Customer heterogeneity in operational e-service design attributes: n empirical investigation of service quality

Purpose – This study aims to empirically examine whether heterogeneity in personal customer profiles translates to heterogeneity in the valued operational e-service design attributes. It focuses on a key operational e-service design attribute – service quality – by investigating whether customers with different profiles (demographics, pattern of use of the service, and pattern of channel use) attach different levels of importance to different dimensions of web site quality. Design/methodology/approach – The study is based on path analysis of data collected from multiple sources in a commercial e-service setting (e-banking): data from an online survey of the customers of the e-service; data stored in the transaction and log files generated by the operation of the e-service over time; and data from the e-service provider’s customer database and back office IT systems. Findings – The results suggest that: customer demographics, pattern of service use, and pattern of channel use have no influence on the importance attached by customers to web site quality dimensions; and customer demographics affect the pattern of use of an e-service. Research limitations/implications – Future research should examine this question in other types of e-services and should examine other types of profile variables. Practical implications – Service providers may not need to employ customization at the level of web site quality dimensions. The findings support the existence of the concept of an “optimal” web site design for quality. Originality/value – The paper answers calls for an increased understanding of the design of high quality e-services and for multidisciplinary research in the field of services management, in particular, incorporating operations management perspectives

An empirical study on digitalization's impact on operational efficiency and the moderating role of multiple uncertainties

While many organizations are increasingly willing to invest in adopting digitalization in recent years, they might not be aware that different levels of uncertainty within and outside their organizations may impend digitalization's effectiveness. This study aims to empirically explores the performance impact from digitalization on organizations and the effect from uncertainty on the impact. More specifically, the objectives are pertinent to examining 1) the association between digitalization and operational efficiency and 2) the moderating effect of macro-level uncertainty, industrial-level uncertainty, and firm-level uncertainty on this association. Using a dataset collected from multiple sources employing innovative methodologies including natural language processing (NLP) to analyze digitalization announcements from Factiva and measuring operational efficiency based on the stochastic frontier approach (SFA), this study analyzes the impact from digitalization via 2,520 samples from 496 listed firms in North America during 2015-2021. The results show that digitalization significantly enhances operational efficiency, and this positive impact from digitalization is weakened by macro-level uncertainty and industrial-level uncertainty. Our findings provide researchers and practitioners with useful insights into digitalization's important role in enhancing operational efficiency and guidance indicating the business environments deserve extra attention so as to retain digitalization's positive impact

Computational narrative mapping for the acquisition and representation of lessons learned knowledge

Lessons learned knowledge is traditionally gained from trial and error or narratives describing past experiences. Learning from narratives is the preferred option to transfer lessons learned knowledge. However, learners with insufficient prior knowledge often experience difficulties in grasping the right information from narratives. This paper introduces an approach that uses narrative maps to represent lessons learned knowledge to help learners understand narratives. Since narrative mapping is a time-consuming, labor-intensive and knowledge-intensive process, the proposed approach is supported by a computational narrative mapping (CNM) method to automate the process. CNM incorporates advanced technologies, such as computational linguistics and artificial intelligence (AI), to identify and extract critical narrative elements from an unstructured, text-based narrative and organize them into a structured narrative map representation. This research uses a case study conducted in the construction industry to evaluate CNM performance in comparison with existing paragraph and concept mapping approaches. Among the results, over 90% of respondents asserted that CNM enhanced their understanding of the lessons learned. CNM’s performance in identifying and extracting narrative elements was evaluated through an experiment using real-life narratives from a reminiscence study. The experiment recorded a precision and recall rate of over 75%

The bright side of being uncertain: the impact of economic policy uncertainty on corporate innovation

