A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms

With the discovery of the COVID-19 vaccine, what has always been worrying the decision-makers is related to the distribution management, the vaccination centers' location, and the inventory control of all types of vaccines. As the COVID-19 vaccine is highly demanded, planning for its fair distribution is a must. University is one of the most densely populated areas in a city, so it is critical to vaccinate university students so that the spread of this virus is curbed. As a result, in the present study, a new stochastic multi-objective, multi-period, and multi-commodity simulation-optimization model has been developed for the COVID-19 vaccine's production, distribution, location, allocation, and inventory control decisions. In this study, the proposed supply chain network includes four echelons of manufacturers, hospitals, vaccination centers, and volunteer vaccine students. Vaccine manufacturers send the vaccines to the vaccination centers and hospitals after production. The students with a history of special diseases such as heart disease, corticosteroids, blood clots, etc. are vaccinated in hospitals because of accessing more medical care, and the rest of the students are vaccinated in the vaccination centers. Then, a system dynamic structure of the prevalence of COVID -19 in universities is developed and the vaccine demand is estimated using simulation, in which the demand enters the mathematical model as a given stochastic parameter. Thus, the model pursues some goals, namely, to minimize supply chain costs, maximize student desirability for vaccination, and maximize justice in vaccine distribution. To solve the proposed model, Variable Neighborhood Search (VNS) and Whale Optimization Algorithm (WOA) algorithms are used. In terms of novelties, the most important novelties in the simulation model are considering the virtual education and exerted quarantine effect on estimating the number of the vaccines. In terms of the mathematical model, one of the remarkable contributions is paying attention to social distancing while receiving the injection and the possibility of the injection during working and non-working hours, and regarding the novelties in the solution methodology, a new heuristic method based on a meta-heuristic algorithm called Modified WOA with VNS (MVWOA) is developed. In terms of the performance metrics and the CPU time, the MOWOA is discovered with a superior performance than other given algorithms. Moreover, regarding the data, a case study related to the COVID-19 pandemic period in Tehran/Iran is provided to validate the proposed algorithm. The outcomes indicate that with the demand increase, the costs increase sharply while the vaccination desirability for students decreases with a slight slope.     

工业智能化对中国工业碳排放效率的影响及空间效应研究

“30·60”的双碳目标提出后，工业智能化作为新一轮技术革命和产业变革的关键驱动力，对我国实现“碳达峰、碳中和”目标发挥重要作用。基于2006—2019年中国30个省份（因数据缺失，不包括西藏和港澳台地区）的面板数据，运用固定效应模型、中介效应模型、空间杜宾模型，多维度实证分析了工业智能化对中国工业碳排放效率的影响及空间效应。研究发现：(1)工业智能化发展能够提升工业碳排放效率；(2)工业智能化能够依靠技术进步和抑制能源强度提升工业碳排放效率；(3)空间效应上，工业智能化的发展能够提升本地区工业碳排放效率，但对周边地区工业碳排放效率的溢出、扩散效应还未显现；(4)工业智能化对工业碳排放效率的影响存在空间异质性，其发展能够带动东部地区工业碳排放效率的提升，而对中西部地区工业碳排放影响较小。研究结果有效补充了有关工业碳排放效率的影响因素及有关工业智能化的研究，为工业智能化驱动工业碳排放效率提升提供了可靠的实证依据，同时为工业的绿色发展提供了政策参考。     

人工智能、侵蚀性竞争和行业质量阶梯

本文采用国际机器人联合会提供的工业机器人跨国跨行业安装数据，考察了人工智能对行业质量阶梯的影响与作用机制，并在此基础上探究了侵蚀性竞争的调节作用及其引致的质量集聚效应。研究结果发现，人工智能显著缩短了行业质量阶梯，提升了产品质量，且这一作用在一系列检验后依然稳健；基于内生中介效应模型的结果表明，人工智能对质量阶梯的影响是通过增加中间品使用、提高生产率和改变资本密集度实现的。侵蚀性竞争的存在，不仅负向调节了人工智能对行业质量阶梯的作用，还引发了质量维度上的地理转移现象，导致高质量产品的产销活动转变为以高质量国家为中心进行的区域性活动。探究人工智能对行业质量阶梯的影响，既有助于厘清人工智能的经济效应，也有助于提升产品国际竞争力，助推贸易高质量发展。     

高校财务信息化与智能化应用研究

在信息化背景下，传统财务管理模式已经不能满足高校管理的需求，高校财务信息化的转型势在必行。首先对目前高校财务信息化应用存在的问题进行深入探讨，然后对高校财务智能化应用做进一步思考，构建基于高校特殊性的高校财务智能化应用的基本架构，归纳高校财务智能化应用的特点，展望未来高校财务智能化的4种应用场景。最后提出转变管理者的思想观念、强化顶层设计、加强人才队伍建设、完善数据安全风险防控措施等建议。     

(Im)Balanced customer-oriented behaviors and AI chatbots' Efficiency–Flexibility performance: The moderating role of customers’ rational choices

