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Rather than viewing social network technology (SNT) as a mere tool to access a networked audience, we emphasize its role as both a means and a social actor to help verify people’s self-images in an online social context.

Drawing upon self-verification theory, this study investigates a mechanism of how users are willing to use SNTs continuously through the cognitive and affective reactions on two different SNTs. Structural equation modeling was used via data collected from 320 Facebook and 313 Twitter users.

Our results demonstrated that Facebook users regard it only as a useful tool for presenting self-images, while Twitter users are likely to feel an emotional attachment to technology as a social actor when ideal self-verification is gained, and that different types of SNTs create differential contexts for self-verification.

This study suggests a new lens to understand SNT’s role as a social actor in the self-verification process, further identifying the SNT context in which SNT takes different roles.

In a certain SNT usage context, users are attached to SNTs, suggesting SNT providers consider features that enable SNT users to fulfill their own self-verification motives.

This study explores the roles of SNTs from a self-verification perspective. Our conceptualization of technology as a self-verifying social actor can further extend existing discussions on the role of SNT in response to self-verifying needs, while also promoting the continued use of SNTs in the future.

In this study, we examine the developments of transactive memory systems and collective mind and their influence on performance in virtual teams. Although one of the oft‐cited benefits of the virtual team is the ability of its members to contribute diverse knowledge and expertise, the question of how virtual team members can bring their respective knowledge and expertise to solve the problems they face has been largely ignored in the past research on virtual teams. Building on an emerging body of socio‐cognitive literature, we argue that transactive memory systems and the collective mind are two important variables that explain team performance. We tested our hypotheses with a longitudinal data set that was collected from 38 virtual teams of graduate management students from six universities in four countries over eight weeks. The results suggest that the influence of team members' early communication volume on team performance decreases as teams develop transactive memory systems and a collective mind. The results further suggest that the development of a collective mind represents a high‐order learning in team settings.

During hyper‐competition, disruptive technological innovations germinate causing significant changes in software development organizations' (SDOs) knowledge. The scope and flexibility of the SDO's knowledge base increases; its volatility and the demand for efficiency grows. This creates germane needs to translate abstract knowledge into workable knowledge fast while delivering solutions. The aim of this article is to examine SDOs' responses to such learning challenges through an inductive, theory‐generating study which addresses the question: how did some SDOs successfully learn under these circumstances?

The article takes the form of an exploratory, theory‐building case study investigating seven SDOs' web‐development activities and associated changes in their learning routines during the dot‐com boom.

The SDOs increased their ability to learn broadly, deeply, and quickly – a learning contingency referred as “hyper‐learning” – by inventing, selecting and configuring learning routines. Two sets of learning routines enabled broad and flexible exploratory‐knowledge identification and exploratory‐knowledge assimilation: distributed gate‐keeping; and brokering of external knowledge. Likewise, two sets of learning routines enabled fast and efficient exploitative‐knowledge transformation and exploitation: simple design rules; and peer networks. The authors further observed that SDOs created systemic connections between these routines allowing for fast switching and dynamic interlacing concurrently within the same organizational sub‐units. The authors refer to this previously unidentified form of organizational learning as parallel ambidexterity.

The study contributes to organizational learning theories as applied to SDOs by recognizing a condition where knowledge scope, flexibility, efficiency and volatility increase. It also argues a new form of ambidexterity, parallel ambidexterity, was created and implemented in response to this set of requirements. Parallel ambidexterity differs from traditional exploitative forms where SDOs focus on improving and formalizing their operational knowledge and improving efficiency. It also differs from traditional explorative forms where SDOs focus on identifying and grafting and distributing external abstract knowledge by expanding knowledge scope, flexibility. Most importantly, parallel ambidexterity differs from the widely recognized forms of sequential and structural ambidexterity because exploration and exploitation take place at the same time within the same unit in holographic ways to address volatility. Here learning outcome are applied directly and fast to the tasks for which the learning was initiated.

Ubiquitous digital technologies are driving organisations to embrace non-traditional digitally transformed business models incessantly. Heterogeneous literature contributions have resulted in a spur in the research related to business transformation driven by digital technologies in recent years; consequently, the research under the digital transformation (DT), even though becoming a hotspot, remains very fragmented. The authors endeavour to holistically present the literature's intellectual structure under DT as a concept, its evolving journey and the emerging research streams in the business and management domains using the techniques of bibliometric analysis.

