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The purpose of studying digitization transformation of the supply chain is to understand how digital technologies and processes are changing the way supply chains operate and to identify the opportunities and challenges associated with this transformation. Studying digitization transformation of the supply chain is important because it can help global businesses in identifying the best practices in supply chain management (SCM) systems and enhance supply chain performance. Hence, this research study is contributing in revealing the outcomes of digital inclusiveness in overall SCM for the growth of retail and e-commerce based platforms.

This research is using both descriptive and explanatory research designs to provide a comprehensive understanding of the problems in SCM. Descriptive research provides a detailed description of the characteristics of the population under study, while explanatory research identifies the causal relationships between the variables. Descriptive research has helped us to develop hypotheses about the relationships between variables that can be tested using explanatory research. Explanatory research has been used to validate the findings of descriptive research. By using both descriptive and explanatory research designs, our research design has increased the generalizability of our findings.

According to this study, businesses intend to change their supply chain strategies after the wake of competitive era to make them more robust, sustainable and collaborative with suppliers, customers and stakeholders by investing more in SCM technology like Blockchain, AI, analytics, robotic process automation and data control centers. This study evaluates the impact of digitization on supply chain systems. This includes assessing the benefits of digitization and identifying the factors that contribute to successful implementation. This research is studying the role of data analytics in SCM and how it can be leveraged to improve efficiency, reduce costs and increase transparency.

The study highlights the importance of adopting digitization in supply chain systems to improve supply chain robustness, sustainability and collaboration with stakeholders. This study's emphasis on data analytics in SCM presents an opportunity for businesses to gain insights into their supply chain systems and make data-driven decisions. This can enhance efficiency, reduce costs and improve overall supply chain performance. The study's focus on SCM technology and data analytics may overlook other factors that contribute to successful SCM, such as organizational culture, human resources and supply chain governance.

This study will complement to the existing body of information, management theory and practice and will benefit all. The research work is original and can be implemented worldwide to promote digitization in SCM for smooth transactions in the entire chain of wholesalers, retail distributors and customers.

This conceptual paper focusses on climate change as a social issue and therefore as a social scientific problem. According to young climate activists, Greta Thunberg being the most widely known, climate change is specifically a problem of generations. Typically, the discourse on responsibility focusses on the technical and philosophical questions posed by the study into “intra-” and “inter-generational justice”. It is the purpose of this paper to present sociological conceptual tools with which to both analyze and propose solutions to specific social problems caused by current generations that will affect future generations.

Figurational process sociology develops and tests models of long-term, unplanned developments, which produce the conditions in which short-term practices of informing and planning social interventions are bound up.

The paper reveals the significance of sociological models that can describe and explain social processes and long-term developments in human habitus that have important explanatory value for understanding contemporary social problems such as human-caused climate change.

The concepts and analytical frames of reference provided by figurational process sociology provide crucial insights into the problem of generations and can help reveal how this social dynamic contributes to challenges facing young climate activists calling for rapid “ecologization” processes and increased human restraint with regard to the natural environment.

Despite the undeniable need for innovation in hospital building design, the literature highlights the disconnect between research and practice as the primary knowledge gap hindering such innovation. This study shows this focus to be an oversimplification, for the complex processes that trigger design innovations and impact their ecosystems need to be examined from a systemic perspective. This paper aims to conceptualise the evolution of hospital building design and identify and explain the main factors triggering design and construction innovations over the past 100 years.

A novel hybrid research design to mixed grounded theory (MGT) methodology, with Charmaz constructivist paradigm, is developed as a new systematic way of constructing and interpreting the concepts and interconnections among them that triggered design innovation.

This study represents a taxonomy of concepts and an explanatory innovation framework, containing 617 interconnections between 146 factors classified across 14 categories. The complex innovation ecosystem comprises multi-faceted processes between heterogenous factors with both individual and collective impacts on design innovations.

This research highlights the main components of the innovation ecosystem and its overall behaviour in this field, and the most influential and interrelated contextual factors, as well as representing and mapping generative interactions that support innovation processes. This knowledge can help hospital researchers, designers, policymakers and stakeholders adopt a multidimensional outlook to analyse the strength of all influential factors, introduce potential novel ways of collaborating, conceptualise an organisational approach, re-formulate research questions through transdisciplinary methods and introduce interdisciplinary courses and programs in architecture schools, thereby contributing to timely design innovation.

This study employs Stein Rokkan's methodological approach to analyse state formation in the Greater Middle East. It develops a conceptual framework distinguishing colonial, populist and democratic pacts, suitable for analysis of state formation and nation-building through to the present period. The framework relies on historical institutionalism. The methodology, however, is Rokkan's. The initial conceptual analysis also specifies differences between European and the Middle Eastern state formation processes. It is followed by a brief and selective discussion of historical preconditions. Next, the method of plotting singular cases into conceptual-typological maps is applied to 20 cases in the Greater Middle East (including Afghanistan, Iran and Turkey). For reasons of space, the empirical analysis is limited to the colonial period (1870s to the end of World War 1). Three typologies are combined into one conceptual-typological map of this period. The vertical left-hand axis provides a composite typology that clarifies cultural-territorial preconditions. The horizontal axis specifies transformations of the region's agrarian class structures since the mid-19th century reforms. The right-hand vertical axis provides a four-layered typology of processes of external intervention. A final section presents selected comparative case reconstructions. To the authors' knowledge, this is the first time such a Rokkan-style conceptual-typological map has been constructed for a non-European region.

