaster area as soon as possible in a proper direction. The proposed assignment model yields a final solution that effectively eliminates uncertainty regarding the selection of emergency assembly points for administrative and academic staff as well as students, in the event of disasters.

Grey clustering suggests an assignment plan and concurrently, an investigation is underway utilizing the grey p-median linear programming model. This investigation aims to optimize various scenarios and body sizes concerning emergency assembly areas. All campus users who are present at the disaster in units of the campus are getting uncertainty about which emergency assembly point to use, and with this study, the vital risks aim to be ultimately reduced with reasonable plans.

This study seeks to investigate the interrelationships among sector reputation, purchasing stock intention, sector engagement and attitude toward the sector. Moreover, it aims to explore the moderating role of sector knowledge in the indirect relationship between sector reputation and purchasing stock intention, mediated by sector engagement and/or attitude toward the sector.

Drawing upon the theory of planned behavior, the research framework is empirically tested. A survey involving 300 potential and actual investors was conducted, representing individuals investing in tourism brands within the Egyptian stock market. The validity and reliability of the constructs were assessed using AMOS, while Hayes’s PROCESS macro was employed to examine the mediation and moderated mediation effects.

The findings reveal that sector reputation significantly influences sector engagement, attitude toward the sector and purchasing stock intention. Furthermore, the attitude toward the sector serves as an explanatory factor for investors’ propensity to purchase stocks of tourism brands. The study confirms the serial mediation effect of sector engagement and attitude toward the sector, respectively, in the relationship between sector reputation and purchasing stock intention. Additionally, it confirms the moderated mediation role of sector knowledge in the relationship between sector reputation and purchasing stock intention, mediated by attitude toward the sector.

The research outcomes suggest that executives in the tourism industry should identify key determinants to enhance purchasing stock intention by fostering greater sector engagement and fostering positive attitudes toward the tourism sector.

This study contributes to the existing literature by shedding light on the importance of maintaining a positive brand reputation and emphasizing the influential role of investors’ knowledge, engagement and attitude in shaping investment decisions in the stock market. These insights contribute to the understanding of investor behavior and have practical implications for organizations in managing their brand reputation and fostering positive customer–brand interactions in the stock market context, thus the current study was conducted.

This study provides an up-to-date review of additive manufacturing (AM) technologies and guidance for selecting the most appropriate ones for specific applications, taking into account the main features, strengths, and limitations of the existing options.

A literature review on AM technologies was conducted to assess the current state-of-the-art. This was followed by a closer examination of different AM machines to gain a deeper insight into their main features and operational characteristics. The conclusions and data gathered were used to formulate a classification and decision-support framework.

The findings indicate the building blocks of the selection process for AM technologies. Furthermore, this work shows the suitability of the existing AM technologies for specific cases and points to opportunities for technological and decision-support improvements. Lastly, more standardization in AM would be beneficial for future research.

The proposed framework offers valuable support for decision-makers to select the most suitable AM technologies, as demonstrated through practical examples of its utilization. In addition, it can help researchers identify the limitations of AM by pinpointing applications where existing technologies fail to meet the requirements.

The study offers a novel classification and decision-support framework for selecting AM technologies, incorporating machine characteristics, process features, physical properties of printed parts, and costs as key features to evaluate the potential of AM. Additionally, it provides a deeper understanding of these features as well as the potential opportunities for AM and its impact on various industries.

The main goal of the study is to investigate how same-brand slogans simultaneously in use in two emerging markets, namely Turkey and Russia, differ semantically. The study further examines in what ways the industrial competition structure impacts the semantic slogan design within these two contexts.

The study uses the method of semantic explication that is based on a 19-device taxonomy. This method is applied to 56 slogan pairs in the Turkish and Russian languages launched for the same brands/products across 6 industries.

Results indicate that same-brand slogans differ semantically between Turkey and Russia. Moreover, firms tend to conform to a shared semantic pattern within a given industry, largely depending on the industrial competition structure. While strong local competition (as in the electronics and cleaning products industries in Turkey and in the personal care and beverages industries in Russia) leads firms to use self-reference, international competition (as in the automotive, personal care and beverages industries in Turkey and in the electronics and cleaning products industries in Russia) promotes them to use hyperbole in their slogan design.

Adopting a common semantic pattern within an industry may carry the risk of restricting brand differentiation and consumers' sense of novelty. Furthermore, the inclusion of brand names in slogans may make slogans sound assertive and lead consumers to overreact to the brand.

Unlike many studies exploring different-brand slogans through a syntactic or grammatical lens, this study investigates the semantic features of same-brand slogans launched in two emerging market contexts. It adopts a B2B perspective, unlike many extant studies that often focus on a B2C one.