By performing bibliometric analysis on 234 research articles published over the last 20 years in the DT domain, retrieved from Thompson Reuters Web of Science ^TM, this study culls out thorough insights from the citation, co-citation and keyword analysis. Further emerging research streams were evaluated using VOSviewer software.

The study depicts an overall incremental trend of year-on-year publications, authors' performance, publication journals, associated institutions and research driving countries, along with key insights from co-citation network analysis. Furthermore, the study evaluates four research areas – organisational impacts, applied applications and insights, operational processes and social aspects, comprising eighteen research streams that comprehensively cover-up research under the DT domain.

The study contributes to the literature of DT by amalgamating the status of the present research, but more importantly, by deriving the research areas and research streams, which can be further expanded by researchers as future research streams.

For the practitioners, the study aims to act as a ready reckoner repository with practice-oriented literature references to facilitate them building knowledge and taking effective strategic decisions to harness the benefits of DT more proficiently.

This study illustrates the bibliometric structure of the DT literature and presents insights from the growth of the literature year-on-year.

The purpose of this paper is to identify the potential and the challenges for different firms in adopting an AI-first strategy. The study attempts to discern if any company can prioritize AI at the forefront of their strategic plans.

Drawing from illustrative examples from well-known AI-leaders like Netflix and Spotify, as well as from upcoming AI startups and industry incumbents, the paper explores the strategic role of AI in core business processes and customer value creation. It also discusses the advent and implications of generative AI tools since late 2022 to firms’ business strategies.

The authors identify three types of AI-first strategies, depending on firms’ starting points: digital tycoon, niche carver and asset augmenter. The authors discuss how each strategy can aim to achieve data, algorithmic and execution advantages, and what the strategic bottlenecks and risks are within each strategy.

To the best of the authors’ knowledge, this paper is the first to systematically describe how companies can form “AI-first” strategies from different starting points. This study includes actionable examples from known industry players to more emerging startups and industrial incumbents.

The weapon system of The Navy is the small quantity producing system on multiple kinds. It is consisted of various equipment and the subordinate parts of those which can repair the damaged part. The operating procedure concerning warship's repair parts managed under these systems is as follows. Firstly, if demand of repair parts occurs from warship which is the operating unit of weapon, then the Fleet(the repair & supply support battalion) is in charge of dealing with these requests. If certain request from warship is beyond the battalion's capability, it is delivered directly to the Logistic Command. In short, the repair and supply support system of repair parts can be described as the multi-level support system. The various theoretical researches on inventory management of Navy's repair parts and simulation study that reflects reality in detail have been carried out simultaneously. However, the majority of existing research has been conducted on aircraft and tank's repairable items, in that, the studies is woefully deficient in the area concerning Navy's inventory management. For that reason, this paper firstly constructs the model of consumable items that is frequently damaged reflecting characteristics of navy's repair parts inventory management using ARENA simulation. After that, this paper is trying to propose methodology to analyze optimal inventory level of each supply unit through OptQuest, the optimization program of ARENA simulation.

Previous literature has discussed the importance of two types of social media exposures: owned social media (OSM) exposures generated by service providers and earned social media (ESM) exposures initiated by consumers. This study aims to examine the relative effects of owned and ESM exposures on brand purchase, as well as their advertising externality to competing brands. Rooted in theory of planned behavior and advertising externality literature, this study hypothesizes that owned and ESM exposures positively influence brand purchase. Such effects, however, can spill over to competing brands that invest in social media marketing and co-exist in the market.

This study collects brand purchase records and social media messages on the Facebook brand pages of a group of service providers over 12 months. The data are assembled for time series analysis with the unit of analysis being “brand × bi-week”.

Using a blend of fixed-effects models and seemingly unrelated regressions, this study finds that both owned and ESM exposures positively affect brand purchase, the purchase effect of OSM exposures is greater than ESM exposures, OSM exposures generate not only more purchase of the focal brand but also positive advertising externality to competing brands, whereas ESM exposures locks up the advertising effect to the focal brand without spilling over to competing brands.

This study advances the understanding about the externality of social media exposures in an increasingly competitive market where multiple brands invest in social media marketing and co-exist. Important implications on the strategic use of social media exposures to drive brand purchase while competing with similar brands are provided.