Organizations have been challenged to identify antecedents to improved employee adjustment to the work environment changes that arose in the wake of the COVID-19 global pandemic. This study aims to explore the effect of multilingualism on employee ability to adjust to workplace changes based on the concept that multilinguals have been found to switch between tasks more efficiently as compared to monolinguals.

Applying a sequential explanatory mixed methods research approach, quantitative performance evaluation data on 207 credit union employees is analyzed using hierarchical linear modeling to predict employee performance, and thematic analysis of qualitative data representing the adjustment narratives of six monolingual and six multilingual employees within the sample is conducted, corresponding to the period during which employees were adjusting to broad workplace changes after the onset of the global pandemic.

The results suggest greater predicted improvement in the performance of multilingual employees. Reliance on the task-switching ability associated with multilingualism is found to be the primary self-evaluative factor for successful change adjustment among multilingual employees.

In light of work performance benefits identified in this study, organizations may consider multilingualism as a characteristic preceding better adjustment to organizational change, and not simply as a skill applicable to tasks requiring language proficiency, suggesting practical implications for human resource and organizational management.

This is the first sequential explanatory study focusing on the task-switching ability of multilinguals as an antecedent to change adjustment evidenced by improved work performance within an organizational context.

This study investigates how and when employees' exposure to organizational leaders who propose major changes might direct those employees toward efforts to mobilize support for innovative ideas. It specifically theorizes a mediating role of performance pressure beliefs and a moderating role of perceived organizational underperformance in this process.

Three-wave, multi-rater survey data were collected among employees and their supervisors across various industries.

A critical explanatory mechanism that links change-oriented leadership with enhanced championing efforts is that employees experience performance-related hardships. The extent to which employees perceive that their organization is unable to meet its own performance targets triggers this process.

For organizational decision makers, the findings identify results-driven pressures as key mechanisms by which employees' exposures to change-oriented leadership can be leveraged to promote novel ideas. This translation is more likely among employees who are convinced that there is significant room for organizational improvement.

This study unravels the previously unexplored link between change-oriented leadership and idea championing, pinpointing the influences of two performance-related aspects: beliefs about strenuous organization-induced performance expectations and perceptions of an underperforming employer.

The purpose of this study is to examine the relationship between explanatory language, audit fees and audit hours to demonstrate that auditors use explanatory language in audit reports to explain perceived audit risk.

The authors construct the sentiment value, a novel audit risk proxy derived from audit reports, using big data analysis. The relationship between sentiment value and explanatory language is then investigated. The authors present the validity of their new metric by examining the relationship between sentiment value and accounting quality, taking audit fees and hours into account.

The authors first find that reporting explanatory language is positively related to audit fees. More importantly, the authors provide an evidence that explanatory language in audit reports is indicative of increased audit risk as it is negatively correlated with sentiment value. As a positive (negative) sentimental value means that the audit risk is low (high), the results indicate that auditors describe explanatory language in a negative manner to convey the inherent audit risk and receive higher audit fees from the risky clients. Furthermore, the relationship is strengthened when the explanatory language is more severe, such as reporting the multiple numbers of explanatory language or going-concern opinion. Finally, the sentiment value is correlated with accounting quality, as measured by the absolute value of discretionary accruals.

Contrary to previous research, the authors’ findings suggest that auditors disclose audit risks of client firms by including explanatory language in audit reports. In addition, the authors demonstrate that their new metric effectively identifies the audit risk outlined qualitatively in audit report. To the best of the authors’ knowledge, this is the first study that establishes a connection between sentiment analysis and audit-related textual data.

Utilizing the social cognitive theory, this study aims to suggest that organizations that promote high-performance work practices (HPWPs) are instrumental in fostering an individual's affective commitment to change through the explanatory process of an individual's readiness to change. In addition, high adaptive leadership would also strengthen HPWPs and readiness to change relationships.

This study tested the hypotheses using a temporally segregated research design across three time waves (n = 337).

This study found support for the direct, mediating, moderating and mod-med hypotheses. The results of this study corroborate that a high adaptive leadership and an organization implementing HPWPs set the stage for creating an individual's affective commitment to change via their readiness to change.

The current study integrates the change management, leadership and HRM literature by suggesting a unique mechanism and boundary conditions that advance research and practice in an individual's willingness and acceptance to change. Based on the study's findings, the authors suggest theoretical and practical implications for research and practice.

The authors compare sentiment level with sentiment shock from different angles to determine which measure better captures the relationship between sentiment and stock returns.

This paper examines the relationship between investor sentiment and contemporaneous stock returns. It also proposes a model of systems science to explain the empirical findings.

The authors find that sentiment shock has a higher explanatory power on stock returns than sentiment itself, and sentiment shock beta exhibits a much higher statistical significance than sentiment beta. Compared with sentiment level, sentiment shock has a more robust linkage to the market factors and the sentiment shock is more responsive to stock returns.

This is the first study to compare sentiment level and sentiment shock. It concludes that sentiment shock is a better indicator of the relationship between investor sentiment and contemporary stock returns.

The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya.

The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs.

The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable.

A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent.

While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.