Artificial intelligence (AI) based chatbots are increasingly deployed in frontline encounters, because they combine frontline service efficiency and flexibility. Using a large-scale data set with more than 130,000 man–machine dialogues from an e-bike sharing platform, Study 1 reveals a complex relationship between chatbots' customer-oriented behaviors and their efficiency–flexibility ambidexterity. Chatbots' level of efficiency–flexibility ambidexterity is higher when their functional and relational customer-oriented behaviors are balanced rather than imbalanced (i.e., a negative imbalance effect) and when they are balanced at a higher rather than a lower level (i.e., a positive balance effect). A follow-up experiment, Study 2, and online survey, Study 3, consistently show that the negative imbalance effect is stronger as customers' perceptions of non-personalization costs decrease and privacy concerns increase, while opportunity cost has no significant influence on the negative imbalance effect. However, consistent with rational choice theory, the positive balance effect is stronger as non-personalization costs increase, privacy concerns decrease, and opportunity cost decreases. In addition, Study 1 and 3 consistently show that in alignment with the stimulus–organism–response framework, efficiency–flexibility ambidexterity partially mediates the relationship between chatbots’ (im)balanced customer-oriented behaviors and customer patronage. This study contributes to the literature on frontline ambidexterity by introducing an AI application context and a more nuanced nonlinear view of the antecedents and consequences of frontline ambidexterity.     

Controllers’ role in managerial sensemaking and information trust building in a business intelligence environment

This study explores how an organization’s controllers (management accountants) give sense to the information provided by its business intelligence (BI) system, and thus shape the construction of information trust. A qualitative case study was conducted within a Finnish food manufacturing company, building on the notion of trust related to management accounting information and sensemaking theory. The study was informed through open-ended interviews and an examination of internal accounting and management reports. The authors found that the company used an integrated BI system that enabled the production of information in a timely and perceivably standardized manner. Controllers managed this accounting information and gave sense to it, helping deliver a shared understanding of the daily business situation. The findings show that controllers play a pivotal role in building information trust by giving sense to the information provided by the BI system.     

Agency and representation in internet governance

This special issue offers a new perspective on decision-making in internet governance, bringing together theoretical contributions and empirical knowledge on authority and influence in underexplored policy settings, from the Congress of the United States to RightsCon, a leading summit on human rights in the digital era. The articles included here shed light on specific groups underrepresented in existing research (such as civil society groups, expert activists, technical community) and on the interactions between these groups and traditional, powerful actors such as governments and industry. These contributions raise new questions, such as: is agency possible without representation in the digital space? Who can represent? Are deficiencies in technology governance affecting the representation model of our political systems more broadly? How do representation and agency manifest themselves across time and space?     

The role of artificial intelligence in the procurement process: State of the art and research agenda

Artificial intelligence (AI) is widely adopted in many areas, but it is still in its infancy in procurement, despite its potential. To map the state of the art of both research and practice and identify future research directions, this paper presents a mixed methodology exploratory study of the role of AI in the procurement process. The paper combines a systematic literature review, a mapping of the offerings of providers of AI-based procurement platforms and a focus group with procurement managers. Results map the functionalities of AI-based solutions throughout the procurement process, describe benefits and challenges to their adoption and identify future research directions.     

A ‘biased’ emerging governance regime for artificial intelligence? How AI ethics get skewed moving from principles to practices

Over the past few years, the awareness that the full potential of artificial intelligence (AI) could be attained only through the establishment of a trustworthy and human-centric framework has expanded, thereby prompting demand for regulatory frameworks as well as engendering a flourish of initiatives that set ethical codes and good governance principles for AI development. This study investigates whether the convergence of many of the proposed ethical frameworks around a narrow set of values and principles may be interpreted as a case of transnational norms emergence, a pre-condition for a more structured global regulatory framework or policy regime. Moreover, it explores how this emerging normative framework is reframed in its concrete implementation. Findings suggest that AI governance poses a complex dilemma: while its hybrid governance ecosystem entrusts developers and deployers, mainly from the private sector and technical communities, with the task of translating principles into workable tools, their institutional logics substantially narrow the scope and purposes of the ethical approach.     

The influence of chatbot humour on consumer evaluations of services

Technological advances have enabled firms to automate customer service by employing artificial intelligence (AI) chatbots. Despite their many potential benefits, interactions with chatbots may still feel machine-like and cold. The current study proposes the use of humour by chatbots as a gateway to humanizing them and thereby enhancing the customer experience. Across three experimental studies, the results reveal that (i) the use of humour enhances service satisfaction when it is used by a chatbot but not when it is used by a human agent, (ii) this chatbot humour effect is serially mediated by enhanced perceptions of anthropomorphism and interestingness of the interactions with the chatbot, and (iii) whilst both positively and negatively valenced chatbot humour may enhance the interestingness of the interactions, socially appropriate (i.e., affiliative) humour as opposed to inappropriate (i.e., aggressive) humour leads to enhanced service satisfaction. This study extends the understanding of the humanization processes of chatbots and provides guidelines for how firms should use chatbot humour to positively influence consumers' service satisfaction.