When shopping for hotels online, consumers usually follow a sequential process of search, click-through and book. How to maximize consumer conversion on the path to purchase and prevent potential customers from giving up the online search remains an important topic to hotel marketers and online travel agents (OTAs). The purpose of this study is to understand how informational cues displayed in an online hotel search process, including quality indicators, brand affiliation, incentives (discounted price and promotion) and position in the search results, influence consumer conversion from one stage to another.

The authors collected clickstream data of hotel search from Expedia. The data include information on individual consumers’ click-through and booking, as well as events leading up to the conversions (or failure to convert) from search, click-through to book. It contains 940,164 hotels searched and displayed in 39,574 online search queries made by users in a regional US market between November 1, 2012 and June 20, 2013. The modeling strategy comprised the Heckman model and random effects model, which integrated sequential consumer behavior in different problem-solving stages while accounting for heterogeneity across different hotels online.

The authors find that consumers rely on informational cues displayed online to make decisions about hotel booking. Specifically, consumers tend to click through hotels with higher consumer-generated ratings and industry-endorsed ratings. However, they tend to rely on consumer-generated ratings rather than industry-endorsed ratings when committing to a booking. Moreover, consumers are strongly responsive to incentives (discounted price and promotion) when clicking-through and booking a hotel. Finally, the likelihood of consumer conversions from search to click-through and booking is higher for hotels with brand affiliation and higher positions in the search results.

This research provides critical managerial implications of online search for hotel marketers and OTAs. The results inform hotel marketers and OTAs on how consumers respond to informational cues displayed in their search process and how these informational cues influence consumer conversion from one stage to another. The sequential problem-solving process of search, click-through and booking disclosed in this study also helps hotel marketers to identify customer conversion opportunities using effective informational cues.

当在线酒店预定时, 消费者往往遵循一系列流程, 搜索, 点击查询, 到最后预定。对于酒店营销商和线上旅游社（OTAs）来说, 如何最大化提高消费转化, 使得消费者不会半途中断, 最后预定酒店, 是一个重要话题。本论文的研究目的就是理解酒店在线搜索过程中, 信息线索如何影响每个阶段的消费转化, 其中涉及的因素有：信息质量、品牌、激励（折扣和促销）、以及搜索结果排名等。

研究样本数据采集于Expedia酒店搜索点击流。其中包括个人消费者点击和预定信息、以及由搜索、点击查询到预定过程中的消费转化（或者中途转化失败）的各种事件。样本容量包括940,164家酒店, 其涉及到由美国局部市场消费者在2012年11月1日到2013年6月20日之间做出的39,574条搜索结果。 我们采用Heckman模型和随机效应模型来整合不同线性时间上的消费者行为, 同时考虑不同酒店的多样性。

研究发现消费者使用在线信息线索来做酒店预订决策。具体来说, 消费者倾向于对于消费者评价高和行业认证高的酒店进行点击查询。然而, 相比行业认证, 消费者更倾向于借鉴消费者评价, 来做出最后预定决策。此外, 在点击查询和预定时, 消费者对于激励（折扣和促销）反应强烈。最后, 品牌和搜索排名靠前的酒店往往获得从搜索、点击查询到最后预定中更高的消费转化率。

本论文对酒店营销商和OTAs有重要的在线搜索启示。研究结果向酒店营销商和OTAs证明消费者在搜索过程中对信息线索如何反应, 以及这些信息线索如何影响每个阶段之间的消费转化。本论文展示的从搜索、点击查询、到预定的线性决策过程对于酒店营销商们有着重大帮助, 帮助其使用信息线索找出各种消费转化机遇。

Transactive memory systems (TMS) is a theory of group cognition which conceptualizes knowledge sharing and retrieval processes in groups by the use of a shared “directory”. This paper aims to review and analyze the literature and outline a set of requirements for an information system to support and facilitate TMS processes.

The author analyzed all TMS research literature in the SCOPUS and PROQUEST databases, mapping relevant observations about TMS to a TMS process model. These findings were then translated into functional requirements for a TMS information system (TMS‐IS).

A reasonable integration of information management functions into the TMS process model is possible. However, it was also found that social software functions for social networking, self‐disclosure and conversation are a necessary component of such a TMS‐IS.

The specification provides a useful consolidation of the research literature and a reliable point from which to commence design of a TMS‐IS. It is expected that basing these requirements on the research into social cognition will improve the functional fit of a TMS‐IS to group behavior and performance.

Although TMS is a cognitive theory based on knowledge sharing, there has been no analysis until now of the research literature specifically in order to derive specifications for a supporting software system.