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AI-based chatbots as enabler for efficient external knowledge management in public administration
(2024)
This study addresses the pressing issue of staff shortages in German public administrations through the lens of digitalization, focusing on the potential of AI-based chatbots to solve this problem by replacing human labour. Employing a Design Science Research Process (DSRP) methodology, the research synthesizes theoretical foundations and regulatory frameworks to develop a robust chatbot concept. The artifact presented is a comprehensive architectural framework integrating user-centric design, linguistic processing, and regulatory compliance. The proposed artifact navigates complex federal structures and diverse IT infrastructures, promoting accessibility and inclusivity. Implications suggest enhanced efficiency and accessibility in public service delivery for potentially increasing citizen satisfaction and decreasing employee workload. The study underscores the importance of legal compliance and the evolving regulatory landscape in AI deployment. Future research will involve prototyping and evaluating the artifact's performance and applicability throughout the course of the DSRP, thus contributing to the advancement of digital transformation in public administrations.
This study investigates the impact of Large Language Model (LLM) parameters, specifically
temperature and top P, on Supply Chain Risk Detection (SCRD). With a heightened focus
on Supply Chain Risk Management (SCRM) using AI, the research employs a Design of
Experiments (DoE) approach. The results reveal optimal temperature values for valid
assessments in SCRD applications. The study emphasizes the importance of tailored LLM
parameter settings, contributing insights for future research and practical applications in
enhancing supply chain resilience. Suggestions for incorporating Response Surface
Methodology (RSM) and refining the process are proposed for further investigation.
Assessing serious games within purchasing and supply management education: an in-class experiment
(2021)
Supply chains often match the supply of labour to uncertain demand by using precarious workprecarious workers. This increases flexibility and lowers costs for the supply chain by shifting risk to the workers and costs to society. Supply chains are maximizing profits, often literally, on the backs of their workers by creating serious negative externalities for society. We address this issue using a powerpower perspective because powerpower is asymmetrically oriented against workers in many supply chain contexts. This allows us to identify examples of how to reverse this trend and shift powerpower back to workers. The goal is to get to where stakeholders understand the costs and limited benefits of precarity, where we can separate the notion of flexibility from low costs, and where through a combination of incentives, policy, social norms of ethical behaviour, and consumer action, we can get to a better place than where we are now.
An important, often overlooked group of workers that HR managers have trouble reaching are those intentionally disconnected from personal digital devices. That is, workers in manufacturing facilities, distribution centers, secure areas, or locations where employers ban workers from bringing their own devices. We explore the engagement problem for these intentionally disconnected workers. We outline a disruptive HR strategy in these work contexts. We then focus on implementation, testing a simple digital platform prototype that can serve as an entry for existing, disruptive HR management engagement tools (e.g. chatbots, HR analytics) in these settings. Our exploratory findings suggest engagement is a problem for these workers and these simple tools can be an effective strategy to help HR managers improve engagement. We conclude that simple digital solutions aimed at engaging this underserved segment of the workforce can have disruptive yet positive effects for workers, HR managers and shareholders.
This study investigates the role of individual differences in channel choice and switching behavior in a multichannel environment using latent class analysis on data from 1512 customers. Psychographic variables from five domains (risk attitudes, cognitive ability, motivation, personality, and decision-making style) serve as covariates for multichannel customer behavior. We identify six segments that differ significantly on six psychographic variables (readiness to take risks, need for cognition, autotelic and instrumental need for touch, and rational and intuitive decision-making styles). The results advance the theory-building of multichannel customer behavior and present insights for proactively managing customer journeys of distinct segments.
Toward a notation for modeling value driver trees: Classification development and research agenda
(2024)
In-depth analysis of customer journeys to broaden the understanding of customer behaviors and expectations in order to improve the customer experience is considered highly relevant in modern business practices. Recent studies predominantly focus on retrospective analysis of customer data, whereas more forward-directed concepts, namely predictions, are rarely addressed. Additionally, the integration of robotic process automation (RPA) to potentially increase the efficiency of customer journey analysis is not discussed in the current field of research. To fill this research gap, this paper introduces “customer journey mining”. Process mining techniques are applied to leverage digital customer data for accurate prediction of customer movements through individual journeys, creating valuable insights for improving the customer experience. Striving for improved efficiency, the potential interplay of RPA and customer journey mining is examined accordingly. The research methodology followed is based on a design science research process. An initially defined customer journey mining artifact is operationalized through an illustrative case study. This operationalization is achieved by analyzing a log file of an online travel agency functioning as an orientation for researchers and practitioners while also evaluating the initially defined framework. The data is used to train seven distinct prediction models to forecast the touchpoint a customer is most likely to visit next. Gradient-boosted trees yield the highest prediction accuracy with 43.1%. The findings further indicate technical suitability for RPA implementation, while financial viability is unlikely.
Against the setting of an increasing need for innovation and low margins, companies in the logistics
sector are facing highly competitive pressure. One field with high potential for optimization lies within
damage quotas. The use of big data analytics or data mining represents a promising approach to face
this challenge. However, within supply chain management, data mining is hardly being researched on
regarding damage quotas and thus not being utilized to its full possible extend. At the current time it
seems to predominantly be used for route and utilization optimization while the analysis of delivery
damages is hardly considered.
The aim of this research is therefore to showcase an initial approach for data mining in logistics to predict
delivery damage probabilities and to validate this by means of a multiple case study research. To create
a sound basis for evaluation, the groundwork is laid out based on CRISP-DM by the analysis of reference
data (German road-cargo market).
As a central result it is noted that data mining can systematically be used to help reducing the damages
by forecasting the probabilities of damages occurring during transport in dependence of different factors.
The approach can be utilized across different markets as long as sufficient data tracking delivery
damages is being collected within a company. Challenges arise in the field of air- and sea-freight.
The urge for personalisation and the rise of technological advancements
in the 21st century is pushing for more innovative marketing strategies. As such, this dissertation examines the impact of personality-tailored
campaigns (PTC) and how it affects purchasing decisions among
Generation Z, focusing on theoretical and practical implications.
A conceptual framework for the process of personality-tailored marketing has been developed to provide tangible value for businesses of
various industries in particular the fragrance, smartphone, and food
industry.
Purpose
The purpose of this paper is to investigate the relationships between technology orientations and export performance of small and medium-sized enterprises (SMEs).
Design/methodology/approach
A quantitative research design was adopted for this study. The paper formulates hypotheses from the literature review. These hypotheses are tested using structural equation modeling with data collected from 231 SMEs in Uganda. Data were analyzed using SPSS version 23 and AMOS.
Findings
The findings of this study showed technology orientation has a positive and significant relationship with the performance of Ugandan SMEs and that supply chain agility moderates technology orientation and export performance.
Research limitations/implications
The study discusses the findings, advances limitations and managerial implications. It also suggests future research avenues. It proposes some recommendations to help Ugandan SMEs to form flexible supply chains, use the latest technology and create strong relationship ties with their partners in the supply chain.
Practical implications
The study suggests that managers of Ugandan SMEs should use the latest technology in production, marketing, logistics and supply chain management which will enable them to respond quickly to customer tastes and preferences leading to higher levels of export performance.
Originality/value
This study contributes to the literature on strategic management showing the reliability of scales used and the confirmatory of the factor structure. This study shows that in strategic management technology, orientation is critical in increasing export performance. This study has extended the resource-based view (RBV) and dynamic capabilities theories.