Purpose: This study aims to theoretically hypothesize and empirically examine the impact of economic policy uncertainty (EPU) on firms' innovation performance as well as the contingency conditions of this relationship. Design/methodology/approach: This study collects and combines secondary longitudinal data from multiple sources to test for a direct impact of EPU on firms' innovation performance. It further examines the moderating effects of firms' operational and marketing capabilities. A series of robustness checks are performed to ensure the consistency of the findings. Findings: In contrast to the common belief that EPU reduces the innovativeness of firms, the authors find an inverted-U relationship between EPU and innovation performance, indicating that a moderate level of EPU actually promotes innovation. Further analysis suggests that firms' operational and marketing capabilities make the inverted-U relationship steeper, further enhancing firms' innovation performance at a moderate level of EPU. Originality/value: This study adds to the emerging literature that investigates the operational implications of EPU, which enhances our understanding of the potential bright side of EPU and broadens the scope of operational risk management

Mayer and virial series at low temperature

We analyze the Mayer pressure-activity and virial pressure-density series for a classical system of particles in continuous configuration space at low temperature. Particles interact via a finite range potential with an attractive tail. We propose physical interpretations of the Mayer and virial series' radius of convergence, valid independently of the question of phase transition: the Mayer radius corresponds to a fast increase from very small to finite density, and the virial radius corresponds to a cross-over from monatomic to polyatomic gas. Our results have consequences for the search of a low density, low temperature solid-gas phase transition, consistent with the Lee-Yang theorem for lattice gases and with the continuum Widom-Rowlinson model.Comment: 36 pages, 1 figur

The impact of firms’ social media initiatives on operational efficiency and innovativeness

Social media have been increasingly adopted for organizational purposes but their operational implications are not well understood. Firms’ social media initiatives might facilitate information flow and knowledge sharing within and across organizations, strengthening firm‐customer interaction, and improving internal and external collaboration. In this research we empirically examine the impact of social media initiatives on firms’ operational efficiency and innovativeness. Taking the resource‐based view of firms’ information capability, we consider firms’ social media initiatives as strategic resources for operational improvement. We posit that firms’ social media initiatives enhance dynamic knowledge‐sharing routines through an information‐rich social network, leading to both operational efficiency and innovativeness. Collecting secondary data in a longitudinal setting from multiple sources, we construct dynamic panel data (DPD) models. Based on system generalized method of moments (GMM) estimation, we show that firms’ social media initiatives improve operational efficiency and innovativeness. We identify the importance of an information‐rich social network to the creation of knowledge‐based advantage through firms’ social media initiatives, and discuss the theoretical and managerial implications from the perspective of operations management

Scholarly publishing depends on peer reviewers

The peer-review crisis is posing a risk to the scholarly peer-reviewed journal system. Journals have to ask many potential peer reviewers to obtain a minimum acceptable number of peers accepting reviewing a manuscript. Several solutions have been suggested to overcome this shortage. From reimbursing for the job, to eliminating pre-publication reviews, one cannot predict which is more dangerous for the future of scholarly publishing. And, why not acknowledging their contribution to the final version of the article published? PubMed created two categories of contributors: authors [AU] and collaborators [IR]. Why not a third category for the peer-reviewer?Scopu

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

The Psychological Science Accelerator’s COVID-19 rapid-response dataset

In response to the COVID-19 pandemic, the Psychological Science Accelerator coordinated three large-scale psychological studies to examine the effects of loss-gain framing, cognitive reappraisals, and autonomy framing manipulations on behavioral intentions and affective measures. The data collected (April to October 2020) included specific measures for each experimental study, a general questionnaire examining health prevention behaviors and COVID-19 experience, geographical and cultural context characterization, and demographic information for each participant. Each participant started the study with the same general questions and then was randomized to complete either one longer experiment or two shorter experiments. Data were provided by 73,223 participants with varying completion rates. Participants completed the survey from 111 geopolitical regions in 44 unique languages/dialects. The anonymized dataset described here is provided in both raw and processed formats to facilitate re-use and further analyses. The dataset offers secondary analytic opportunities to explore coping, framing, and self-determination across a diverse, global sample obtained at the onset of the COVID-19 pandemic, which can be merged with other time-sampled or geographic data

Open data from the third observing run of LIGO, Virgo, KAGRA, and GEO

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages